>>C G technology is focused on data where we have long believed that data is in our DNA. We help maximize humanity's potential by delivering world class, precision engineered data solutions developed through sustainable and profitable partnerships. Included in our offerings are hard disk drives. As I'm sure many of you know, ah, hard drive consists of a slider also known as a drive head or transducer attached to a head gimbal assembly. I had stack assembly made up of multiple head gimbal assemblies and a drive enclosure with one or more platters, or just that the head stacked assembles into. And while the concept hasn't changed, hard drive technology has progressed well beyond the initial five megabytes, 500 quarter inch drives that Seagate first produced. And, I think 1983. We have just announced in 18 terabytes 3.5 inch drive with nine flatters on a single head stack assembly with dual head stack assemblies this calendar year, the complexity of these drives further than need to incorporate Edge analytics at operation sites, so G Edward stemming established the concept of continual improvement and everything that we do, especially in product development and operations and at the end of World War Two, he embarked on a mission with support from the US government to help Japan recover from its four time losses. He established the concept of continual improvement and statistical process control to the leaders of prominent organizations within Japan. And because of this, he was honored by the Japanese emperor with the second order of the sacred treasure for his teachings, the only non Japanese to receive this honor in hundreds of years. Japan's quality control is now world famous, as many of you may know, and based on my own experience and product development, it is clear that they made a major impact on Japan's recovery after the war at Sea Gate. The work that we've been doing and adopting new technologies has been our mantra at continual improvement. As part of this effort, we embarked on the adoption of new technologies in our global operations, which includes establishing machine learning and artificial intelligence at the edge and in doing so, continue to adopt our technical capabilities within data science and data engineering. >>So I'm a principal engineer and member of the Operations and Technology Advanced Analytics Group. We are a service organization for those organizations who need to make sense of the data that they have and in doing so, perhaps introduce a different way to create an analyzed new data. Making sense of the data that organizations have is a key aspect of the work that data scientist and engineers do. So I'm a project manager for an initiative adopting artificial intelligence methodologies for C Gate manufacturing, which is the reason why I'm talking to you today. I thought I'd start by first talking about what we do at Sea Gate and follow that with a brief on artificial intelligence and its role in manufacturing. And I'd like them to discuss how AI and machine Learning is being used at Sea Gate in developing Edge analytics, where Dr Enterprise and Cooper Netease automates deployment, scaling and management of container raised applications. So finally, I like to discuss where we are headed with this initiative and where Mirant is has a major role in case some of you are not conversant in machine learning, artificial intelligence and difference outside some definitions. To cite one source, machine learning is the scientific study of algorithms and statistical bottles without computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference Instead, thus, being seen as a subset of narrow artificial intelligence were analytics and decision making take place. The intent of machine learning is to use basic algorithms to perform different functions, such as classify images to type classified emails into spam and not spam, and predict weather. The idea and this is where the concept of narrow artificial intelligence comes in, is to make decisions of a preset type basically let a machine learn from itself. These types of machine learning includes supervised learning, unsupervised learning and reinforcement learning and in supervised learning. The system learns from previous examples that are provided, such as images of dogs that are labeled by type in unsupervised learning. The algorithms are left to themselves to find answers. For example, a Siris of images of dogs can be used to group them into categories by association that's color, length of coat, length of snout and so on. So in the last slide, I mentioned narrow a I a few times, and to explain it is common to describe in terms of two categories general and narrow or weak. So Many of us were first exposed to General Ai in popular science fiction movies like 2000 and One, A Space Odyssey and Terminator General Ai is a I that can successfully perform any intellectual task that a human can. And if you ask you Lawn Musk or Stephen Hawking, this is how they view the future with General Ai. If we're not careful on how it is implemented, so most of us hope that is more like this is friendly and helpful. Um, like Wally. The reality is that machines today are not only capable of weak or narrow, a I AI that is focused on a narrow, specific task like understanding, speech or finding objects and images. Alexa and Google Home are becoming very popular, and they can be found in many homes. Their narrow task is to recognize human speech and answer limited questions or perform simple tasks like raising the temperature in your home or ordering a pizza as long as you have already defined the order. Narrow. AI is also very useful for recognizing objects in images and even counting people as they go in and out of stores. As you can see in this example, so artificial intelligence supplies, machine learning analytics inference and other techniques which can be used to solve actual problems. The two examples here particle detection, an image anomaly detection have the potential to adopt edge analytics during the manufacturing process. Ah, common problem in clean rooms is spikes in particle count from particle detectors. With this application, we can provide context to particle events by monitoring the area around the machine and detecting when foreign objects like gloves enter areas where they should not. Image Anomaly detection historically has been accomplished at sea gate by operators in clean rooms, viewing each image one at a time for anomalies, creating models of various anomalies through machine learning. Methodologies can be used to run comparative analyses in a production environment where outliers can be detected through influence in an automated real Time analytics scenario. So anomaly detection is also frequently used in machine learning to find patterns or unusual events in our data. How do you know what you don't know? It's really what you ask, and the first step in anomaly detection is to use an algorithm to find patterns or relationships in your data. In this case, we're looking at hundreds of variables and finding relationships between them. We can then look at a subset of variables and determine how they are behaving in relation to each other. We use this baseline to define normal behavior and generate a model of it. In this case, we're building a model with three variables. We can then run this model against new data. Observations that do not fit in the model are defined as anomalies, and anomalies can be good or bad. It takes a subject matter expert to determine how to classify the anomalies on classify classification could be scrapped or okay to use. For example, the subject matter expert is assisting the machine to learn the rules. We then update the model with the classifications anomalies and start running again, and we can see that there are few that generate these models. Now. Secret factories generate hundreds of thousands of images every day. Many of these require human toe, look at them and make a decision. This is dull and steak prone work that is ideal for artificial intelligence. The initiative that I am project managing is intended to offer a solution that matches the continual increased complexity of the products we manufacture and that minimizes the need for manual inspection. The Edge Rx Smart manufacturing reference architecture er, is the initiative both how meat and I are working on and sorry to say that Hamid isn't here today. But as I said, you may have guessed. Our goal is to introduce early defect detection in every stage of our manufacturing process through a machine learning and real time analytics through inference. And in doing so, we will improve overall product quality, enjoy higher yields with lesser defects and produce higher Ma Jin's. Because this was entirely new. We established partnerships with H B within video and with Docker and Amaranthus two years ago to develop the capability that we now have as we deploy edge Rx to our operation sites in four continents from a hardware. Since H P. E. And in video has been an able partner in helping us develop an architecture that we have standardized on and on the software stack side doctor has been instrumental in helping us manage a very complex project with a steep learning curve for all concerned. To further clarify efforts to enable more a i N M l in factories. Theobald active was to determine an economical edge Compute that would access the latest AI NML technology using a standardized platform across all factories. This objective included providing an upgrade path that scales while minimizing disruption to existing factory systems and burden on factory information systems. Resource is the two parts to the compute solution are shown in the diagram, and the gateway device connects to see gates, existing factory information systems, architecture ER and does inference calculations. The second part is a training device for creating and updating models. All factories will need the Gateway device and the Compute Cluster on site, and to this day it remains to be seen if the training devices needed in other locations. But we do know that one devices capable of supporting multiple factories simultaneously there are also options for training on cloud based Resource is the stream storing appliance consists of a kubernetes cluster with GPU and CPU worker notes, as well as master notes and docker trusted registries. The GPU nodes are hardware based using H B E l 4000 edge lines, the balance our virtual machines and for machine learning. We've standardized on both the H B E. Apollo 6500 and the NVIDIA G X one, each with eight in video V 100 GP use. And, incidentally, the same technology enables augmented and virtual reality. Hardware is only one part of the equation. Our software stack consists of Docker Enterprise and Cooper Netease. As I mentioned previously, we've deployed these clusters at all of our operations sites with specific use. Case is planned for each site. Moran Tous has had a major impact on our ability to develop this capability by offering a stable platform in universal control plane that provides us, with the necessary metrics to determine the health of the Kubernetes cluster and the use of Dr Trusted Registry to maintain a secure repository for containers. And they have been an exceptional partner in our efforts to deploy clusters at multiple sites. At this point in our deployment efforts, we are on prem, but we are exploring cloud service options that include Miranda's next generation Docker enterprise offering that includes stack light in conjunction with multi cluster management. And to me, the concept of federation of multi cluster management is a requirement in our case because of the global nature of our business where our operation sites are on four continents. So Stack Light provides the hook of each cluster that banks multi cluster management and effective solution. Open source has been a major part of Project Athena, and there has been a debate about using Dr CE versus Dr Enterprise. And that decision was actually easy, given the advantages that Dr Enterprise would offer, especially during a nearly phase of development. Cooper Netease was a natural addition to the software stack and has been widely accepted. But we have also been a work to adopt such open source as rabbit and to messaging tensorflow and tensor rt, to name three good lab for developments and a number of others. As you see here, is well, and most of our programming programming has been in python. The results of our efforts so far have been excellent. We are seeing a six month return on investment from just one of seven clusters where the hardware and software cost approached close to $1 million. The performance on this cluster is now over three million images processed per day for their adoption has been growing, but the biggest challenge we've seen has been handling a steep learning curve. Installing and maintaining complex Cooper needs clusters in data centers that are not used to managing the unique aspect of clusters like this. And because of this, we have been considering adopting a control plane in the cloud with Kubernetes as the service supported by Miranda's. Even without considering, Kubernetes is a service. The concept of federation or multi cluster management has to be on her road map, especially considering the global nature of our company. Thank you.

>> From the theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Welcome to this week's Wikibon, theCUBE's insights powered by ETR. As we exited the isolation economy last year, supercloud is a term that we introduced to describe something new that was happening in the world of cloud. In this Breaking Analysis, we address the 10 most frequently asked questions we get around supercloud. Okay, let's review these frequently asked questions on supercloud that we're going to try to answer today. Look at an industry that's full of hype and buzzwords. Why the hell does anyone need a new term? Aren't hyperscalers building out superclouds? We'll try to answer why the term supercloud connotes something different from hyperscale clouds. And we'll talk about the problems that superclouds solve specifically. And we'll further define the critical aspects of a supercloud architecture. We often get asked, isn't this just multi-cloud? Well, we don't think so, and we'll explain why in this Breaking Analysis. Now in an earlier episode, we introduced the notion of super PaaS. Well, isn't a plain vanilla PaaS already a super PaaS? Again, we don't think so, and we'll explain why. Who will actually build and who are the players currently building superclouds? What workloads and services will run on superclouds? And 8-A or number nine, what are some examples that we can share of supercloud? And finally, we'll answer what you can expect next from us on supercloud? Okay, let's get started. Why do we need another buzzword? Well, late last year, ahead of re:Invent, we were inspired by a post from Jerry Chen called "Castles in the Cloud." Now in that blog post, he introduced the idea that there were sub-markets emerging in cloud that presented opportunities for investors and entrepreneurs that the cloud wasn't going to suck the hyperscalers. Weren't going to suck all the value out of the industry. And so we introduced this notion of supercloud to describe what we saw as a value layer emerging above the hyperscalers CAPEX gift, we sometimes call it. Now it turns out, that we weren't the only ones using the term as both Cornell and MIT have used the phrase in somewhat similar, but different contexts. The point is something new was happening in the AWS and other ecosystems. It was more than IaaS and PaaS, and wasn't just SaaS running in the cloud. It was a new architecture that integrates infrastructure, platform and software as services to solve new problems that the cloud vendors in our view, weren't addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud. And we felt there was a structural change going on at the industry level, the supercloud, metaphorically was highlighting. So that's the background on why we felt a new catch phrase was warranted, love it or hate it. It's memorable and it's what we chose. Now to that last point about structural industry transformation. Andy Rappaport is sometimes and often credited with identifying the shift from the vertically integrated IBM mainframe era to the fragmented PC microprocesor-based era in his HBR article in 1991. In fact, it was David Moschella, who at the time was an IDC Analyst who first introduced the concept in 1987, four years before Rappaport's article was published. Moschella saw that it was clear that Intel, Microsoft, Seagate and others would replace the system vendors, and put that forth in a graphic that looked similar to the first two on this chart. We don't have to review the shift from IBM as the center of the industry to Wintel, that's well understood. What isn't as well known or accepted is what Moschella put out in his 2018 book called "Seeing Digital" which introduced the idea of "The Matrix" that's shown on the right hand side of this chart. Moschella posited that new services were emerging built on top of the internet and hyperscale clouds that would integrate other innovations and would define the next era of computing. He used the term Matrix because the conceptual depiction included not only horizontal technology rose like the cloud and the internet, but for the first time included connected industry verticals, the columns in this chart. Moschella pointed out that whereas historically, industry verticals had a closed value chain or stack and ecosystem of R&D, and production, and manufacturing, and distribution. And if you were in that industry, the expertise within that vertical generally stayed within that vertical and was critical to success. But because of digital and data, for the first time, companies were able to traverse industries, jump across industries and compete because data enabled them to do that. Examples, Amazon and content, payments, groceries, Apple, and payments, and content, and so forth. There are many examples. Data was now this unifying enabler and this marked a change in the structure of the technology landscape. And supercloud is meant to imply more than running in hyperscale clouds, rather it's the combination of multiple technologies enabled by CloudScale with new industry participants from those verticals, financial services and healthcare, manufacturing, energy, media, and virtually all in any industry. Kind of an extension of every company is a software company. Basically, every company now has the opportunity to build their own cloud or supercloud. And we'll come back to that. Let's first address what's different about superclouds relative to hyperscale clouds? You know, this one's pretty straightforward and obvious, I think. Hyperscale clouds, they're walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their particular cloud so they're meeting customers where their data lives with initiatives like Amazon Outposts and Azure Arc, and Google Anthos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, cost, and performance they can deliver. The more complex the environment, the more difficult it is to deliver on their brand promises. And of course, the lesser margin that's left for them to capture. Will the hyperscalers get more serious about cross-cloud services? Maybe, but they have plenty of work to do within their own clouds and within enabling their own ecosystems. They had a long way to go a lot of runway. So let's talk about specifically, what problems superclouds solve? We've all seen the stats from IDC or Gartner, or whomever the customers on average use more than one cloud. You know, two clouds, three clouds, five clouds, 20 clouds. And we know these clouds operate in disconnected silos for the most part. And that's a problem because each cloud requires different skills because the development environment is different as is the operating environment. They have different APIs, different primitives, and different management tools that are optimized for each respective hyperscale cloud. Their functions and value props don't extend to their competitors' clouds for the most part. Why would they? As a result, there's friction when moving between different clouds. It's hard to share data, it's hard to move work. It's hard to secure and govern data. It's hard to enforce organizational edicts and policies across these clouds, and on-prem. Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations and share data safely, irrespective of location. It's pretty straightforward, but non-trivial, which is why I always ask a company's CEO and executives if stock buybacks and dividends will yield as much return as building out superclouds that solve really specific and hard problems, and create differential value. Okay, let's dig a bit more into the architectural aspects of supercloud. In other words, what are the salient attributes of supercloud? So first and foremost, a supercloud runs a set of specific services designed to solve a unique problem and it can do so in more than one cloud. Superclouds leverage the underlying cloud native tooling of a hyperscale cloud, but they're optimized for a specific objective that aligns with the problem that they're trying to solve. For example, supercloud might be optimized for lowest cost or lowest latency, or sharing data, or governing, or securing that data, or higher performance for networking, for example. But the point is, the collection of services that is being delivered is focused on a unique value proposition that is not being delivered by the hyperscalers across clouds. A supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud and then using its own specific platform as a service tooling, creates a common experience across clouds for developers and users. And it does so in a most efficient manner, meaning it has the metadata knowledge and management capabilities that can optimize for latency, bandwidth, or recovery, or data sovereignty, or whatever unique value that supercloud is delivering for the specific use case in their domain. And a supercloud comprises a super PaaS capability that allows ecosystem partners through APIs to add incremental value on top of the supercloud platform to fill gaps, accelerate features, and of course innovate. The services can be infrastructure-related, they could be application services, they could be data services, security services, user services, et cetera, designed and packaged to bring unique value to customers. Again, that hyperscalers are not delivering across clouds or on-premises. Okay, so another common question we get is, isn't that just multi-cloud? And what we'd say to that is yes, but no. You can call it multi-cloud 2.0, if you want, if you want to use it, it's kind of a commonly used rubric. But as Dell's Chuck Whitten proclaimed at Dell Technologies World this year, multi-cloud by design, is different than multi-cloud by default. Meaning to date, multi-cloud has largely been a symptom of what we've called multi-vendor or of M&A, you buy a company and they happen to use Google Cloud, and so you bring it in. And when you look at most so-called, multi-cloud implementations, you see things like an on-prem stack, which is wrapped in a container and hosted on a specific cloud or increasingly a technology vendor has done the work of building a cloud native version of their stack and running it on a specific cloud. But historically, it's been a unique experience within each cloud with virtually no connection between the cloud silos. Supercloud sets out to build incremental value across clouds and above hyperscale CAPEX that goes beyond cloud compatibility within each cloud. So if you want to call it multi-cloud 2.0, that's fine, but we chose to call it supercloud. Okay, so at this point you may be asking, well isn't PaaS already a version of supercloud? And again, we would say no, that supercloud and its corresponding superPaaS layer which is a prerequisite, gives the freedom to store, process and manage, and secure, and connect islands of data across a continuum with a common experience across clouds. And the services offered are specific to that supercloud and will vary by each offering. Your OpenShift, for example, can be used to construct a superPaaS, but in and of itself, isn't a superPaaS, it's generic. A superPaaS might be developed to support, for instance, ultra low latency database work. It would unlikely again, taking the OpenShift example, it's unlikely that off-the-shelf OpenShift would be used to develop such a low latency superPaaS layer for ultra low latency database work. The point is supercloud and its inherent superPaaS will be optimized to solve specific problems like that low latency example for distributed databases or fast backup and recovery for data protection, and ransomware, or data sharing, or data governance. Highly specific use cases that the supercloud is designed to solve for. Okay, another question we often get is who has a supercloud today and who's building a supercloud, and who are the contenders? Well, most companies that consider themselves cloud players will, we believe, be building or are building superclouds. Here's a common ETR graphic that we like to show with Net Score or spending momentum on the Y axis and overlap or pervasiveness in the ETR surveys on the X axis. And we've randomly chosen a number of players that we think are in the supercloud mix, and we've included the hyperscalers because they are enablers. Now remember, this is a spectrum of maturity it's a maturity model and we've added some of those industry players that we see building superclouds like CapitalOne, Goldman Sachs, Walmart. This is in deference to Moschella's observation around The Matrix and the industry structural changes that are going on. This goes back to every company, being a software company and rather than pattern match an outdated SaaS model, we see new industry structures emerging where software and data, and tools, specific to an industry will lead the next wave of innovation and bring in new value that traditional technology companies aren't going to solve, and the hyperscalers aren't going to solve. You know, we've talked a lot about Snowflake's data cloud as an example of supercloud. After being at Snowflake Summit, we're more convinced than ever that they're headed in this direction. VMware is clearly going after cross-cloud services you know, perhaps creating a new category. Basically, every large company we see either pursuing supercloud initiatives or thinking about it. Dell showed project Alpine at Dell Tech World, that's a supercloud. Snowflake introducing a new application development capability based on their superPaaS, our term of course, they don't use the phrase. Mongo, Couchbase, Nutanix, Pure Storage, Veeam, CrowdStrike, Okta, Zscaler. Yeah, all of those guys. Yes, Cisco and HPE. Even though on theCUBE at HPE Discover, Fidelma Russo said on theCUBE, she wasn't a fan of cloaking mechanisms, but then we talked to HPE's Head of Storage Services, Omer Asad is clearly headed in the direction that we would consider supercloud. Again, those cross-cloud services, of course, their emphasis is connecting as well on-prem. That single experience, which traditionally has not existed with multi-cloud or hybrid. And we're seeing the emergence of companies, smaller companies like Aviatrix and Starburst, and Clumio and others that are building versions of superclouds that solve for a specific problem for their customers. Even ISVs like Adobe, ADP, we've talked to UiPath. They seem to be looking at new ways to go beyond the SaaS model and add value within their cloud ecosystem specifically, around data as part of their and their customers digital transformations. So yeah, pretty much every tech vendor with any size or momentum and new industry players are coming out of hiding, and competing. Building superclouds that look a lot like Moschella's Matrix, with machine intelligence and blockchains, and virtual realities, and gaming, all enabled by the internet and hyperscale cloud CAPEX. So it's moving fast and it's the future in our opinion. So don't get too caught up in the past or you'll be left behind. Okay, what about examples? We've given a number in the past, but let's try to be a little bit more specific. Here are a few we've selected and we're going to answer the two questions in one section here. What workloads and services will run in superclouds and what are some examples? Let's start with analytics. Our favorite example is Snowflake, it's one of the furthest along with its data cloud, in our view. It's a supercloud optimized for data sharing and governance, query performance, and security, and ecosystem enablement. When you do things inside of that data cloud, what we call a super data cloud. Again, our term, not theirs. You can do things that you could not do in a single cloud. You can't do this with Redshift, You can't do this with SQL server and they're bringing new data types now with merging analytics or at least accommodate analytics and transaction type data, and bringing open source tooling with things like Apache Iceberg. And so it ticks the boxes we laid out earlier. I would say that a company like Databricks is also in that mix doing it, coming at it from a data science perspective, trying to create that consistent experience for data scientists and data engineering across clouds. Converge databases, running transaction and analytic workloads is another example. Take a look at what Couchbase is doing with Capella and how it's enabling stretching the cloud to the edge with ARM-based platforms and optimizing for low latency across clouds, and even out to the edge. Document database workloads, look at MongoDB, a very developer-friendly platform that with the Atlas is moving toward a supercloud model running document databases very, very efficiently. How about general purpose workloads? This is where VMware comes into to play. Very clearly, there's a need to create a common operating environment across clouds and on-prem, and out to the edge. And I say VMware is hard at work on that. Managing and moving workloads, and balancing workloads, and being able to recover very quickly across clouds for everyday applications. Network routing, take a look at what Aviatrix is doing across clouds, industry workloads. We see CapitalOne, it announced its cost optimization platform for Snowflake, piggybacking on Snowflake supercloud or super data cloud. And in our view, it's very clearly going to go after other markets is going to test it out with Snowflake, running, optimizing on AWS and it's going to expand to other clouds as Snowflake's business and those other clouds grows. Walmart working with Microsoft to create an on-premed Azure experience that's seamless. Yes, that counts, on-prem counts. If you can create that seamless and continuous experience, identical experience from on-prem to a hyperscale cloud, we would include that as a supercloud. You know, we've written about what Goldman is doing. Again, connecting its on-prem data and software tooling, and other capabilities to AWS for scale. And we can bet dollars to donuts that Oracle will be building a supercloud in healthcare with its Cerner acquisition. Supercloud is everywhere you look. So I'm sorry, naysayers it's happening all around us. So what's next? Well, with all the industry buzz and debate about the future, John Furrier and I, have decided to host an event in Palo Alto, we're motivated and inspired to further this conversation. And we welcome all points of view, positive, negative, multi-cloud, supercloud, hypercloud, all welcome. So theCUBE on Supercloud is coming on August 9th, out of our Palo Alto studios, we'll be running a live program on the topic. We've reached out to a number of industry participants, VMware, Snowflake, Confluent, Sky High Security, Gee Rittenhouse's new company, HashiCorp, CloudFlare. We've hit up Red Hat and we expect many of these folks will be in our studios on August 9th. And we've invited a number of industry participants as well that we're excited to have on. From industry, from financial services, from healthcare, from retail, we're inviting analysts, thought leaders, investors. We're going to have more detail in the coming weeks, but for now, if you're interested, please reach out to me or John with how you think you can advance the discussion and we'll see if we can fit you in. So mark your calendars, stay tuned for more information. Okay, that's it for today. Thanks to Alex Myerson who handles production and manages the podcast for Breaking Analysis. And I want to thank Kristen Martin and Cheryl Knight, they help get the word out on social and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does a lot of editing and appreciate you posting on SiliconANGLE, Rob. Thanks to all of you. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcast. It publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me @DVellante, or comment on my LinkedIn post. And please do check out ETR.ai for the best survey data. And the enterprise tech business will be at AWS NYC Summit next Tuesday, July 12th. So if you're there, please do stop by and say hello to theCUBE, it's at the Javits Center. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching. And we'll see you next time on "Breaking Analysis." (bright music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vallante. >> Welcome to this week's Wikibon CUBE Insights powered by ETR. As we exited the isolation economy last year, Supercloud is a term that we introduced to describe something new that was happening in the world of cloud. In this "Breaking Analysis," we address the 10 most frequently asked questions we get around Supercloud. Okay, let's review these frequently asked questions on Supercloud that we're going to try to answer today. Look at an industry that's full of hype and buzzwords. Why the hell does anyone need a new term? Aren't hyperscalers building out Superclouds? We'll try to answer why the term Supercloud connotes something different from hyperscale clouds. And we'll talk about the problems that Superclouds solve specifically, and we'll further define the critical aspects of a Supercloud architecture. We often get asked, "Isn't this just multi-cloud?" Well, we don't think so, and we'll explain why in this "Breaking Analysis." Now, in an earlier episode, we introduced the notion of super PaaS. Well, isn't a plain vanilla PaaS already a super PaaS? Again, we don't think so, and we'll explain why. Who will actually build and who are the players currently building Superclouds? What workloads and services will run on Superclouds? And eight A or number nine, what are some examples that we can share of Supercloud? And finally, we'll answer what you can expect next from us on Supercloud. Okay, let's get started. Why do we need another buzzword? Well, late last year ahead of re:Invent, we were inspired by a post from Jerry Chen called castles in the cloud. Now, in that blog post, he introduced the idea that there were submarkets emerging in cloud that presented opportunities for investors and entrepreneurs. That the cloud wasn't going to suck the hyperscalers, weren't going to suck all the value out of the industry. And so we introduced this notion of Supercloud to describe what we saw as a value layer emerging above the hyperscalers CAPEX gift, we sometimes call it. Now, it turns out that we weren't the only ones using the term, as both Cornell and MIT, have used the phrase in somewhat similar, but different contexts. The point is, something new was happening in the AWS and other ecosystems. It was more than IS and PaaS, and wasn't just SaaS running in the cloud. It was a new architecture that integrates infrastructure, platform and software as services, to solve new problems that the cloud vendors, in our view, weren't addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud. And we felt there was a structural change going on at the industry level. The Supercloud metaphorically was highlighting. So that's the background on why we felt a new catch phrase was warranted. Love it or hate it, it's memorable and it's what we chose. Now, to that last point about structural industry transformation. Andy Rapaport is sometimes and often credited with identifying the shift from the vertically integrated IBM mainframe era to the fragmented PC microprocesor based era in his HBR article in 1991. In fact, it was David Moschella, who at the time was an IDC analyst who first introduced the concept in 1987, four years before Rapaport's article was published. Moschella saw that it was clear that Intel, Microsoft, Seagate and others would replace the system vendors and put that forth in a graphic that looked similar to the first two on this chart. We don't have to review the shift from IBM as the center of the industry to Wintel. That's well understood. What isn't as well known or accepted is what Moschella put out in his 2018 book called "Seeing Digital" which introduced the idea of the matrix that's shown on the right hand side of this chart. Moschella posited that new services were emerging, built on top of the internet and hyperscale clouds that would integrate other innovations and would define the next era of computing. He used the term matrix, because the conceptual depiction included, not only horizontal technology rows, like the cloud and the internet, but for the first time included connected industry verticals, the columns in this chart. Moschella pointed out that, whereas historically, industry verticals had a closed value chain or stack and ecosystem of R&D and production and manufacturing and distribution. And if you were in that industry, the expertise within that vertical generally stayed within that vertical and was critical to success. But because of digital and data, for the first time, companies were able to traverse industries jump across industries and compete because data enabled them to do that. Examples, Amazon and content, payments, groceries, Apple and payments, and content and so forth. There are many examples. Data was now this unifying enabler and this marked a change in the structure of the technology landscape. And Supercloud is meant to imply more than running in hyperscale clouds. Rather, it's the combination of multiple technologies, enabled by cloud scale with new industry participants from those verticals; financial services, and healthcare, and manufacturing, energy, media, and virtually all and any industry. Kind of an extension of every company is a software company. Basically, every company now has the opportunity to build their own cloud or Supercloud. And we'll come back to that. Let's first address what's different about Superclouds relative to hyperscale clouds. Now, this one's pretty straightforward and obvious, I think. Hyperscale clouds, they're walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their particular cloud. So they're meeting customers where their data lives with initiatives like Amazon Outposts and Azure Arc and Google Antos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, costs, and performance they can deliver. The more complex the environment, the more difficult it is to deliver on their brand promises. And, of course, the less margin that's left for them to capture. Will the hyperscalers get more serious about cross cloud services? Maybe, but they have plenty of work to do within their own clouds and within enabling their own ecosystems. They have a long way to go, a lot of runway. So let's talk about specifically, what problems Superclouds solve. We've all seen the stats from IDC or Gartner or whomever, that customers on average use more than one cloud, two clouds, three clouds, five clouds, 20 clouds. And we know these clouds operate in disconnected silos for the most part. And that's a problem, because each cloud requires different skills, because the development environment is different as is the operating environment. They have different APIs, different primitives, and different management tools that are optimized for each respective hyperscale cloud. Their functions and value props don't extend to their competitors' clouds for the most part. Why would they? As a result, there's friction when moving between different clouds. It's hard to share data. It's hard to move work. It's hard to secure and govern data. It's hard to enforce organizational edicts and policies across these clouds and on-prem. Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations, and share data safely, irrespective of location. It's pretty straightforward, but non-trivial, which is why I always ask a company's CEO and executives if stock buybacks and dividends will yield as much return as building out Superclouds that solve really specific and hard problems and create differential value. Okay, let's dig a bit more into the architectural aspects of Supercloud. In other words, what are the salient attributes of Supercloud? So, first and foremost, a Supercloud runs a set of specific services designed to solve a unique problem, and it can do so in more than one cloud. Superclouds leverage the underlying cloud native tooling of a hyperscale cloud, but they're optimized for a specific objective that aligns with the problem that they're trying to solve. For example, Supercloud might be optimized for lowest cost or lowest latency or sharing data or governing or securing that data or higher performance for networking, for example. But the point is, the collection of services that is being delivered is focused on a unique value proposition that is not being delivered by the hyperscalers across clouds. A Supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud, and then using its own specific platform as a service tooling, creates a common experience across clouds for developers and users. And it does so in the most efficient manner, meaning it has the metadata knowledge and management capabilities that can optimize for latency, bandwidth, or recovery or data sovereignty, or whatever unique value that Supercloud is delivering for the specific use case in their domain. And a Supercloud comprises a super PaaS capability that allows ecosystem partners through APIs to add incremental value on top of the Supercloud platform to fill gaps, accelerate features, and of course, innovate. The services can be infrastructure related, they could be application services, they could be data services, security services, user services, et cetera, designed and packaged to bring unique value to customers. Again, that hyperscalers are not delivering across clouds or on premises. Okay, so another common question we get is, "Isn't that just multi-cloud?" And what we'd say to that is yeah, "Yes, but no." You can call it multi-cloud 2.0, if you want. If you want to use, it's kind of a commonly used rubric. But as Dell's Chuck Whitten proclaimed at Dell Technologies World this year, multi-cloud, by design, is different than multi-cloud by default. Meaning, to date, multi-cloud has largely been a symptom of what we've called multi-vendor or of M&A. You buy a company and they happen to use Google cloud. And so you bring it in. And when you look at most so-called multi-cloud implementations, you see things like an on-prem stack, which is wrapped in a container and hosted on a specific cloud. Or increasingly, a technology vendor has done the work of building a cloud native version of their stack and running it on a specific cloud. But historically, it's been a unique experience within each cloud, with virtually no connection between the cloud silos. Supercloud sets out to build incremental value across clouds and above hyperscale CAPEX that goes beyond cloud compatibility within each cloud. So, if you want to call it multi-cloud 2.0, that's fine, but we chose to call it Supercloud. Okay, so at this point you may be asking, "Well isn't PaaS already a version of Supercloud?" And again, we would say, "No." That Supercloud and its corresponding super PaaS layer, which is a prerequisite, gives the freedom to store, process, and manage and secure and connect islands of data across a continuum with a common experience across clouds. And the services offered are specific to that Supercloud and will vary by each offering. OpenShift, for example, can be used to construct a super PaaS, but in and of itself, isn't a super PaaS, it's generic. A super PaaS might be developed to support, for instance, ultra low latency database work. It would unlikely, again, taking the OpenShift example, it's unlikely that off the shelf OpenShift would be used to develop such a low latency, super PaaS layer for ultra low latency database work. The point is, Supercloud and its inherent super PaaS will be optimized to solve specific problems like that low latency example for distributed databases or fast backup in recovery for data protection and ransomware, or data sharing or data governance. Highly specific use cases that the Supercloud is designed to solve for. Okay, another question we often get is, "Who has a Supercloud today and who's building a Supercloud and who are the contenders?" Well, most companies that consider themselves cloud players will, we believe, be building or are building Superclouds. Here's a common ETR graphic that we like to show with net score or spending momentum on the Y axis, and overlap or pervasiveness in the ETR surveys on the X axis. And we've randomly chosen a number of players that we think are in the Supercloud mix. And we've included the hyperscalers because they are enablers. Now, remember, this is a spectrum of maturity. It's a maturity model. And we've added some of those industry players that we see building Superclouds like Capital One, Goldman Sachs, Walmart. This is in deference to Moschella's observation around the matrix and the industry structural changes that are going on. This goes back to every company being a software company. And rather than pattern match and outdated SaaS model, we see new industry structures emerging where software and data and tools specific to an industry will lead the next wave of innovation and bring in new value that traditional technology companies aren't going to solve. And the hyperscalers aren't going to solve. We've talked a lot about Snowflake's data cloud as an example of Supercloud. After being at Snowflake Summit, we're more convinced than ever that they're headed in this direction. VMware is clearly going after cross cloud services, perhaps creating a new category. Basically, every large company we see either pursuing Supercloud initiatives or thinking about it. Dell showed Project Alpine at Dell Tech World. That's a Supercloud. Snowflake introducing a new application development capability based on their super PaaS, our term, of course. They don't use the phrase. Mongo, Couchbase, Nutanix, Pure Storage, Veeam, CrowdStrike, Okta, Zscaler. Yeah, all of those guys. Yes, Cisco and HPE. Even though on theCUBE at HPE Discover, Fidelma Russo said on theCUBE, she wasn't a fan of cloaking mechanisms. (Dave laughing) But then we talked to HPE's head of storage services, Omer Asad, and he's clearly headed in the direction that we would consider Supercloud. Again, those cross cloud services, of course, their emphasis is connecting as well on-prem. That single experience, which traditionally has not existed with multi-cloud or hybrid. And we're seeing the emergence of smaller companies like Aviatrix and Starburst and Clumio and others that are building versions of Superclouds that solve for a specific problem for their customers. Even ISVs like Adobe, ADP, we've talked to UiPath. They seem to be looking at new ways to go beyond the SaaS model and add value within their cloud ecosystem, specifically around data as part of their and their customer's digital transformations. So yeah, pretty much every tech vendor with any size or momentum, and new industry players are coming out of hiding and competing, building Superclouds that look a lot like Moschella's matrix, with machine intelligence and blockchains and virtual realities and gaming, all enabled by the internet and hyperscale cloud CAPEX. So it's moving fast and it's the future in our opinion. So don't get too caught up in the past or you'll be left behind. Okay, what about examples? We've given a number in the past but let's try to be a little bit more specific. Here are a few we've selected and we're going to answer the two questions in one section here. What workloads and services will run in Superclouds and what are some examples? Let's start with analytics. Our favorite example of Snowflake. It's one of the furthest along with its data cloud, in our view. It's a Supercloud optimized for data sharing and governance, and query performance, and security, and ecosystem enablement. When you do things inside of that data cloud, what we call a super data cloud. Again, our term, not theirs. You can do things that you could not do in a single cloud. You can't do this with Redshift. You can't do this with SQL server. And they're bringing new data types now with merging analytics or at least accommodate analytics and transaction type data and bringing open source tooling with things like Apache Iceberg. And so, it ticks the boxes we laid out earlier. I would say that a company like Databricks is also in that mix, doing it, coming at it from a data science perspective trying to create that consistent experience for data scientists and data engineering across clouds. Converge databases, running transaction and analytic workloads is another example. Take a look at what Couchbase is doing with Capella and how it's enabling stretching the cloud to the edge with arm based platforms and optimizing for low latency across clouds, and even out to the edge. Document database workloads, look at Mongo DB. A very developer friendly platform that where the Atlas is moving toward a Supercloud model, running document databases very, very efficiently. How about general purpose workloads? This is where VMware comes into play. Very clearly, there's a need to create a common operating environment across clouds and on-prem and out to the edge. And I say, VMware is hard at work on that, managing and moving workloads and balancing workloads, and being able to recover very quickly across clouds for everyday applications. Network routing, take a look at what Aviatrix is doing across clouds. Industry workloads, we see Capital One. It announced its cost optimization platform for Snowflake, piggybacking on Snowflake's Supercloud or super data cloud. And in our view, it's very clearly going to go after other markets. It's going to test it out with Snowflake, optimizing on AWS, and it's going to expand to other clouds as Snowflake's business and those other clouds grows. Walmart working with Microsoft to create an on-premed Azure experience that's seamless. Yes, that counts, on-prem counts. If you can create that seamless and continuous experience, identical experience from on-prem to a hyperscale cloud, we would include that as a Supercloud. We've written about what Goldman is doing. Again, connecting its on-prem data and software tooling, and other capabilities to AWS for scale. And you can bet dollars to donuts that Oracle will be building a Supercloud in healthcare with its Cerner acquisition. Supercloud is everywhere you look. So I'm sorry, naysayers, it's happening all around us. So what's next? Well, with all the industry buzz and debate about the future, John Furrier and I have decided to host an event in Palo Alto. We're motivated and inspired to further this conversation. And we welcome all points of view, positive, negative, multi-cloud, Supercloud, HyperCloud, all welcome. So theCUBE on Supercloud is coming on August 9th out of our Palo Alto studios. We'll be running a live program on the topic. We've reached out to a number of industry participants; VMware, Snowflake, Confluent, Skyhigh Security, G. Written House's new company, HashiCorp, CloudFlare. We've hit up Red Hat and we expect many of these folks will be in our studios on August 9th. And we've invited a number of industry participants as well that we're excited to have on. From industry, from financial services, from healthcare, from retail, we're inviting analysts, thought leaders, investors. We're going to have more detail in the coming weeks, but for now, if you're interested, please reach out to me or John with how you think you can advance the discussion, and we'll see if we can fit you in. So mark your calendars, stay tuned for more information. Okay, that's it for today. Thanks to Alex Myerson who handles production and manages the podcast for "Breaking Analysis." And I want to thank Kristen Martin and Cheryl Knight. They help get the word out on social and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does a lot of editing and appreciate you posting on SiliconANGLE, Rob. Thanks to all of you. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search, breaking analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me directly at david.vellante@siliconangle.com. Or DM me @DVallante, or comment on my LinkedIn post. And please, do check out etr.ai for the best survey data in the enterprise tech business. We'll be at AWS NYC summit next Tuesday, July 12th. So if you're there, please do stop by and say hello to theCUBE. It's at the Javits Center. This is Dave Vallante for theCUBE Insights, powered by ETR. Thanks for watching. And we'll see you next time on "Breaking Analysis." (slow music)

(upbeat music) >> Full stack observability is all the rage today. As businesses lean into digital, customer experience becomes ever more important. Why? Well, it's obvious, fickle consumers can switch brands in the blink of an eye or the click of a mouse. Technology companies have sprung into action and the observability space is getting pretty crowded in an effort to simplify the process of figuring out the root cause of application performance problems without an army of PhDs and lab coats, also known as endlessly digging through logs, for example. We see decades old software companies that have traditionally done monitoring or log analytics and or application performance management stepping up their game. These established players, you know, they typically have deep feature sets and sometimes purpose-built tools that attack one particular segment of the marketplace. And now they're pivoting through M&A and some organic development trying to fill gaps in their portfolio. And then, you got all these new entrants coming to the market, claiming end to end visibility across the so-called modern cloud and now edge native stacks. Meanwhile, cloud players are gaining traction and participating through a combination of native tooling combined with strong ecosystems to address this problem. But, you know, recent survey research from ETR confirms our thesis that no one company has it all. Here's the thing. Customers just want to figure out the root cause as quickly and as efficiently as possible. It's one thing to observe the stack end to end, but the question is who is automating the observers? And that's why we're here today. Hello, my name is Dave Vellante and welcome to this special Cube presentation where we dig into root cause analysis, and specifically, how one company, Zebrium, is using unsupervised machine learning to detect anomalies and pinpoint root causes and delivering it as an automated service. And in this session, we have two deep dives. First, we're going to dig into this exciting new field of RCaaS, Root Cause As A Service with two of the founders and technical experts behind Zebrium. And then we bring in two technical experts from Cisco, an early Zebrium customer who ran a POC with Zebrium's service, automating and identifying root cause problems within four very well established and well known Cisco product lines, including WebEx Client and UCS. I was pretty amazed at the results and I think you'll be impressed as well. So thanks for being here. Let's get started. With me right now is Larry Lancaster, who's a founder and CTO of Zebrium. And he's joined by Rod Bagg, who's the founder and vice president of engineering at the company. Gents, welcome. Thanks for coming on. >> Thanks. >> Okay. >> It's good to be here. >> It's good to be here >> All right Rod, talk to me. Talk to me about software downtime, what root cause means, all the buzzwords in your domain, MTTR and SLO. What do we need to know? >> Yeah, I mean, it's like you said. I mean, it's extremely important to our customers and to most businesses out there to drive uptime and avoid as much downtime as possible. So, you know, when you think about it, all of these businesses, most companies nowadays, either their product is software and it's running, you know, running on the web and that's how you get a point click. Or the business depends on, you know, internal systems to drive their business and to run it. When that is down, that is hugely impacting to them. So if you take a look, you know, way back, you know, 20, 30 years ago, software was simple. You know, there wasn't much to it. It was pretty monolithic and maybe it took a couple of people to maintain it and keep it running. There wasn't really anything complicated about it. It was a single tenant piece of software. Today's software is so complicated, often running, you know, maybe hundreds of services to keep that or to actually implement what that software is doing. So as you point out, you know, enter the sort of observability space and the tools that are now in use to help monitor that software and make sure when something goes wrong, they know about it But there's kind of an interesting stat around the observability space. So when you look at observability in the context or through the lens of the cost of downtime, it's really interesting. So observability tools are about a $20 billion market, okay? But the cost of downtime, even with that in place, is still hundreds of billions of dollars. So you're not taking much of a bite out of what the real problem is. You have to solve root cause and get to that fast. So it's all great to know that something went wrong but you got to know why. And it's our contention here that, you know, really, when you take a look at the observability space, you have metrics, that's a great tool. I mean, there's lots of great tools out there, you know, around metrics monitoring that's going to tell you when something went wrong. It's very rarely it's going to tell you why. Similarly for tracing, it's going to point you to where the issue is. It's going to take you through that stack and probably pinpoint where you're being, you know where it's happening or where something is running slow, potentially. So that's great. But again, the root cause of why it's happening is going to be buried in log files. And I can expand on that a little bit more but you know, when you're a software developer and you're writing your software, those log files are a wealth of information. It's just a set of breadcrumbs that are littered with facts about how the software is behaving and why it's doing what it's doing, or why it went wrong. And it's that that really gets you to the root cause very fast. And that's our contention, is that these software systems are so complex nowadays and that the root cause is lying in those logs. So how do you get there fast? You know, we would contend that you better automate that or you are just doomed for failure. And that's where we come in. >> Great. >> Getting to that root cause. >> Thank you, Rod. You know, it's interesting you talk about the $20 billion market. There's an analogy with security, right? We spend 80, $100 billion a year on securing our infrastructure, and yet we lose probably closer to a trillion dollars a year in breaches. And there's a similar analogy here. 20 billion could be 5X in downtime impacts or more. Okay, let's go to Larry. Tell us a little bit more about Zebrium. I'm interested always to ask a founder why you started the company. Rod touched on that a little bit. You guys have invented this concept of RCaaS. What does it mean? What problems does it solve, and how does it solve the problem? Let's get into it. >> Yeah. Hey, thanks, Dave. So I think when you said, you know, who's automating the observer, that that's a great way to think about it because what observability really means is it's a property of a system that means you can see into it. You can observe the internal state and that makes it easier to troubleshoot, right? But the problem is if it's too complicated, you just push the bottleneck up to your eyeball. There's only so much a person can filter through manually, right? And I love the way you put that. So that's a great way to think about it is automating the observer. Now, of course, it means that, you know, you reduce your MTTR, you meet your service level objectives, all that stuff, you improve customer experience. That's all true, but it's important to step back and realize like we have cracked a real nut here. People have been trying to figure out how to automate this part of sort of the troubleshooting experience, this human part of finding the root cause indicators for a long time. And until Zebrium came along, I would argue, no one's really done it right. So, you know, I think it's also important you know, as we step back, we can probably look forward five to 10 years and say, everyone's going to look back and say how did we do all this manually? You're going to see this sort of last mile of observability and troubleshooting is going to be automated everywhere because otherwise, you know, people are just... They're not going to be able to scale their business. So, you know, I think one more thing that's important to point out is, you know, I think Zebrium, you know, it's one thing to have the technology but we've learned we need to deliver it right where people are today. You can't just expect people to dive into a new tool. So, you know, we're looking at, you know, if you look at Zebrium, you'll put us on your dashboard and we don't care what kind of a dashboard it is. It could be, you know Datadog, New Relic, Elastic, Dynatrace, Grafana AppDynamics, ScienceLogic, we don't care. You know, they're all our friends. So we're more interested in getting to that root cause than trying to fight, you know, these incumbents and all that stuff. Yep. >> Yeah. So, interesting. Again, another analogy I think about. You know, you talked about automation. If we're to look back and say this is what... We're never going to do this again, it's like provisioning loans. Nobody provisions loans anymore, it's all automated. >> Larry: (chuckling) That's right. >> So Larry, I'll stay with you, then the skeptic in me says, this sounds amazing, but if I, you know... It might be too good to be true. Tell us how it works. >> Larry: (chuckling) Yeah. So that's interesting. So Cisco came along and they were equally skeptical. So what they did was they took a couple of months and they did a very detailed study. And they got together 192 incidents across four product lines, where they knew that the root cause was in the logs. And they knew what that root cause was because they had had their best engineers, you know work on those cases and take detailed notes of the incidents that had taken place. And so they ran that data through the Zebrium software. And what they found was that in more than 95% of those incidents, Zebrium reflected the correct root cause indicators at the correct time. Like that blew us away. When we saw that kind of evidence, Dave, I have to tell you, everyone was just jumping up and down. It was like, you know, it was like the Apollo command center, you know when they finally, you know, touchdown on the moon kind of thing. So, you know, it's really an exciting point in time to be at the company, like just seeing everything finally being proven out according to this vision. I'm going to tell you one more story which is actually one of my favorites, because we got a chance to work with Seagate Lyve Cloud. So they're, you know, a hyper modern, you know, SaaS business, they're an S3 competitor. Zoom has their files stored on Lyve Cloud, you know, to let you know who they are. So essentially, what happened was they were in alpha, their early access, and they had an outage, and it was pretty bad. I mean, it went on for longer than a day, actually, before they were completely restored. And it was, you know, fortunately for them, it was early access. So no one was expecting, you know, uptime, you know, service level objectives and so on. But they were scared, because they realized, if something like this happens in production, you know, they're screwed. So what they did was they saw Zebrium. They went and did some research, they saw Zebrium. They went in a staging environment, recreated the exact (indistinct) that they had had. And what they saw was immediately, Zebrium pops up a root cause report that tells them exactly the root cause that they took over a day to find. These are the kind of stories that let us know we're onto something transformational. >> Dave: Yeah. That's great. I mean, you guys are jumping up and down, I'm sure. We're going to hear from Cisco later. I bet you, they were jumping up and down too because they didn't have to do all that heavy lifting anymore. So Rod, Larry's just sort of implying that, or actually, you guys both talked about that your tool is agnostic. So how does one actually use the service? How do I deploy it? >> Yeah. So let me step back. So when we talk about logs right? Like, you know, all these bread crumbs being in logs and everything else? So, you know, they are a great wealth of you know, information, but people hate dealing with them. I mean, they hate having to go in and figure out what log to look at. In fact, you know, we had one of our... Or we've heard from several of our customers now prior to using Zebrium, when they, you know, have some issue, and they know there's something wrong, something on their dashboard has told them that something's wrong, maybe a metric has, you know, taken a blip or something's happened that they know there's a problem. We've heard from them that it can take like a number of hours just to get to the right set of logs, like figuring out over these hundreds of services where the logs are, to get to them, maybe searching in a log manager. Just to get into the right context, even, can take hours. So, you know, that's obviously the problem we solve but, you know, we don't want them just looking at logs. I mean, you know, we don't want to put them back in the thing they don't like doing because people don't do that. They don't like doing it. So we put it up on the dashboard. So if something is going wrong with your metrics and that's the indicator, or maybe it's something with tracing that you're sort of digging through that you know something's wrong, we will be right on that same dashboard. So we're deployed as a SaaS service. You send us your logs, you click on one of our integrations and we integrate with all these tools that Larry's talked about. And when we detect anything that is a root cause report, it will show up on your dashboard in the same timeline as those blips in your metrics. So when you see something going wrong and you know there's an issue, take a look at the portion of your dashboard that is us, and we're going to tell you why. We're going to get you to the why that went wrong. No other work could be... You can, you know, also click down and click through to us so that you land up in our portal, if you want to do some more digging around, if you need to or whatever, maybe to get some context what have you, but it's fair that if you ever need to do that, the answer should be right there on your dashboard. And that that's how we expect people to use it. We don't want them digging in logs and going through things, we want it to be right in their workflow. >> Great. Thank you, Larry. So Rod, we talked about Cisco. We're going to hear more from them in a moment in Seagate. I would think this is like a perfect solution for a SaaS provider, anybody doing AI ops. Do you have some examples of those types of firms leaning into this? >> Rod: Yeah, a couple of great ones. Well, I mean, we've got many of them, but a couple that I'll touch on. We have an actual AI ops company that was looking for, you know, sort of some complimentary technology and so on. And so they decided to just put us through our paces by having one of their own SREs sign up for our service in our SaaS environment, and send the logs from their system to us, you know, and just see how we did. So it turned out we ended up talking back to this SRE like a week after he had installed the product, you know signed up and then, you know, started sending us logs. And, you know, he was hewing and hawing, saying that he was busy, like every SRE is, and that he didn't have a chance to really do much with us yet. And, you know, we were just, you know, having this conversation on the phone, and he comes to tell us that, yeah I've been busy because we had this, you know, terrible outage, like, you know, five days ago. And we said like, "Okay did you actually look on the Zebrium dashboard?" (chuckles) And he goes, "You know what? I didn't even think to do it yet. I mean, I'd just been so busy and frazzled." So we have an integration with that company, he hadn't put that integration in, so it wasn't in his dashboard yet, but it was certainly on ours. So he went there, and he looks and he looks on the day, you know, on the time range of when he had had this incident. And right at the very top of the page on our portal was that incident with that root cause. And he was flabbergasted. It literally would've saved him hours and hours and hours. They had this issue going on for over 24 hours. And we had the answer right there in five minutes, and it was crazy. And we get that kind of stories. It's just like the Seagate one. If you use us and you have a problem, we're going to detect it. And you're going to hear from Cisco how successful we are at detecting things. I mean, it'll be there when you have a problem. In SaaS companies, you know, one of our customers is Alchera. They do cost optimizations for cloud properties, you know, for AWS optimization, Google, Google cloud, and so on. But they use our software, and they have a lot of interaction, obviously with these cloud vendors and the APIs of those cloud vendors. So, you know, in order to figure out your costing at AWS, they're using all those APIs. So it turned out, you know, they had some issue where their services were breaking. And we had that root cause report right on the screen, again within five minutes, that was pointing to an API problem with Google. And they had changed one of their APIs and Alchera was not aware of it. So their stuff was breaking because of a change downstream that we had caught. And I'll just tell you one last one because it's somewhat related to one of these cloud vendors. You know, it was a big cloud vendor who had an outage a couple of months ago. And it's interesting because, you know, a lot of our customers will set up shared Slack channels with us, where we're monitoring or seeing their incidents as well as they are. So we get a little Slack representation of the incident that we detected for them or the root cause that we detected for them, and that's in a shared community channel. So we could see this happening when that AWS outage happened. We could see our customers getting impacted by that AWS outage, and the root cause of what was going on there in AWS that was impacting our customers that was showing up in our incidents. Now we didn't obviously, you know, have the very root cause of what was going on in AWS, per se but we were getting to the root cause of why our customer's applications were failing. And that was because of issues going on at AWS. >> Very interesting. I mean, I think one of your biggest challenges is going to be getting people's attention because these SREs are so busy, their hair's on fire. >> Rod: That's it. Right. (chuckling). You know, when you say, hey, (indistinct). >> I tell you, if you get their attention, they love it. I mean, this AI ops company, I didn't even tell you the punchline there, but, you know, they had this incident that occurred that we found. And quite literally, the next week, they ended up signing up as a paid customer. So... >> Dave: that's great. And Larry, to give you the last word. I mean, you know, Rod was talking about, you know, changes in APIs and you know, there's still a lot of scripts out there. You guys, if I understand it correctly, run both as a service in the cloud and you can run on-prem, which is important because there's a lot of sensitive information in logs that people are trying not to leave. >> Larry: That's right. Absolutely. >> Dave: But close it out here. >> Yeah. I mean, that's right, you can run it on-prem. Just like we run it in our cloud, you can run it in your cloud or on your own infrastructure. Now that's all true. You know, I think the one hurdle now that we have left as a company is getting the word out and getting people to believe that this is actually possible and try it for themselves. You don't believe it, do a POC, try it yourself. And you know, people have become so jaded by the lack of, you know, real, sort of, innovation in the software industry for the last 10 years that it's hard to get people to... But guys, you got to give it a shot, I'm telling you. I'm telling you right now, it works. And you'll hear more about that from one of our customers in a minute. >> All right guys, thanks so much. Great story. Really appreciate you sharing. >> Thank you. >> Yeah. Thanks Dave. Appreciate the time. >> Okay. In a moment, we're going to hear from Cisco who is the customer in this case example and a company that has... Look, they have quite an impressive suite of observability tooling, and they've done a pretty compelling proof of concept with Zebrium using real data on some Cisco products that you've heard of, like WebEx. So stay tuned and learn about how you can really take advantage of this new technology called Root Cause As A Service. You're watching theCube, the leader in enterprise and emerging tech coverage. (upbeat music)

(bright intro music) >> Full stack observability is all the rage today. As businesses lean in to digital, customer experience becomes ever more important, why? Well, it's obvious. Fickle consumers can switch brands in the blink of an eye or the click of a mouse. Technology companies have sprung into action, and the observability space is getting pretty crowded in an effort to simplify the process of figuring out the root cause of application performance problems without an army of PhDs and lab coats, also known as endlessly digging through logs, for example. We see decades-old software companies that have traditionally done monitoring or log analytics and/or application performance management stepping up their game. These established players, you know, they typically have deep feature sets and sometimes purpose built tools that attack one particular segment of the marketplace, and now, they're pivoting through M&A and some organic development trying to fill gaps in their portfolio, and then you got all these new entrants coming to the market claiming end to end visibility across the so-called modern cloud and now edge-native stacks. Meanwhile, cloud players are gaining traction and participating through a combination of native tooling combined with strong ecosystems to address this problem, but, you know, recent survey research from ETR confirms our thesis that no one company has at all. Here's the thing. Customers just want to figure out the root cause as quickly and efficiently as possible. It's one thing to observe the stack end to end, but the question is who is automating the observers? And that's why we're here today. Hello, my name is Dave Vellante, and welcome to this special "CUBE" presentation where we dig into root cause analysis and, specifically, how one company, Zebrium, is using unsupervised machine learning to detect anomalies and pinpoint root causes and delivering it as an automated service. In this session, we have two deep dives. First, we're going to dig into this exciting new field of RCA, root cause as a service, with two of the founders and technical experts behind Zebrium, and then we bring in two technical experts from Cisco, an early Zebrium customer who ran a POC with Zebrium's service, automating and identifying root cause problems within four very well established and well-known Cisco product lines including Webex client and UCS. I was pretty amazed at the results, and I think you'll be impressed as well. So thanks for being here. Let's get started with me right now is Larry Lancaster who's a founder and CTO of Zebrium, and he's joined by Rod Bagg who's a founder and Vice-President of Engineering at the company. Gents, welcome, thanks for coming on. >> Thanks. >> (indistinct). >> To be here. >> Great to be here. >> All right, Rod, talk to me. Talk to me about software downtime, what root cause means, all the buzzwords in your domain, MTTR and SLO, what do we need to know? >> Yeah, I mean, it's like you said. I mean, it's extremely important to our customers and to most businesses out there to drive up time and avoid as much downtime as possible. So, you know, when you think about it, all of these businesses, most companies nowadays, either their product is software and it's running, you know, running on the web, and that that's how you get a point click or their business depends on it and, you know, internal systems to drive their business and to run it. Now, when that is down, that is hugely impacting to them. So if you take a look, you know, way back, you know, 20, 30 years ago, software was simple. You know, there wasn't much to it. It was pretty monolithic, and maybe it took a couple of people to maintain it and keep it running. It wasn't really anything complicated about it. It was a single tenant piece of software. Today's software is so complicated, often running, you know, maybe hundreds of services to keep that or to actually implement what that software is doing. So as you point out, you know, enter the sort of observability space and the tools that are now in use to help monitor that software and make sure when something goes wrong, they know about it, but there's kind of an interesting stat around the observability space. So when you look at observability in the context or through the lens of the cost of downtime, it's really interesting. So observability tools are about a $20 billion market, okay? But the cost of downtime, even with that in place, is still hundreds of billions of dollars. So you're not taking much of a bite out of what the real problem is. You have to solve root cause and get to that fast. So it's all great to know that something went wrong, but you got to know why, and it it's our contention here that, you know, really, when you take a look at the observability space, you have metrics. That's a great tool. I mean, there's lots of great tools out there, you know, around metrics monitoring that's going to tell you when something went wrong. It's very rarely it's going to tell you why. Similarly for tracing, it's going to point you to where the issue is. It's going to take you through that stack and probably pinpoint where you're being, you know, where it's happening or where something is running slow potentially. So that's great, but again, the root cause of why it's happening is going to be buried in log files, and I can expand on that a little bit more, but, you know, when you're a software developer, and you're writing your software, those log files are a wealth of information. It's just a set of breadcrumbs that are littered with facts about how the software is behaving and why it's doing what it's doing or why it went wrong, and it's that that really gets you to the root cause very fast, and that's, our contention is that these software systems are so complex nowadays, and that the root cause is lying in those logs. So how do you get there fast? You know, we would contend that you better automate that or you're just doomed for failure, and that's where we come in. >> Great. >> Getting to that request. >> Thank you, Rod. You know, it's interesting. You talk about the $20 billion market. There's an analogy with security, right? We spend 80, $100 billion a year on securing our infrastructure, and yet we lose, probably, closer to a trillion dollars a year in breaches, and there's a similar analogy here. 20 billion could be 5x in downtime impacts or more. Okay, let's go to Larry. Tell us a little bit more about Zebrium. I'm interested always to ask a founder why you started the company. Rod touched on that a little bit. You guys have invented this concept of RCAs. What does it mean? What problems does it solve? And how does it solve the problem? Let's get into it. >> Yeah, hey, thanks, Dave. So I think when you said, you know, who's automating the observer? That's a great way to think about it because what observability really means is it's a property of a system that means you can see into it. You can observe the internal state, and that makes it easier to troubleshoot, right? But the problem is if it's too complicated, you just push the bottleneck up to your eyeball. There's only so much a person can filter through manually, right? And I love the way you put that. So that's a great way to think about it is automating the observer. Now, of course, it means that, you know, you reduce your MTTR, you meet your service level objectives, all that stuff, you improve customer experience, that's all true, but it's important to step back and realize like we have cracked a real nut here. People have been trying to figure out how to automate this part of sort of the troubleshooting experience, this human part of finding the root cause indicators for a long time, and until Zebrium came along, I would argue no one's really done it right. So, you know, I think it's also important, you know, as we step back, we can probably look forward five to 10 years and say, "Everyone's going to look back and say, 'How did we do all this manually?'" You're going to see this sort of last mile of observability and troubleshooting is going to be automated everywhere because otherwise, you know, people are just, they're not going to be able to scale their business. So, you know, I think one more thing that's important to point out is, you know, I think Zebrium, you know, it's one thing to have the technology, but we've learned we need to deliver it right where people are today. You can't just expect people to dive into a new tool. So, you know, we're looking at, you know, if you look at Zebrium, you'll put us on your dashboard, and we don't care what kind of a dashboard it is. It could be, you know, Datadog, New Relic, Elastic, Dynatrace, Grafana, AppDynamics, ScienceLogic, we don't care. You know, they're all our friends. So we're more interested in getting to that root cause than trying to fight, you know, these incumbents and all that stuff, yeah. >> Yeah, so interesting. Again, another analogy I think about, you know, you talked about automation, where to look back, and say, "This is what- We're never going to do this again." It's like provisioning LANs. Nobody provisioned LANs anymore. It's all automated. >> That's correct. >> So, Larry, stay with you. The skeptic in me says, "This sounds amazing," but if, you know, it probably too good to be true. Tell us how it works. >> Yeah, so that's interesting. So Cisco came along and they were equally skeptical. So what they did was they took a couple of months, and they did a very detailed study, and they got together 192 incidents across four product lines where they knew that the root cause was in the logs, and they knew what that root cause was because they'd had their best engineers, you know, work on those cases and take detailed notes of the incidents that had taken place, and so they ran that data through the Zebrium software, and what they found was that in more than 95% of those incidents, Zebrium reflected the correct root cause indicators at the correct time. Like that blew us away. When we saw that kind of evidence, Dave, I have to tell you, everyone was just jumping up and down. It was like, you know, it was like the Apollo Command Center, you know, when they finally, (Dave laughs) you know, touchdown on the moon kind of thing. So, you know, it's really exciting at a point in time to be at the company, like just seeing everything finally being proven out according to this vision. I'm going to tell you one more story, which is actually one of my favorites, because we got a chance to work with Seagate Lyve Cloud. So they're, you know, a hyper modern, you know, SaaS business. They're an S3 competitor. Zoom has their files stored on Lyve Cloud to give, you know, to let you know who they are. So, essentially, what happened was they were in alpha, in their early access, and they had an outage, and it was pretty bad. I mean, it went on for longer than a day, actually, before they were completely restored, and it was, you know, fortunately, for them, it was early access. So no one was expecting, you know, uptime, you know, service level objectives and so on, but they were scared because they realized if something like this happens in production, you know, they're screwed. So what they did was they saw Zebrium, they did some research, they saw Zebrium. They went in a staging environment, recreated the exact (indistinct) that they'd had, and what they saw was, immediately, Zebrium pops up a root cause report that tells them exactly the root cause that they took over a day to find. These are the kind of stories that let us know we're onto something transformational. >> Yeah, that's great. I mean, you guys are jumping up and down. I'm sure, we're going to hear from Cisco later. I bet you, they were jumping up and down, too, 'cause they didn't have to do all that heavy lifting anymore. So Rod, Larry's just sort of implying that or, actually, you guys both talked about that your tool's agnostic. So how does one actually use the service? How do I deploy it? >> Yeah, so let me step back. So when we talk about logs, right? Like, you know, all these red crumbs being in logs and everything else. So, you know, they are a great wealth of, you know, information, but people hate dealing with them. I mean, they hate having to go in and figure out what log to look at. In fact, you know, we had one of our, or we've heard from several of our customers now prior to using Zebrium, but when they're, you know, have some issue, and they know there's something wrong, something on their dashboard has told them that something's wrong, maybe a metrics is, you know, taken a blip or something's happened that they know there's a problem, we've heard from them that it can take like a number of hours just to get to the right set of logs, like figuring out over these hundreds of services where the logs are to get to them, maybe searching in a log manager, just to get into the right context even can take hours. So, you know, that's obviously the problem we solve, but, you know, we don't want them just looking at logs. I mean, you know, we don't want to put 'em back in the thing they don't like doing 'cause people don't do what they don't like doing. So we put it up on the dashboard. So if something is going wrong with your metrics, and that's the indicator or maybe it's something with tracing that you're sort of digging through now that you know something's wrong, we will be right on that same dashboard. So we're deployed as a SaaS service. You send us your logs. You click on one of our integrations, and we integrate with all these tools that Larry's talked about, and when we detect anything that is a root cause report, it will show up on your dashboard in the same timeline as those blips in your metrics. So when you see something going wrong, and you know there's an issue, take a look at the portion of your dashboard that is us, and we're going to tell you why. We're going to get you to the why that went wrong. Not no other work could be- You can, you know, also click down and click through to us so that you land up in our portal if you want to do some more digging around if you need to or whatever, maybe to get some context, what have you, but it's fair that you ever need to do that. The answer should be right there on your dashboard, and that's how we expect people to use it. We don't want them digging in logs and going through things. We want it to be right in their workflow. >> Great, thank you, Larry. So Rod, we talked about Cisco. We're going to hear more from them in a moment and Seagate. I would think this is like a perfect solution for a SaaS provider, anybody doing AIOps, do you have some examples of those types of firms leaning into this? >> Yeah, a couple of great, well, I mean, we got many of them, but couple that I'll touch on. We have an actual AIOps company that was looking for, you know, sort of some complimentary technology and so on, and so they decided to just put us through our paces by having one of their own SREs sign up for our service in our SaaS environment and send the logs from their system to us, you know, and just see how we did. So it turned out we ended up talking back to this SRE like a week after he had installed the product, you know, signed up, and then, you know, started sending us logs, and, you know, he was hemming and hawing saying that he was busy like, you know, like every SRE is, and that he didn't have a chance to really do much with us yet, and, you know, we just, you know, having this conversation on the phone, and he comes to tell us that, "Yeah, I've been busy because we had this, you know, terrible outage like, you know, five days ago," and we said like, "Okay, did you actually look on the Zebrium dashboard?" (laughs) And he goes, "You know what? I didn't even think to do it yet. I mean, I'd just been so busy and frazzled." So we have an integration with that company. He hadn't put that integration in so it wasn't in his dashboard yet, but it was certainly on ours. So he went there and he looks on the day like, you know, on the time range of when he had this incident, and right at the very top of the page on our portal was the incident with the root cause, and he was flabbergasted. It literally would've saved him hours and hours and hours. They had this issue going on for over 24 hours, and we had the answer right there in five minutes, and it was crazy, and we get that kind of story. It's just like the Seagate one. If you use us and you have a problem, we're going to detect it, and you're going to hear from Cisco how successful we are at detecting things. I mean, it'll be there when you have a problem. In SaaS companies, you know, one of our customers is Archera. They do cost optimizations for cloud properties, you know, for AWS optimization, Google cloud, and so on, but they use our software, and they have a lot of interaction, obviously, with these cloud vendors and the APIs of those cloud vendors. So, you know, in order to figure out you're costing at AWS, they're using all those APIs. So it turned out, you know, they had some issue where their services were breaking and we had that root cause report right on the screen, again, within five minutes that was pointing to an API problem with Google, and they had changed one of their APIs, and Archera was not aware of it. So their stuff was breaking because of a change downstream that we had caught, and I'll just tell you one last one because it's somewhat related to one of these cloud vendors of, you know, big cloud vendor who had an outage couple of months ago, and it's interesting because, you know, lot of our customers will set up shared Slack channels with us where we're monitoring or seeing their incidents as well as they are. So we get a little Slack representation of the incident that we detected for them or the root cause that we've detected for them, and that's in a shared community channel. So we could see this happening when that AWS outage happened. We could see our customers getting impacted by that AWS outage and the root cause of what was going on there in AWS that was impacting our customers, that was showing up in our incidents. Now, we didn't obviously, you know, have the very root cause of what was going on in AWS per se, but we were getting to the root cause of why our customer's applications were failing, and that was because of issues going on at AWS. >> Very interesting. I mean, I think one of your biggest challenge is going to be getting people's attention because these SREs is so busy, their hair's on fire. (all laughs) You know, he's like, "Hey, chap, I'm going to show you, look at this." >> I tell you. You get their attention, they love it. I mean, this AIOps company, I didn't even tell you the punchline there, but, you know, they had this incident that occurred that we found and, quite literally, the next week, they ended up signing up as a paid customer, so. >> That's great, and Larry, give you the last word. I mean, you know, Rod was talking about, you know, changes in APIs, and, you know, there's still a lot of scripts out there. You guys, if I understand it correctly, run both as a service in the cloud and you can run on-prem, which is important because there's a lot of sensitive information in logs and people don't want to leave. >> That's right, absolutely. >> But, yeah, close it out here. >> Yeah, I mean, you can, that's right, you can run it on-prem, just like we run it in our cloud. You can run it in your cloud or on your own infrastructure. Now, that's all true. You know, I think the one hurdle now that we have left as a company is getting the word out and getting people to believe that this is actually possible and try it for themselves. You don't believe it? Do a POC, try it yourself. And, you know, people have become so jaded by the lack of, you know, real sort of innovation in the software industry for the last 10 years that it's hard to get people to... But guys, you got to give it a shot. I'm telling you. I'm telling you right now, it works, and you'll hear more about that from one of our customers in a minute. >> Alright guys, thanks so much. Great story, really appreciate you sharing. >> Thank you. >> Yeah, thanks, Dave. Appreciate the time. >> Okay, in a moment, we're going to hear from Cisco who is the customer in this case example, and a company that is... Look, they have quite an impressive suite of observability tooling, and they've done a pretty compelling proof of concept with Zebrium using real data on some Cisco products that you've heard of like Webex. So stay tuned and learn about how you can really take advantage of this new technology called root cause as a service. You're watching "theCUBE", the leader in enterprise and emerging tech coverage. (bright outro music)

>>We're seeing green here at Vemo in 2022, you're watching the cube, Dave ante and David Nicholson wrapping up our second day of coverage. Dave, good show. Good to be, you know, again, good to be back. This is our third show in a row. We're a Cuban as well. So the cube is, is out there, but same every, every show we go to so far has been most of the people here haven't been out in two plus years. Yeah. Right. And, and, and they're like, Hey, let's go. Let's hug. Let's shake. I got my red band on cuz we've been on a lot of shows or just being careful <laugh> um, you know, Hey, but it's great to see people back, uh, >>Absolutely >>Such a different vibe than virtual virtual sucks. Everybody hates it now, but now it's going hybrid. People are trying to figure that out. Yeah. Uh, but it's, it's in your view, what's different. What's the same >>In terms of, uh, in person versus hybrid kind of what's happened since what's >>Different being here now versus say 2019, not that you were here in 2019, but a show in 2019. >>I, I think there's right now, there's a certain sense of, uh, of appreciation for the ability to come and do this. Mm-hmm <affirmative> um, >>As opposed to on we or oh, another show, right? >>Yeah. Yeah, exactly. And, and, uh, a personal opinion is that, um, I think that the hybrid model moving forward is going to end up being additive. I don't know that I don't, you know, people say we'll never go back to having in person the way we did before. Um, I'm holding out hope that that's not the case because I, I think so there's so much value to the kinds of conversations that we have, not only here on the set with folks in person, but just the hallway conversations, uh, the dinner conversations, um, those are so critical, uh, not only with between vendors and customers, but between different business units. Um, you know, I, I, I came into this thinking, you know, I know Veeam very well. I've known them since the beginning. Um, but you think I'm going to a conference to talk about backup software and it wasn't like that at all. I mean, this is, this is an overarching, very, very interesting subject to cover. So how is it different? I think people are appreciative. I wouldn't say we're backed full throttle a hundred percent, um, uh, back in the game yet. But, uh, but we we're getting there. Some >>Of the highlights Veeam now, number one, statistical tie for first place in revenue. There aren't a lot of segments, especially in storage where Dell is not number one, I guess technically Dell is like, I don't know, half a percentage point ahead, but Veeam's gonna blow by that. Unless Dell gets its data, >>Protect me as the luxury of focus, they can focus >>Like a laser on it focus. Right? That, that we, we saw this in the P PC where focused, we saw Dell's ascendancy cuz they were focused on PCs, right? Yeah. We saw Seagate on dis drives Intel and microprocesors Oracle on databases and, and, and Veeam applied that model to what they call modern data protection. Um, and, and the, so the reason why we think they're gonna go past is they growing at 20 plus percent each year. And, and I can almost guarantee Dell's data protection business isn't although it's been in a, I, I sense a downward slope lately, they don't divulge that data. Um, but if they were growing nicely, they would be talking about it. So I think they've been kind of hiding that ball, but Dell, you know, you can't count those guys out they're baby. >>No, you can't. And there's always >>A, they don't like to lose. They get that EMC DNA still in >>There. Yeah. You take, you can, you might take your eye off the ball for a little while to focus on other things. But uh, I think it'll be healthy for the industry at large, as Veeam continues to take market share. There's definitely gonna be pushback from, from others in the field, but >>The pure software play. Um, and you know that no hardware agenda thing and all that I think is, is clearly in Veeam's favor. Uh, but we'll see. I mean, Dell's got other, other strengths as do others. I mean, this is, this is, let's not forget this, this, this market is crowded and getting kind. I mean, you got, you got other players, new, new entrants, like cohesive in Rubrik Rubic, by the way is the one I was kind of referring to. That seems to be, you go to their LinkedIn, they seem to be pivoting to security. I was shocked when I saw that. I'm like, wow, is that just like a desperation move? Is that a way to get your valuation up? Is that, is there something I'm missing? I, I don't know. I haven't talked to those guys in a little bit, need to get, get there, but cause he and Rubrik couldn't get to IPO prior to, uh, you know, the, the, the, the, the tech sell off the tech lash. >>If you will Veeam, didn't need toves. We have 30% EBITDA and, and has had it for a while. So they've been, they caught lightning in a bottle years ago, and then now they got the inside capital behind them. Um, you got new entrance, like, like Kuo, you got com. Vault is out there. You still got, you know, Veritas is still out there competing and you know, a number of other, you get you got is wherever HP software landed in, in the MicroStrategy, uh, micro strategy. <laugh> um, no not micro strategy anyway, in that portfolio of companies that HP sold its software business to, you know, they're still out there. So, you know, a lot of ways to, to buy backup and recovery software, but these guys being the leader is no surprise. >>Yeah. You know, it's, I, I, I have to say it to me. It's a classic story of discipline >>Microfocus, sorry, >>Microfocus. Yeah, that's right. That's right. You know, it's funny. I, I, I could see that logo on a, I know I've got a notebook at home. Um, but, but theme is a classic example of well disciplined growth where you're not playing the latest buzzword game and trying to create adjacent businesses that are really, that might sound sexy, but have nothing to do with your core. They've been very, very disciplined about their approach, starting with, you know, looking at VMFS and saying, this is what we're gonna do, and then branching out from there in a logical way. So, so they're not out ahead of the tips of their skis in a way that some others have have gotten. And those, you know, sometimes swinging for the fence is great, but you can strike out that way also. And they've been hitting, you know, you could say they've been hitting singles and doubles just over and over and over again for years now. Well, that's been a great strategy. >>You've seen this a lot. I mean, I, I think you watched this at EMC when you were there as you, it was acquisitions to try to keep the growth up. It was, it was great marketing. I mean, unbelievable marketing cloud meets big data. Oh yeah. And you'd hear on CNBC. AMC is the cloud company. You're like, eh, fucking have a cloud. So, so you, you you've seen companies do that to your point about getting ahead of your skis. VMs never done that EMS like, eh, this is the product that works great. Yeah. Customers love it. They buy it, you know, we got the distribution channel set up and so that's always been, been, been part of their DNA. Um, and I think the other piece is putting meat on the bone of the tagline of modern data protection. When I first heard that I'm like, mm, okay. >>But then when you peel the onion on that, the core is back up in recovery, a lot of focus on recovery. And then the way they, I remember it was there in the audience when they announced, you know, support for bare metal, people went crazy. I'm like, wow, okay. They cuz they used to say, oh, never virtualization forever. Okay. So they beat that drum and you never say never in this business, do you, and then moving on to cloud and hybrid and containers and we're hearing about super cloud now, and maybe there'll be an edge use case there it's still unclear what that pattern is. You've talked about that with Zs, but it's not clear to me where you put your muscle yet in, um, in edge, but really being able to manage all that data that is people talk about data management that starts to be data management. And they've got a footprint that enables 'em to do that. >>Yeah. And, and I'd like to see that same discipline approach. That's gotten them here to continue no need to get on board a hype cycle. Um, what I really love from a business execution perspective from Veeam is the fact that they know their place in terms of the, their strategic advisory role for end user customers and their places largely in partnership with folks in the channel partners, large and small, um, in a couple of the conversations we had over the last few days, we talked about this idea that there are fewer and fewer seats at the table. Uh, working with customers, customers can't have 25 strategic vendor partners and a lot of smaller niche players that focus on something even as important as backup will pretend that they are, that they hold the same sort of strategic weight as a hyperscale cloud provider. Does they pretend that they're gonna be there in the CX O meetings? Um, when they're not Veeam knows exactly how to best leverage what they do with customers and that's through partners in the channel. >>The other thing is, um, new CEO, a non Eron, uh, the fifth CEO, I think I'm correct. Is that right at, at VE yes. Um, so two founders, uh, and then when Peter McKay came on, he was co CEO. Um, and then, um, yep. And let's see, I think yep. You the fifth. Okay. So each of the CEOs kind of had their own mark. Right. Um, and we asked an on in the analyst thing, what do you want your legacy to be? And I, I loved his answer. He's like, this is a fragmented business with a lot of adjacencies and we are the leader in revenue, but we only have 12% revenue share. I want to take that to 25%, 40%. That's like EMC at 30 plus percent of the storage market, Cisco of 60% of the networking market. Wow. If anybody could ever get there, but so 25 to 30% of a market that's that's big. Yeah. I liked his demeanor thought he had a really good style philosophy. Well-spoken well spoken. So new leadership, obviously insight brought him in to take them to the next level. Um, and, and really drive. I gotta believe get ready for IPO. We kind of admitted that. >>Yeah. And I, and IPO for them, one thing he mentioned is that, um, in this case, this is not an IPO let's high five and go to Vegas and get table service because now we finally have money. Uh, they're not doing, you know, obviously an injection in capital from an IPO is always a good thing or should be a good thing if handled properly, but that's not their primary driver. So it'll be very interesting to see if they can hit the timing. Right. Um, how that, how that works out >>Well and, and bill large is his was predecessor. Uh, he, he, he took over, uh, once the company, excuse me, went private. Um, >>Yeah, that phone backed up. >>I still good in the mic once the company went private, uh, well, no, they were always private. Once they got acquired for five plus billion dollars from inside capital, um, they, they put bill in charge, perfect choice for the transition. And it was like, okay, bill. It's like, when you, my brother's a sailor. He says, Hey, take, take the wheel, see that lighthouse or see that tree go for it, keep it on track. And that's what bill did. Perfect. And he knew the company knew where all the skeletons were buried and, and was perfect. Perfect transition for that. Now they're bringing in somebody who they feel can take it to the next level. They're at a billion. He said he could see 5 billion and, and beyond. So that's kind of cool. Um, the other thing was ecosystem as companies got a really robust ecosystem, all the storage array vendors came on. >>The, the, the backup appliance companies, you know, came on to the cube and had a presence here. Why? Because this is where all the customers are. This is the leader in backup in recovery. Yeah. They all want to partner with that leader. Now they're at out the other shows as well, uh, for the Veeam competitors, but frankly, Veeam, Veeam competitors. They don't have, like you said, they're pure play. Many of them don't have a show like this, or it's a smaller event. Um, and so they gotta be here. Uh, and I think the, the, the other thing was the ransomware study. What I really liked about Veeam is they not only just talked about it, they not only talked about their solution. They sh they did deep dive surveys and shared a ton of data with guys that knew data. Um, Dave Russell and Jason Buffington, both former analysts, Russell was a Gartner very well respected top Gartner analyst for years. Jason buff, Buffington at ESG who those guys did always did some really good, still do deep research. So you had them representing that data, but sharing it with the community, of course, it's, it's gonna be somewhat self-serving, but it wasn't as blatant. It that wasn't nearly as blatant as I often see with these surveys, gender surveys, I'll look at 'em. I can tell within like, seconds, whether it's just a bunch of marketing, you know, what, or there's real substance. Yeah. And this one had real substance to >>It. Yeah. And it's okay. When substance supports your business model. >>Yeah. Cool. >>It's great. Good >>Marketing. But yeah, as an best marketing, I'm not gonna use it. The whole industry can use this and build on it. Yeah. I think there were a lot of unanswered questions. I, what I love about Vema is they're going back and they, they did it in February. They, they updated it just recently. Now they're going back and doing more cuz they want to get it by country. So they're making investments. And then they're sharing that with the industry. I love that. >>It'll be interesting to see if they continue it over time, how things change if things change. Um, one of the things that we really didn't talk a lot about is, uh, and you know, it's, I know it's talked about behind closed doors, um, this idea of, uh, stockpiling day zero exploits, and the fact that a lot of these, these >>Things, >>A lot of these problems arguably could have been headed off, had our taxpayer funded organizations, shared information with private industry in a more timely fashion. Um, um, we had, um, uh, uh, was it, uh, Gina from AWS who gave the example of, uh, the not Petia, uh, experience in the hospital environment. And that came directly out of frankly a day zero exploit that the NSA had identified years earlier within Microsoft's operating system. And, uh, somehow others got ahold of that and used it for nefarious means. So the intent to stockpile and hang onto these things is always, um, noble, but sometimes the result is, uh, less than desirable. So that's, it'll be an interesting conversation. >>We'd be remiss if we didn't mention the, the casting acquisition, the, the, the container data protection, small piece of the business today. Uh, but strategic in the sense that, yeah, absolutely. If you want to appeal to developers, if, if, if, if, if you want to be in the cloud, you know, you better be able to talk containers generally in Kubernetes specifically. So they gotta play there as well. >>Well, they, they, they hit virtualization cloud containers. Maybe I'm missing something in between, but they seem to be >>Ransomware >>Catching waves effectively. Yeah. Ransomware, uh, catching waves effectively, uh, again, not in an artificial buzzword driven way, but in a legitimate disciplined business growth approach that, uh, that's impressive. >>And I, and I think Danny mentioned this, we, he said we've been a PLG product led growth company. Um, and I think they're evolving now. We talked about platforms versus product. We still got still a product company. Uh, but they're bill wants to build out a Supercloud. So we're watching that very closely. I, I think it is a thing. You got a lot of grief for the term, super cloud. Some people wince at it, but it's, there's something brewing. There's something different. That's not just cloud public cloud, not hybrid cloud, not private cloud it's across cloud it's super cloud. All right, Dave, Hey, it was a pleasure working with you this week. Always kind of funny. I mean, we're, the crew was out in, uh, in Valencia, Spain. Yeah. Uh, they'll in fact, they'll be broadcasting, I believe all the way through Friday. Uh, that's an early morning thing for the, uh, for the west coast and, but east coast should be able to catch that easily. >>Of course you can all check out all the replays on the cube.net, also YouTube, youtube.com/silicon angle go to wikibon.com. There's some, you know, research there I publish every week and, and others do, uh, as well, maybe not as frequently, but, uh, we have a great relationship with ETR. I'm gonna poke into some data protection stuff in their survey. See if I can find some interesting, uh, data there. And don't forget to go to Silicon an angle.com, which is all the news. This is the cube, our flagship production we're out at VEON 2022. Thanks for watching.

(upbeat music) >> We're back at VeeamON 2022. We're here at the Aria in Las Vegas Dave Vellante with Dave Nicholson. Bill Andrews is here. He's the president and CEO of ExaGrid, mass boy. Bill, thanks for coming on theCUBE. >> Thanks for having me. >> So I hear a lot about obviously data protection, cyber resiliency, what's the big picture trends that you're seeing when you talk to customers? >> Well, I think clearly we were talking just a few minutes ago, data's growing like crazy, right This morning, I think they said it was 28% growth a year, right? So data's doubling almost just a little less than every three years. And then you get the attacks on the data which was the keynote speech this morning as well, right. All about the ransomware attacks. So we've got more and more data, and that data is more and more under attack. So I think those are the two big themes. >> So ExaGrid as a company been around for a long time. You've kind of been the steady kind of Eddy, if you will. Tell us about ExaGrid, maybe share with us some of the differentiators that you share with customers. >> Sure, so specifically, let's say in the Veeam world you're backing up your data, and you really only have two choices. You can back that up to disc. So some primary storage disc from a Dell, or a Hewlett Packard, or an NetApp or somebody, or you're going to back it up to what's called an inline deduplication appliance maybe a Dell Data Domain or an HPE StoreOnce, right? So what ExaGrid does is we've taken the best of both those but not the challenges of both those and put 'em together. So with disc, you're going to get fast backups and fast restores, but because in backup you keep weekly's, monthly's, yearly retention, the cost of this becomes exorbitant. If you go to a deduplication appliance, and let's say the Dell or the HPs, the data comes in, has to be deduplicated, compare one backup to the next to reduce that storage, which lowers the cost. So fixes that problem, but the fact that they do it inline slows the backups down dramatically. All the data is deduplicated so the restores are slow, and then the backup window keeps growing as the data grows 'cause they're all scale up technologies. >> And the restores are slow 'cause you got to rehydrate. >> You got to rehydrate every time. So what we did is we said, you got to have both. So our appliances have a front end disc cache landing zone. So you're right directed to the disc., Nothing else happens to it, whatever speed the backup app could write at that's the speed we take it in at. And then we keep the most recent backups in that landing zone ready to go. So you want to boot a VM, it's not an hour like a deduplication appliance it's a minute or two. Secondly, we then deduplicate the data into a second tier which is a repository tier, but we have all the deduplicated data for the long term retention, which gets the cost down. And on top of that, we're scale out. Every appliance has networking processor memory end disc. So if you double, triple, quadruple the data you double, triple, quadruple everything. And if the backup window is six hours at 100 terabyte it's six hours at 200 terabyte, 500 terabyte, a petabyte it doesn't matter. >> 'Cause you scale out. >> Right, and then lastly, our repository tier is non-network facing. We're the only ones in the industry with this. So that under a ransomware attack, if you get hold of a rogue server or you hack the media server, get to the backup storage whether it's disc or deduplication appliance, you can wipe out all the backup data. So you have nothing to recover from. In our case, you wipe it out, our landing zone will be wiped out. We're no different than anything else that's network facing. However, the only thing that talks to our repository tier is our object code. And we've set up security policies as to how long before you want us to delete data, let's say 10 days. So if you have an attack on Monday that data doesn't get deleted till like a week from Thursday, let's say. So you can freeze the system at any time and do restores. And then we have immutable data objects and all the other stuff. But the culmination of a non-network facing tier and the fact that we do the delayed deletes makes us the only one in the industry that can actually truly recover. And that's accelerating our growth, of course. >> Wow, great description. So that disc cache layer is a memory, it's a flash? >> It's disc, it's spinning disc. >> Spinning disc, okay. >> Yeah, no different than any other disc. >> And then the tiered is what, less expensive spinning disc? >> No, it's still the same. It's all SaaS disc 'cause you want the quality, right? So it's all SaaS, and so we use Western Digital or Seagate drives just like everybody else. The difference is that we're not doing any deduplication coming in or out of that landing zone to have fast backups and fast restores. So think of it like this, you've got disc and you say, boy it's too expensive. What I really want to do then is put maybe a deduplication appliance behind it to lower the cost or reverse it. I've got a deduplication appliance, ugh, it's too slow for backups and restores. I really want to throw this in front of it to have fast backups first. Basically, that's what we did. >> So where does the cost savings, Bill come in though, on the tier? >> The cost savings comes in the fact that we got deduplication in that repository. So only the most recent backup >> Ah okay, so I get it. >> are the duplicated data. But let's say you had 40 copies of retention. You know, 10 weekly's, 36 monthly's, a few yearly. All of that's deduplicated >> Okay, so you're deduping the stuff that's not as current. >> Right. >> Okay. >> And only a handful of us deduplicate at the layer we do. In other words, deduplication could be anywhere from two to one, up to 50 to one. I mean it's all over the place depending on the algorithm. Now it's what everybody's algorithms do. Some backup apps do two to one, some do five to one, we do 20 to one as well as much as 50 to one depending on the data types. >> Yeah, so the workload is going to largely determine the combination >> The content type, right. with the algos, right? >> Yeah, the content type. >> So the part of the environment that's behind the illogical air gap, if you will, is deduped data. >> Yes. >> So in this case, is it fair to say that you're trading a positive economic value for a little bit longer restore from that environment? >> No, because if you think about backup 95% of the customers restores are from the most recent data. >> From the disc cache. >> 95% of the time 'cause you think about why do you need fast restores? Somebody deleted a file, somebody overwrote a file. They can't go work, they can't open a file. It's encrypted, it's corrupted. That's what IT people are trying to keep users productive. When do you go for longer-term retention data? It's an SEC audit. It's a HIPAA audit. It's a legal discovery, you don't need that data right away. You have days and weeks to get that ready for that legal discovery or that audit. So we found that boundary where you keep users productive by keeping the most recent data in the disc cache landing zone, but anything that's long term. And by the way, everyone else is long term, at that point. >> Yeah, so the economics are comparable to the dedupe upfront. Are they better, obviously get the performance advance? >> So we would be a lot looped. The thing we replaced the most believe it or not is disc, we're a lot less expensive than the disc. I was meeting with some Veeam folks this morning and we were up against Cisco 3260 disc at a children's hospital. And on our quote was $500,000. The disc was 1.4 million. Just to give you an example of the savings. On a Data Domain we're typically about half the price of a Data Domain. >> Really now? >> The reason why is their front end control are so expensive. They need the fastest trip on the planet 'cause they're trying to do inline deduplication. >> Yeah, so they're chasing >> They need the fastest memory >> on the planet. >> this chips all the time. They need SSD on data to move in and out of the hash table. In order to keep up with inline, they've got to throw so much compute at it that it drives their cost up. >> But now in the case of ransomware attack, are you saying that the landing zone is still available for recovery in some circumstances? Or are you expecting that that disc landing zone would be encrypted by the attacker? >> Those are two different things. One is deletion, one is encryption. So let's do the first scenario. >> I'm talking about malicious encryption. >> Yeah, absolutely. So the first scenario is the threat actor encrypts all your primary data. What's does he go for next? The backup data. 'Cause he knows that's your belt and suspend is to not pay the ransom. If it's disc he's going to go in and put delete commands at the disc, wipe out the disc. If it's a data domain or HPE StoreOnce, it's all going to be gone 'cause it's one tier. He's going to go after our landing zone, it's going to be gone too. It's going to wipe out our landing zone. Except behind that we have the most recent backup deduplicate in the repository as well as all the other backups. So what'll happen is they'll freeze the system 'cause we weren't going to delete anything in the repository for X days 'cause you set up a policy, and then you restore the most recent backup into the landing zone or we can restore it directly to your primary storage area, right? >> Because that tier is not network facing. >> That's right. >> It's fenced off essentially. >> People call us every day of the week saying, you saved me, you saved me again. People are coming up to me here, you saved me, you saved me. >> Tell us a story about that, I mean don't give me the names but how so. >> I'll actually do a funnier story, 'cause these are the ones that our vendors like to tell. 'Cause I'm self-serving as the CEO that's good of course, a little humor. >> It's your 15 minutes of job. >> That is my 15 minutes of fame. So we had one international company who had one ExaGrid at one location, 19 Data Domains at the other locations. Ransomware attack guess what? 19 Data Domains wiped out. The one ExaGrid, the only place they could restore. So now all 20 locations of course are ExaGrids, China, Russia, Mexico, Germany, US, et cetera. They rolled us out worldwide. So it's very common for that to occur. And think about why that is, everyone who's network facing you can get to the storage. You can say all the media servers are buttoned up, but I can find a rogue server and snake my way over the storage, I can. Now, we also of course support the Veeam Data Mover. So let's talk about that since we're at a Veeam conference. We were the first company to ever integrate the Veeam Data Mover. So we were the first actually ever integration with Veeam. And so that Veeam Data Mover is a protocol that goes from Veeam to the ExaGrid, and we run it on both ends. So that's a more secure protocol 'cause it's not an open format protocol like SaaS. So with running the Veeam Data Mover we get about 30% more performance, but you do have a more secure protocol layer. So if you don't get through Veeam but you get through the protocol, boom, we've got a stronger protocol. If you make it through that somehow, or you get to it from a rogue server somewhere else we still have the repository. So we have all these layers so that you can't get at it. >> So you guys have been at this for a while, I mean decade and a half plus. And you've raised a fair amount of money but in today's terms, not really. So you've just had really strong growth, sequential growth. I understand it, and double digit growth year on year. >> Yeah, about 25% a year right now >> 25%, what's your global strategy? >> So we have sales offices in about 30 countries already. So we have three sales teams in Brazil, and three in Germany, and three in the UK, and two in France, and a lot of individual countries, Chile, Argentina, Columbia, Mexico, South Africa, Saudi, Czech Republic, Poland, Dubai, Hong Kong, Australia, Singapore, et cetera. We've just added two sales territories in Japan. We're adding two in India. And we're installed in over 50 countries. So we've been international all along the way. The goal of the company is we're growing nicely. We have not raised money in almost 10 years. >> So you're self-funding. You're cash positive. >> We are cash positive and self-funded and people say, how have you done that for 10 years? >> You know what's interesting is I remember, Dave Scott, Dave Scott was the CEO of 3PAR, and he told me when he came into that job, he told the VCs, they wanted to give him 30 million. He said, I need 80 million. I think he might have raised closer to a hundred which is right around what you guys have raised. But like you said, you haven't raised it in a long time. And in today's terms, that's nothing, right? >> 100 is 500 in today's terms. >> Yeah, right, exactly. And so the thing that really hurt 3PAR, they were public companies so you could see all this stuff is they couldn't expand internationally. It was just too damn expensive to set up the channels, and somehow you guys have figured that out. >> 40% of our business comes out of international. We're growing faster internationally than we are domestically. >> What was the formula there, Bill, was that just slow and steady or? >> It's a great question. >> No, so what we did, we said let's build ExaGrid like a McDonald's franchise, nobody's ever done that before in high tech. So what does that mean? That means you have to have the same product worldwide. You have to have the same spares model worldwide. You have to have the same support model worldwide. So we early on built the installation. So we do 100% of our installs remotely. 100% of our support remotely, yet we're in large enterprises. Customers racks and stacks the appliances we get on with them. We do the entire install on 30 minutes to about three hours. And we've been developing that into the product since day one. So we can remotely install anywhere in the world. We keep spares depots all over the world. We can bring 'em up really quick. Our support model is we have in theater support people. So they're in Europe, they're in APAC, they're in the US, et cetera. And we assign customers to the support people. So they deal with the same support person all the time. So everything is scalable. So right now we're going to open up India. It's the same way we've opened up every other country. Once you've got the McDonald's formula we just stamp it all over the world. >> That's amazing. >> Same pricing, same product same model, same everything. >> So what was the inspiration for that? I mean, you've done this since day one, which is what like 15, 16 years ago. Or just you do engineering or? >> No, so our whole thought was, first of all you can't survive anymore in this world without being an international company. 'Cause if you're going to go after large companies they have offices all over the world. We have companies now that have 17, 18, 20, 30 locations. And there were in every country in the world, you can't go into this business without being able to ship anywhere in the world and support it for a single customer. You're not going into Singapore because of that. You're going to Singapore because some company in Germany has offices in the U.S, Mexico Singapore and Australia. You have to be international. It's a must now. So that was the initial thing is that, our goal is to become a billion dollar company. And we're on path to do that, right. >> You can see a billion. >> Well, I can absolutely see a billion. And we're bigger than everybody thinks. Everybody guesses our revenue always guesses low. So we're bigger than you think. The reason why we don't talk about it is we don't need to. >> That's the headline for our writers, ExaGrid is a billion dollar company and nobody's know about it. >> Million dollar company. >> On its way to a billion. >> That's right. >> You're not disclosing. (Bill laughing) But that's awesome. I mean, that's a great story. I mean, you kind of are a well kept secret, aren't you? >> Well, I dunno if it's a well kept secret. You know, smaller companies never have their awareness of big companies, right? The Dells of the world are a hundred billion. IBM is 70 billion, Cisco is 60 billion. Easy to have awareness, right? If you're under a billion, I got to give a funny story then I think we got to close out here. >> Oh go ahead please. >> So there's one funny story. So I was talking to the CIO of a super large Fortune 500 company. And I said to him, "Just so who do you use?" "I use IBM Db2, and I use, Cisco routers, and I use EMC primary storage, et cetera. And I use all these big." And I said, "Would you ever switch from Db2?" "Oh no, the switching costs would kill me. I could never go to Oracle." So I said to him, "Look would you ever use like a Pure Storage, right. A couple billion dollar company." He says, "Who?" >> Huh, interesting. >> I said to him, all right so skip that. I said, "VMware, would you ever think about going with Nutanix?" "Who?" Those are billion dollar plus companies. And he was saying who? >> Public companies. >> And he was saying who? That's not uncommon when I talk to CIOs. They see the big 30 and that's it. >> Oh, that's interesting. What about your partnership with Veeam? Tell us more about that. >> Yeah, so I would actually, and I'm going to be bold when I say this 'cause I think you can ask anybody here at the conference. We're probably closer first of all, to the Veeam sales force than any company there is. You talk to any Veeam sales rep, they work closer with ExaGrid than any other. Yeah, we are very tight in the field and have been for a long time. We're integrated with the Veeam Data Boomer. We're integrated with SOBR. We're integrated with all the integrations or with the product as well. We have a lot of joint customers. We actually do a lot of selling together, where we go in as Veeam ExaGrid 'cause it's a great end to end story. Especially when we're replacing, let's say a Dell Avamar to Dell Data Domain or a Dell Network with a Dell Data Domain, very commonly Veeam ExaGrid go in together on those types of sales. So we do a lot of co-selling together. We constantly train their systems engineers around the world, every given week we're training either inside sales teams, and we've trained their customer support teams in Columbus and Prague. So we're very tight with 'em we've been tight for over a decade. >> Is your head count public? Can you share that with us? >> So we're just over 300 employees. >> Really, wow. >> We have 70 open positions, so. >> Yeah, what are you looking for? Yeah, everything, right? >> We are looking for engineers. We are looking for customer support people. We're looking for marketing people. We're looking for inside sales people, field people. And we've been hiring, as of late, major account reps that just focus on the Fortune 500. So we've separated that out now. >> When you hire engineers, I mean I think I saw you were long time ago, DG, right? Is that true? >> Yeah, way back in the '80s. >> But systems guy. >> That's how old I am. >> Right, systems guy. I mean, I remember them well Eddie Castro and company. >> Tom West. >> EMV series. >> Tom West was the hero of course. >> The EMV 4000, the EMV 20,000, right? >> When were kids, "The Soul of a New Machine" was the inspirational book but anyway, >> Yeah Tracy Kidder, it was great. >> Are you looking for systems people, what kind of talent are you looking for in engineering? >> So it's a lot of Linux programming type stuff in the product 'cause we run on a Linux space. So it's a lot of Linux programs so its people in those storage. >> Yeah, cool, Bill, hey, thanks for coming on to theCUBE. Well learned a lot, great story. >> It's a pleasure. >> That was fun. >> Congratulations. >> Thanks. >> And good luck. >> All right, thank you. >> All right, and thank you for watching theCUBE's coverage of VeeamON 2022, Dave Vellante for Dave Nicholson. We'll be right back right after this short break, stay with us. (soft beat music)

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> The pandemic precipitated what is shaping up to be a permanent shift in cybersecurity spending patterns. As a direct result of hybrid work, CSOs have vested heavily in endpoint security, identity access management, cloud security, and further hardening the network beyond the headquarters. We've reported on this extensively in this Breaking Analysis series. Moreover, the need to build security into applications from the start rather than bolting protection on as an afterthought has led to vastly high heightened awareness around DevSecOps. Finally, attacking security as a data problem with automation and AI is fueling new innovations in cyber products and services and startups. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we present our quarterly findings in the security industry, and share the latest ETR survey data on the spending momentum and market movers. Let's start with the most recent news in cybersecurity. Nary a week goes by without more concerning news. The latest focus in the headlines is, of course, Russia's relentless cyber attacks on critical infrastructure in the Ukraine, including banking, government websites, weaponizing information. The hacker group, BlackByte, put a double whammy on the San Francisco 49ers, meaning they exfiltrated data and they encrypted the organization's files as part of its ransomware attack. Then there's the best Super Bowl ad last Sunday, the Coinbase floating QR code. Did you catch that? As people rushed to scan the code and participate in the Coinbase Bitcoin giveaway, it highlights yet another exposure, meaning we're always told not to click on links that we don't trust or we've never seen, but so many people activated this random QR code on their smartphones that it crashed Coinbase's website. What does that tell you? In other news, Securonix raised a billion dollars. They did this raise on top of Lacework's massive $1.3 billion raise last November. Both of these companies are attacking security with data automation and APIs that can engage machine intelligence. Securonix, specifically in the announcement, mentioned the uptake from MSSPs, managed security service providers, something we've talked about in this series. And that's a trend that we see as increasingly gaining traction as customers are just drawing in and drowning in security incidents. Peter McKay's company, Snyk, acquired Fugue, a company focused on making sure security policies are consistent throughout the software development life cycle. It's a really an example of a developer-defined security approach where policy can be checked at the dev, deployment, and production phases to ensure the same policies are in place at all stages, including monitoring at runtime. Fugue, according to Crunchbase, had raised $85 million to date. In some other company news, Cisco was rumored to be acquiring Splunk for not much more than Splunk is worth today. And the talks reportedly broke down. This would be a major move in security by Cisco and underscores the pressure to consolidate. Cisco would get an extremely strong customer base and through efficiencies could improve Splunk's profitability, but it seems like the premium Cisco was willing to pay was not enough to entice board to act. Splunk board, that is. Datadog blew away its earnings, and the stock was up 12%. It's pulled back now, thanks to Putin, but it's one of those companies that is disrupting Splunk. Datadog is less than half the size of Splunk, revenue-wise, but its valuation is more than 2 1/2 times greater. Finally, Elastic, another Splunk disruptor, settled its trademark dispute with AWS, and now AWS will now stop using the name Elasticsearch. All right, let's take a high level look at how cyber companies have performed in the stock market over time. Here's a graph of the Cyber ETF, and you can see the March 1st crosshairs of 2020 signifying the start of the lockdown. The trajectory of cybersecurity stocks is shown by the orange and blue lines, and it surely has steepened post March of 2020. And, of course, it's been down with the market lately, but the run up, as you can see, was substantial and eclipsed the trajectory of the previous cycles over the last couple of years, owing much of the momentum to the spending dynamics that we talked about at our open. Let's now drill into some of the names that we've been following over the last few years and take a look at the firm level. This chart shows some data that we've been tracking since before the pandemic. The top rows show the S&P 500 and the NASDAQ prices, and the bottom rows show specific stocks. The first column is the index price or the market cap of the company just before the pandemic, then the same data one year later. Then the next column shows the peak value during the pandemic, and then the current value. Then it shows in the next column where it is today, in percentage terms, i.e., how far has it pulled back from the peak, then the delta from pre-pandemic, in other words, how much did the issue earn or lose during the pandemic for investors? We then compare the pre-pandemic revenue multiple using a trailing 12-month revenue metric. Sorry, that's what we used. It's easy to get. (laughs) And that's the revenue multiple compared to the August in 2020, when multiples were really high, and where they are today, and then a recent quarterly growth rate guide based on the last earnings report. That's the last column. Okay, so I'm throwing a lot of data at you here, but what does it tell us? First, the S&P and the NAS are well up from pre-pandemic levels, yet they're off 9% and 15%, respectively, from their peaks today. That was earlier on Friday morning. Now let's look at the names more closely. Splunk has been struggling. It definitely had a tailwind from the pandemic as all boats seem to rise, but its execution has been lacking. It's now 30% off from its pre-pandemic levels. (groans) And it's multiple is compressing, and perhaps Cisco thought it could pick up the company for a discount. Now let's talk about Palo Alto Networks. We had reported on some of the challenges the company faced moving into a cloud-friendly model. that was before the pandemic. And we talked about the divergence between Palo Alto's stock price and the valuations relative to Fortinet, and we said at the time, we fully expected Palo Alto to rebound, and that's exactly what happened. It rode the tailwinds of the last two years. It's up over 100% from its pre-COVID levels, and its revenue multiple is expanding, owing to the nice growth rates. Now Fortinet had been doing well coming into the pandemic. In fact, we said it was executing on a cloud strategy better than Palo Alto Networks, hence that divergence in valuations at the time. So it didn't get as much of a boost from the pandemic. Didn't get that momentum at first, but the company's been executing very well. And as you can see, with 155% increase in valuation since just before the pandemic, it's going more than okay for Fortinet. Now, Okta is a name that we've really followed closely, the identity access management specialist that rocketed. But since it's Auth0 acquisition, it's pulled back. Investors are concerned about its guidance and its profitability. And several analyst have downgraded their price targets on Okta. We still really like the company. The Auth0 acquisition gives Okta a developer vector, and we think the company is going hard after market presence and is willing to sacrifice short-term profitability. We actually like that posture. It's very Frank Slupin-like. This company spends a lot of money on R&D and go-to-market. The question is, does Okta have inherent profitability? The company, as they say, spends a ton in some really key areas but it looks to us like it's going to establish a footprint. It's guiding revenue CAGR in the mid-30s over the mid to long-term and near term should beat that benchmark handily. But you can see the red highlights on Okta. And even though Okta is up 59% from its pre-pandemic levels, it's far behind its peers shown in the chart, especially CrowdStrike and Zscaler, the latter being somewhat less impacted by the pullback in stocks recently, of course, due to the fears of inflation and interest rates, and, of course, Russian invasion escalation. But these high flyers, they were bound to pull back. The question is can they maintain their category leadership? And for the most part, we think they can. All right, let's get into some of the ETR data. Here's our favorite XY view with net score, or spending momentum on the Y-axis, and market share or pervasiveness in the data center on the horizontal axis. That red 40% line, that indicates a highly elevated spending level. And the chart inserts to the right, that shows how the data is plotted with net score and shared N in each of the columns by each company. Okay, so this is an eye chart, but there really are three main takeaways. One is that it's a crowded market. And this shows only the companies ETR captures in its survey. We filtered on those that had more than 50 mentions. So there's others in the ETR survey that we're not showing here, and there are many more out there which don't get reported in the spending data in the ETR survey. Secondly, there are a lot of companies above the 40% mark, and plenty with respectable net scores just below. Third, check out SentinelOne, Elastic, Tanium, Datadog, Netskope, and Darktrace. Each has under 100 N's but we're watching these companies closely. They're popping up in the survey, and they're catching our attention, especially SentinelOne, post-IPO. So we wanted to pare this back a bit and filter the data some more. So let's look at companies with more than 100 mentions in the same chart. It gets a little cleaner this picture, but it's still crowded. Auth0 leads everyone in net score. Okta is also up there, so that's very positive sign since they had just acquired Auth0. CrowdStrike SalePoint, Cyberark, CloudFlare, and Zscaler are all right up there as well. And then there's the bigger security companies. Palo Alto Network, very impressive because it's well above the 40% mark, and it has a big presence in the survey, and, of course, in the market. And Microsoft as well. They're such a big whale. They skew the data for everybody else to kind of mess up these charts. And the position of Cisco and Splunk make for an interesting combination. They get both decent net scores, not above the 40% line but they got a good presence in the survey as well. Thinking about the acquisition, Al Shugart was the CEO of of Seagate, and founder. Brilliant Silicon valley icon and engineer. Great business person. I was asking him one time, hey, you thinking about buying this company or that company? And of course, he's not going to tell me who he's thinking about buying or acquiring. He said, let me just tell you this. If you want to know what I'm thinking, ask yourself if it were free, would you take it? And he said the answer's not always obviously yes, because acquisitions can be messy and disruptive. In the case of Cisco and Splunk, I think the answer would be a definitive yes It would expand Cisco's portfolio and make it the leader in security, with an opportunity to bring greater operating leverage to Splunk. Cisco's just got to pay more if it wants that asset. It's got to pay more than the supposed $20 billion offer that it made. It's going to have to get kind of probably north of 23 billion. I pinged my ETR colleague, Erik Bradley, on this, and he generally agreed. He's very close to the security space. He said, Splunk isn't growing the customer base but the customers are sticky. I totally agree. Cisco could roll Splunk into its security suite. Splunk is the leader in that space, security information and event management, and Cisco really is missing that piece of the pie. All right, let's filter the data even more and look at some of the companies that have moved in the survey over the past year and a half. We'll go back here to July 2020. Same two-dimensional chart. And we're isolating here Auth0, Okta, SalePoint CrowdStrike, Zscaler, Cyberark, Fortinet, and Cisco. No Microsoft. That cleans up the chart. Okay, why these firms? Because they've made some major moves to the right, and some even up since last July. And that's what this next chart shows. Here's the data from the January 2022 survey. The arrow start points show the position that we just showed you earlier in July 2020, and all these players have made major moves to the right. How come? Well, it's likely a combination of strong execution, and the fact that security is on the radar of every CEO, CIO, of course, CSOs, business heads, boards of directors. Everyone is thinking about security. The market momentum is there, especially for the leaders. And it's quite tremendous. All right, let's now look at what's become a bit of a tradition with Breaking Analysis, and look at the firms that have earned four stars. Four-star firms are leaders in the ETR survey that demonstrate both a large presence, that's that X-axis that we showed you, and elevated spending momentum. Now in this chart, we filter the N's. Has to be greater than 100. And we isolate on those companies. So more than 100 responses in the survey. On the left-hand side of the chart, we sort by net score or spending velocity. On the right-hand side, we sort by shared N's or presence in the dataset. We show the top 20 for each of the categories. And the red line shows the top 10 cutoffs. Companies that show up in the top 10 for both spending momentum and presence in the data set earn four stars. If they show up in one, and make the top 10 in one, and make the top 20 in the other, they get two stars. And we've added a one-star category as honorable mention for those companies that make the top 20 in both categories. Microsoft, Palo Alto Networks, CrowdStrike, and Okta make the four-star grade. Okta makes it even without Auth0, which has the number one net score in this data set with 115 shared N to boot. So you can add that to Okta. The weighted average would pull Okta's net score to just above Cyberark's into fourth place. And its shared N would bump Okta up to third place on the right-hand side of the chart Cisco, Splunk, Proofpoint, KnowBe4, Zscaler, and Cyberark get two stars. And then you can see the honorable mentions with one star. Now thinking about a Cisco, Splunk combination. You'd get an entity with a net score in the mid-20s. Yeah, not too bad, definitely respectable. But they'd be number one on the right-hand side of this chart, with the largest market presence in the survey by far. Okay, let's wrap. The trends around hybrid work, cloud migration and the attacker escalation that continue to drive cybersecurity momentum and they're going to do so indefinitely. And we've got some bullet points here that you're seeing private companies, (laughs) they're picking up gobs of money, which really speaks to the fact that there's no silver bullet in this market. It's complex, chaotic, and cash-rich. This idea of MSSPs on the rise is going to continue, we think. About half the mid-size and large organization in the US don't have a SecOps, a security operation center, and outsourcing to one that can be tapped on a consumption basis, cloud-like, as a service just makes sense to us. We see the momentum that companies that we've highlighted over the many quarters of Breaking Analysis are forming. They're forming a strong base in the market. They're going for market share and footprint, and they're focusing on growth, at bringing in new talent. They have good balance sheets and strong management teams and we think they'll be leading companies in the future, Zscaler, CrowdStrike, Okta, SentinelOne, Cyberark, SalePoint, over time, joining the ranks of billion dollar cyber firms, when I say billion dollar, billion dollar revenue like Palo Alto Networks, Fortinet, and Splunk, if it doesn't get acquired. These independent firms that really focus on security. Which underscores the pressure and consolidation and M&A in the whole space. It's almost assured with the fragmentation of companies and so many new entrants fighting for escape velocity that this market is going to continue with robust M&A and consolidation. Okay, that's it for today. Thanks to my colleague, Stephanie Chan, who helped research this week's topics, and Alex Myerson on the production team. He also manages the Breaking Analysis podcast. Kristen Martin and Cheryl Knight, who get the word out. Thank you to all. Remember these episodes are all available as podcasts wherever you listen. All you do is search Breaking Analysis podcast. Check out ETR's website at etr.ai. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me at david.vellante@siliconangle.com. @dvellante is my DM. Comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week. Be safe, be well, and we'll see you next time. (upbeat music)

>>Mhm Hello and welcome to the cubes coverage of AWS amazon web services, Global public Sector partner awards program. I'm john for your host of the cube here we're gonna talk about the best covid solution to great guests. Benham or with healthcare and life sciences lead at palantir Ben welcome to the cube SAm Michaels, Director of automation and compound management and Cats. National Center for advancing translational sciences and Cats. Part of the NIH National sort of health Gentlemen, thank you for coming on and and congratulations on the best covid solution. >>Thank you so much john >>so I gotta, I gotta ask you the best solution is when can I get the vaccine? How fast how long it's gonna last but I really appreciate you guys coming on. I >>hope you're vaccinated. I would say john that's outside of our hands. I would say if you've not got vaccinated, go get vaccinated right now, have someone stab you in the arm, you know, do not wait and and go for it. That's not on us. But you got that >>opportunity that we have that done. I got to get on a plane and all kinds of hoops to jump through. We need a better solution anyway. You guys have a great technical so I wanna I wanna dig in all seriousness aside getting inside. Um you guys have put together a killer solution that really requires a lot of data can let's step back and and talk about first. What was the solution that won the award? You guys have a quick second set the table for what we're talking about. Then we'll start with you. >>So the national covered cohort collaborative is a secure data enclave putting together the HR records from more than 60 different academic medical centers across the country and they're making it available to researchers to, you know, ask many and varied questions to try and understand this disease better. >>See and take us through the challenges here. What was going on? What was the hard problem? I'll see everyone had a situation with Covid where people broke through and cloud as he drove it amazon is part of the awards, but you guys are solving something. What was the problem statement that you guys are going after? What happened? >>I I think the problem statement is essentially that, you know, the nation has the electronic health records, but it's very fragmented, right. You know, it's been is highlighted is there's there's multiple systems around the country, you know, thousands of folks that have E H. R. S. But there is no way from a research perspective to actually have access in any unified location. And so really what we were looking for is how can we essentially provide a centralized location to study electronic health records. But in a Federated sense because we recognize that the data exist in other locations and so we had to figure out for a vast quantity of data, how can we get data from those 60 sites, 60 plus that Ben is referencing from their respective locations and then into one central repository, but also in a common format. Because that's another huge aspect of the technical challenge was there's multiple formats for electronic health records, there's different standards, there's different versions. And how do you actually have all of this data harmonised into something which is usable again for research? >>Just so many things that are jumping in my head right now, I want to unpack one at the time Covid hit the scramble and the imperative for getting answers quickly was huge. So it's a data problem at a massive scale public health impact. Again, we were talking before we came on camera, public health records are dirty, they're not clean. A lot of things are weird. I mean, just just massive amount of weird problems. How did you guys pull together take me through how this gets done? What what happened? Take us through the the steps He just got together and said, let's do this. How does it all happen? >>Yeah, it's a great and so john, I would say so. Part of this started actually several years ago. I explain this when people talk about in three C is that and Cats has actually established what we like to call, We support a program which is called the Clinical translation Science Award program is the largest single grant program in all of NIH. And it constitutes the bulk of the Cats budget. So this is extra metal grants which goes all over the country. And we wanted this group to essentially have a common research environment. So we try to create what we call the secure scientific collaborative platforms. Another example of this is when we call the rare disease clinical research network, which again is a consortium of 20 different sites around the nation. And so really we started working this several years ago that if we want to Build an environment that's collaborative for researchers around the country around the world, the natural place to do that is really with a cloud first strategy and we recognize this as and cats were about 600 people now. But if you look at the size of our actual research community with our grantees were in the thousands. And so from the perspective that we took several years ago was we have to really take a step back. And if we want to have a comprehensive and cohesive package or solution to treat this is really a mid sized business, you know, and so that means we have to treat this as a cloud based enterprise. And so in cats several years ago had really gone on this strategy to bring in different commercial partners, of which one of them is Palin tear. It actually started with our intramural research program and obviously very heavy cloud use with AWS. We use your we use google workspace, essentially use different cloud tools to enable our collaborative researchers. The next step is we also had a project. If we want to have an environment, we have to have access. And this is something that we took early steps on years prior that there is no good building environment if people can't get in the front door. So we invested heavily and create an application which we call our Federated authentication system. We call it unified and cats off. So we call it, you know, for short and and this is the open source in house project that we built it and cats. And we wanted to actually use this for all sorts of implementation, acting as the front door to this collaborative environment being one of them. And then also by by really this this this interest in electronic health records that had existed prior to the Covid pandemic. And so we've done some prior work via mixture of internal investments in grants with collaborative partners to really look at what it would take to harmonize this data at scale. And so like you mentioned, Covid hit it. Hit really hard. Everyone was scrambling for answers. And I think we had a bit of these pieces um, in play. And then that's I think when we turned to ban and the team at volunteer and we said we have these components, we have these pieces what we really need. Something independent that we can stand up quickly to really address some of these problems. One of the biggest one being that data ingestion and the harmonization step. And so I can let Ben really speak to that one. >>Yeah. Ben Library because you're solving a lot of collaboration problems, not just the technical problem but ingestion and harmonization ingestion. Most people can understand is that the data warehousing or in the database know that what that means? Take us through harmonization because not to put a little bit of shade on this, but most people think about, you know, these kinds of research or non profits as a slow moving, you know, standing stuff up sandwich saying it takes time you break it down. By the time you you didn't think things are over. This was agile. So take us through what made it an agile because that's not normal. I mean that's not what you see normally. It's like, hey we'll see you next year. We stand that up. Yeah. At the data center. >>Yeah, I mean so as as Sam described this sort of the question of data on interoperability is a really essential problem for working with this kind of data. And I think, you know, we have data coming from more than 60 different sites and one of the reasons were able to move quickly was because rather than saying oh well you have to provide the data in a certain format, a certain standard. Um and three C. was able to say actually just give us the data how you have it in whatever format is easiest for you and we will take care of that process of actually transforming it into a single standard data model, converting all of the medical vocabularies, doing all of the data quality assessment that's needed to ensure that data is actually ready for research and that was very much a collaborative endeavor. It was run out of a team based at johns Hopkins University, but in collaboration with a broad range of researchers who are all adding their expertise and what we were able to do was to provide the sort of the technical infrastructure for taking the transformation pipelines that are being developed, that the actual logic and the code and developing these very robust kind of centralist templates for that. Um, that could be deployed just like software is deployed, have changed management, have upgrades and downgrades and version control and change logs so that we can roll that out across a large number of sites in a very robust way very quickly. So that's sort of that, that that's one aspect of it. And then there was a bunch of really interesting challenges along the way that again, a very broad collaborative team of researchers worked on and an example of that would be unit harmonization and inference. So really simple things like when a lab result arrives, we talked about data quality, um, you were expected to have a unit right? Like if you're reporting somebody's weight, you probably want to know if it's in kilograms or pounds, but we found that a very significant proportion of the time the unit was actually missing in the HR record. And so unless you can actually get that back, that becomes useless. And so an approach was developed because we had data across 60 or more different sites, you have a large number of lab tests that do have the correct units and you can look at the data distributions and decide how likely is it that this missing unit is actually kilograms or pounds and save a huge portion of these labs. So that's just an example of something that has enabled research to happen that would not otherwise have been able >>just not to dig in and rat hole on that one point. But what time saving do you think that saves? I mean, I can imagine it's on the data cleaning side. That's just a massive time savings just in for Okay. Based on the data sampling, this is kilograms or pounds. >>Exactly. So we're talking there's more than 3.5 billion lab records in this data base now. So if you were trying to do this manually, I mean, it would take, it would take to thousands of years, you know, it just wouldn't be a black, it would >>be a black hole in the dataset, essentially because there's no way it would get done. Ok. Ok. Sam take me through like from a research standpoint, this normalization, harmonization the process. What does that enable for the, for the research and who decides what's the standard format? So, because again, I'm just in my mind thinking how hard this is. And then what was the, what was decided? Was it just on the base records what standards were happening? What's the impact of researchers >>now? It's a great quite well, a couple things I'll say. And Ben has touched on this is the other real core piece of N three C is the community, right? You know, And so I think there's a couple of things you mentioned with this, johN is the way we execute this is, it was very nimble, it was very agile and there's something to be said on that piece from a procurement perspective, the government had many covid authorities that were granted to make very fast decisions to get things procured quickly. And we were able to turn this around with our acquisition shop, which we would otherwise, you know, be dead in the water like you said, wait a year ago through a normal acquisition process, which can take time, but that's only one half the other half. And really, you're touching on this and Ben is touching on this is when he mentions the research as we have this entire courts entire, you know, research community numbering in the thousands from a volunteer perspective. I think it's really fascinating. This is a really a great example to me of this public private partnership between the companies we use, but also the academic participants that are actually make up the community. Um again, who the amount of time they have dedicated on this is just incredible. So, so really, what's also been established with this is core governance. And so, you know, you think from assistance perspective is, you know, the Palin tear this environment, the N three C environment belongs to the government, but the N 33 the entire actually, you know, program, I would say, belongs to the community. We have co governance on this. So who decides really is just a mixture between the folks on End Cats, but not just end cast as folks at End Cats, folks that, you know, and I proper, but also folks and other government agencies, but also the, the academic communities and entire these mixed governance teams that actually set the stage for all of this. And again, you know, who's gonna decide the standard, We decide we're gonna do this in Oman 5.3 point one um is the standard we're going to utilize. And then once the data is there, this is what gets exciting is then they have the different domain teams where they can ask different research questions depending upon what has interest scientifically to them. Um and so really, you know, we viewed this from the government's perspective is how do we build again the secure platform where we can enable the research, but we don't really want to dictate the research. I mean, the one criteria we did put your research has to be covid focused because very clearly in response to covid, so you have to have a Covid focus and then we have data use agreements, data use request. You know, we have entire governance committees that decide is this research in scope, but we don't want to dictate the research types that the domain teams are bringing to the table. >>And I think the National Institutes of Health, you think about just that their mission is to serve the public health. And I think this is a great example of when you enable data to be surfaced and available that you can really allow people to be empowered and not to use the cliche citizen analysts. But in a way this is what the community is doing. You're doing research and allowing people from volunteers to academics to students to just be part of it. That is citizen analysis that you got citizen journalism. You've got citizen and uh, research, you've got a lot of democratization happening here. Is that part of it was a result of >>this? Uh, it's both. It's a great question. I think it's both. And it's it's really by design because again, we want to enable and there's a couple of things that I really, you know, we we clamor with at end cats. I think NIH is going with this direction to is we believe firmly in open science, we believe firmly in open standards and how we can actually enable these standards to promote this open science because it's actually nontrivial. We've had, you know, the citizen scientists actually on the tricky problem from a governance perspective or we have the case where we actually had to have students that wanted access to the environment. Well, we actually had to have someone because, you know, they have to have an institution that they come in with, but we've actually across some of those bridges to actually get students and researchers into this environment very much by design, but also the spirit which was held enabled by the community, which, again, so I think they go they go hand in hand. I planned for >>open science as a huge wave, I'm a big fan, I think that's got a lot of headroom because open source, what that's done to software, the software industry, it's amazing. And I think your Federated idea comes in here and Ben if you guys can just talk through the Federated, because I think that might enable and remove some of the structural blockers that might be out there in terms of, oh, you gotta be affiliate with this or that our friends got to invite you, but then you got privacy access and this Federated ID not an easy thing, it's easy to say. But how do you tie that together? Because you want to enable frictionless ability to come in and contribute same time you want to have some policies around who's in and who's not. >>Yes, totally, I mean so Sam sort of already described the the UNa system which is the authentication system that encounters has developed. And obviously you know from our perspective, you know we integrate with that is using all of the standard kind of authentication protocols and it's very easy to integrate that into the family platform um and make it so that we can authenticate people correctly. But then if you go beyond authentication you also then to actually you need to have the access controls in place to say yes I know who this person is, but now what should they actually be able to see? Um And I think one of the really great things in Free C has done is to be very rigorous about that. They have their governance rules that says you should be using the data for a certain purpose. You must go through a procedure so that the access committee approves that purpose. And then we need to make sure that you're actually doing the work that you said you were going to. And so before you can get your data back out of the system where your results out, you actually have to prove that those results are in line with the original stated purpose and the infrastructure around that and having the access controls and the governance processes, all working together in a seamless way so that it doesn't, as you say, increase the friction on the researcher and they can get access to the data for that appropriate purpose. That was a big component of what we've been building out with them three C. Absolutely. >>And really in line john with what NIH is doing with the research, all service, they call this raz. And I think things that we believe in their standards that were starting to follow and work with them closely. Multifactor authentication because of the point Ben is making and you raised as well, you know, one you need to authenticate, okay. This you are who you say you are. And and we're recognizing that and you're, you know, the author and peace within the authors. E what do you authorized to see? What do you have authorization to? And they go hand in hand and again, non trivial problems. And especially, you know, when we basis typically a lot of what we're using is is we'll do direct integrations with our package. We using commons for Federated access were also even using login dot gov. Um, you know, again because we need to make sure that people had a means, you know, and login dot gov is essentially a runoff right? If they don't have, you know an organization which we have in common or a Federated access to generate a login dot gov account but they still are whole, you know beholden to the multi factor authentication step and then they still have to get the same authorizations because we really do believe access to these environment seamlessly is absolutely critical, you know, who are users are but again not make it restrictive and not make it this this friction filled process. That's very that's very >>different. I mean you think about nontrivial, totally agree with you and if you think about like if you were in a classic enterprise, I thought about an I. T. Problem like bring your own device to work and that's basically what the whole world does these days. So like you're thinking about access, you don't know who's coming in, you don't know where they're coming in from, um when the churn is so high, you don't know, I mean all this is happening, right? So you have to be prepared two Provisions and provide resource to a very lightweight access edge. >>That's right. And that's why it gets back to what we mentioned is we were taking a step back and thinking about this problem, you know, an M three C became the use case was this is an enterprise I. T. Problem. Right. You know, we have users from around the world that want to access this environment and again we try to hit a really difficult mark, which is secure but collaborative, Right? That's that's not easy, you know? But but again, the only place this environment could take place isn't a cloud based environment, right? Let's be real. You know, 10 years ago. Forget it. You know, Again, maybe it would have been difficult, but now it's just incredible how much they advanced that these real virtual research organizations can start to exist and they become the real partnerships. >>Well, I want to Well, that's a great point. I want to highlight and call out because I've done a lot of these interviews with awards programs over the years and certainly in public sector and open source over many, many years. One of the things open source allows us the code re use and also when you start getting in these situations where, okay, you have a crisis covid other things happen, nonprofits go, that's the same thing. They, they lose their funding and all the code disappears. Saying with these covid when it becomes over, you don't want to lose the momentum. So this whole idea of re use this platform is aged deplatforming of and re factoring if you will, these are two concepts with a cloud enables SAM, I'd love to get your thoughts on this because it doesn't go away when Covid's >>over, research still >>continues. So this whole idea of re platform NG and then re factoring is very much a new concept versus the old days of okay, projects over, move on to the next one. >>No, you're absolutely right. And I think what first drove us is we're taking a step back and and cats, you know, how do we ensure that sustainability? Right, Because my background is actually engineering. So I think about, you know, you want to build things to last and what you just described, johN is that, you know, that, that funding, it peaks, it goes up and then it wanes away and it goes and what you're left with essentially is nothing, you know, it's okay you did this investment in a body of work and it goes away. And really, I think what we're really building are these sustainable platforms that we will actually grow and evolve based upon the research needs over time. And I think that was really a huge investment that both, you know, again and and Cats is made. But NIH is going in a very similar direction. There's a substantial investment, um, you know, made in these, these these these really impressive environments. How do we make sure the sustainable for the long term? You know, again, we just went through this with Covid, but what's gonna come next? You know, one of the research questions that we need to answer, but also open source is an incredibly important piece of this. I think Ben can speak this in a second, all the harmonization work, all that effort, you know, essentially this massive, complex GTL process Is in the N three Seagate hub. So we believe, you know, completely and the open source model a little bit of a flavor on it too though, because, you know, again, back to the sustainability, john, I believe, you know, there's a room for this, this marriage between commercial platforms and open source software and we need both. You know, as we're strong proponents of N cats are both, but especially with sustainability, especially I think Enterprise I. T. You know, you have to have professional grade products that was part of, I would say an experiment we ran out and cast our thought was we can fund academic groups and we can have them do open source projects and you'll get some decent results. But I think the nature of it and the nature of these environments become so complex. The experiment we're taking is we're going to provide commercial grade tools For the academic community and the researchers and let them use them and see how they can be enabled and actually focus on research questions. And I think, you know, N3C, which we've been very successful with that model while still really adhering to the open source spirit and >>principles as an amazing story, congratulated, you know what? That's so awesome because that's the future. And I think you're onto something huge. Great point, Ben, you want to chime in on this whole sustainability because the public private partnership idea is the now the new model innovation formula is about open and collaborative. What's your thoughts? >>Absolutely. And I mean, we uh, volunteer have been huge proponents of reproducibility and openness, um in analyses and in science. And so everything done within the family platform is done in open source languages like python and R. And sequel, um and is exposed via open A. P. I. S and through get repository. So that as SaM says, we've we've pushed all of that E. T. L. Code that was developed within the platform out to the cats get hub. Um and the analysis code itself being written in those various different languages can also sort of easily be pulled out um and made available for other researchers in the future. And I think what we've also seen is that within the data enclave there's been an enormous amount of re use across the different research projects. And so actually having that security in place and making it secure so that people can actually start to share with each other securely as well. And and and be very clear that although I'm sharing this, it's still within the range of the government's requirements has meant that the, the research has really been accelerated because people have been able to build and stand on the shoulders of what earlier projects have done. >>Okay. Ben. Great stuff. 1000 researchers. Open source code and get a job. Where do I sign up? I want to get involved. This is amazing. Like it sounds like a great party. >>We'll send you a link if you do a search on on N three C, you know, do do a search on that and you'll actually will come up with a website hosted by the academic side and I'll show you all the information of how you can actually connect and john you're welcome to come in. Billion by all means >>billions of rows of data being solved. Great tech he's working on again. This is a great example of large scale the modern era of solving problems is here. It's out in the open, Open Science. Sam. Congratulations on your great success. Ben Award winners. You guys doing a great job. Great story. Thanks for sharing here with us in the queue. Appreciate it. >>Thank you, john. >>Thanks for having us. >>Okay. It is. Global public sector partner rewards best Covid solution palantir and and cats. Great solution. Great story. I'm john Kerry with the cube. Thanks for watching. Mm mm. >>Mhm

(digital music) >> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> The storage business as we know it has changed forever. On-prem storage was once a virtually unlimited and untapped bastion of innovation, VC funding and lucrative exits. Today it's a shadow of its former self and the glory days of storage will not return. Hello everyone, and welcome to this week's Wikibon CUBE Insights Powered by ETR. In this breaking analysis, we'll lay out our premise for what's happening in the storage industry, and share some fresh insights from our ETR partners, and data that supports our thinking. We've had three decades of tectonic shifts in the storage business. From the simplified history of this industry shows us there've been five major waves of innovation spanning five decades. The dominant industry model has evolved from what was first the mainframe centric vertically integrated business, but of course by IBM and it became a disintegrated business that saw between like 70 or 80 Winchester disk drive companies that rose and then fell. They served a booming PC industry in this way it was led by the likes of Seagate. Now Seagate supplied the emergence of an intelligent controller based external disc array business that drove huge margins for functions that while lucrative was far cheaper than captive storage from system vendors, this era of course was led by EMC and NetApp. And then this business was disrupted by a flash and software defined model that was led by Pure Storage and also VMware. Now the future of storage is being defined by cloud and intelligent data management is being led by AWS and a three letter company that we'll just call TBD, otherwise known as Jump Ball Incorporated. Now, let's get into it here, the impact of AWS cannot be overstated now while legacy storage players, they're sick and tired of talking about the cloud, the reality cannot be ignored. The cloud has been the most disruptive force in storage over the past 10 years, and we've reported on the spending impact extensively. But cloud is not the only factor pressuring the on-prem storage business, flash has killed what we call performance by spindles. In other words, the practice of adding more disk drives to keep performance from tanking. So much flash has been injected into the data center that that no longer is required. But now as you drill down into the cloud, AWS has been by far the most significant factor in our view. Lots of people talked about object storage before AWS, but there sure wasn't much spending going on, S3 changed that. AWS is getting much more aggressive about expanding its storage portfolio and its offerings. S3 came out in 2006 and it was the very first AWS service and then Elastic Block Service EBS came out a couple of years later, nobody really paid much attention. Well last fall at storage day, we saw AWS announce a number of services, many fire-related and this year we saw four new announcements of Amazon at re:Invent. We think AWS' storage revenue will surpass 8 billion this year and could be as high as 10 billion. There's not much data out there, but this would mean that AWS' storage biz is larger than that of a NetApp, which means AWS is larger than every traditional storage player with the exception of Dell. Here's a little glimpse of what's coming at the legacy storage business. It's a clip of the vice-president of AWS storage, her name is Mahlon Thompson Bukovec, watch this. Okay now, you may say Dave, what the heck does that have to do with anything? Yeah, I don't know, but as an older white guy, that's been in this business for awhile, I just think it's badass that this woman boxes and runs a business that we think is approaching $10 billion. Now let's take a quick look at the storage announcements AWS made at re:Invent. The company made four announcements this year, let me try to be brief, the first is EBS io2 Block Express Volumes, got to love the names. AWS was claims this is the first storage area network or sand for the cloud and it offers up to 256,000 IOPS and 4,000 megabytes per second throughput and 64 terabytes of capacity. Hey, sounds pretty impressive right, Well let's dig in a little bit okay, first of all, this is not the first sand in the cloud, at least in my view there may be others but Pure Storage announced cloud block store in 2019 at its annual accelerate customer conference and it's pretty comparable here. Maybe not so much in the speeds and feeds, but the concept of better block storage in the cloud with higher availability. Now, as you may also be saying, what's the big deal? The performance come on, we can smoke that we're on-prem vendor We can bury that. Compared to what we do, AWS' announcement is really not that impressive okay, let me give you a point of comparison there's a startup out there called VAST Data. Just there for you and closure with bundled storage and compute can do 400,000 IOPS and 40,000 megabytes per second and that can be scaled, so yeah, I get it. And AWS also announced that io2 two was priced at 20% less than previous generation volumes, which you might say is also no big deal and I would agree 20% is not as aggressive as the average price decline per gigabyte of any storage technology. AWS loves to make a big deal about its price declines, it's essentially following the industry trends but the point is that this feature will be great for a lot of workloads and it's fully integrated with AWS services meaning for example, it will be very convenient for AWS customers to invoke this capability for example Aurora and other AWS databases through its RDS service, just another easy button for developers to push. This is specially important as we see AWS rapidly expanding its machine learning in AI capabilities with SageMaker, it's embedding ML into things like Redshift and driving analytics, so integration is very key for its customers. Now, is Amazon retail going to run its business on io2 volumes? I doubt it. I believe they're running on Oracle and they need much better performance, but this is a mainstream service for the EBS masses to tap. Now, the other notable announcement was EBS Gp3 volumes. This is essentially a service that lets let you programmatically set SLAs for IOPS and throughput independently without needing to add additional storage. Again, you may be saying things like, well atleast I remember when SolidFire let me do this several years ago and gave me more than 3000 IOPS and 125 megabytes per a second performance, but look, this is great for mainstream customers that want more consistent and predictable performance and that want to set some kind of threshold or floor and it's integrated again into the AWS stack. Two other announcements were made, one that automatically tiers data to colder storage tiers and a replication service. On the former, data migrates to tier two after 90 days of inaccess and tier three, after 180 days. AWS remember, they hired a bunch of folks out of EMC years ago and they put them up in the Boston Seaport area, so they've acquired lots of expertise in a lot of different areas I'm not sure if tiering came out of that group but look, this stuff is not rocket science, but it saves customers money. So these are tried and true techniques that AWS is applying but the important thing is it's in the cloud. Now for sure we'd like to see more policy options than say for example, a fixed 90 day or 180 day policy and more importantly we'd like to see intelligent tiering where the machine is smart enough to elevate and promote certain datasets when they're needed for instance, at the end of a quarter for comparison purposes or at the end of the year, but as NFL Hall of Fame Coach Hank Stram would have said, AWS is matriculating the ball down the field. Okay, let's look at some of the data that supports what we're saying here in our premise today. This chart shows spending across the ETR taxonomy. It depicts the net score or spending velocity for different sectors. We've highlighted storage, now don't put too much weight on the January data because the survey was just launched, but you can see storage continues to be a back burner item relative to some other spending priorities. Now as I've reported, CIOs are really focused on cloud, containers, container orchestration, automation, productivity and other key areas like security. Now let's take a look at some of the financial data from the storage crowd. This chart shows data for eight leading names in storage and we put storage in quotes because as we said earlier, the market is shifting and for sure companies like Cohesity and Rubrik, they're not positioning as storage players in fact, that's the last thing they want to do. Rather they're category creators around data management or intelligent data management but their inadjacency to storage, they're partnering with all the primary storage companies and they're in the ETR taxonomy. Okay, so as you can see, we're showing the year over year, quarterly revenue growth for the leading storage companies. NetApp is a big winner, they're growing at a whopping 2%. They beat expectations, but expectations were way down so you can see in the right most column upper right, we've added the ETR net score from October and net score of 10% says that if you ask customers, are you spending more or less with a company, there are 10% of the customers that are essentially spending more than are spending less, get into that a little further later. For comparison, a company like Snowflake, it has a net score approaching 70% Pure Storage used to be that high several years ago or high sixties anyway. So 10% is in the red zone and yet NetApp, is the big winner this quarter. Now Nutanix isn't really again a storage company, but they're an adjacency and they sell storage and like many of these companies, it's transitioning to a subscription pricing model, so that puts pressure on the income statement, that's why they went out and did a deal with Bain, Bain put in $750 million to help Bridge that transition so that's kind of an interesting move. Every company in this chart is moving to an annual recurring revenue model and that as a service approach is going to be the norm by the end of the decade. HPE's doing it with GreenLake, Dell has announced Apex, virtually every company is headed in this direction. Now speaking of HPE, it's Nimble business that has momentum, but other parts of the storage portfolio are quite a bit softer. Dell continues to see pressure on its storage business although VxRail is a bright spot. Everybody's got a bright spot, everybody's got new stuff that's growing much faster than the old stuff, the problem is the old stuff is much much bigger than the new stuff. IBM's mainframe storage cycle, well that's seems to have run its course, they had been growing for the last several quarters that looks like it's over. And so very very cyclical businesses here now as you can see, The data protection data management companies, they are showing spending momentum but they're not public so we don't have revenue data. But you got to wonder with all the money these guys have raised and the red hot IPO and tech markets, why haven't these guys gone public? The answer has to be that they're either not ready or maybe their a numbers weren't where they want them to be, maybe they're not predictable enough, maybe they don't have their operational act together or maybe they need to you get that in order, some combination of those factors is likely. They'll tell you, they'll give other answers if you ask them, but if they had their stuff together they'd be going out right now. Now here's another look at the spending data in terms of net score, which is again spending velocity. The ETR here is measuring the percent of respondents that are adopting new, spending more, spending flat, spending less or retiring the platform. So net score is adoptions, which is the lime green plus the spending more, which is the forest green. Add those two and then subtract spending less, which is the pink and then leaving the platform, which is the bright red, what's left over is net score. So, let's look at the picture here, Cohesity leads all players in the storage taxonomy, the ETR storage taxonomy, again they don't position that way, but that's the way the customers are answering. They've got 55% net score which is really solid and you can see the data in the upper right-hand corner, it's followed by Nutanix. Now they're really not again in the scope of Pure play storage play but speaking of Pure, its net score has come down from its high of 73% in January, 2016. It's not going to climb back up there, but it's going to be interesting to see if Pure net scorecard rebound in a post COVID world. We're also watching what Pure does in terms of unifying file and object and how it's fairing in cloud and what it does with the Portworx acquisition which is really designed to bring forth a new programming model. Now, Dell is doing fine with VxRail, but VSAN is well off its net score highs which we're in the 60% plus range a couple of years ago, VSAN is definitely been a factor from VMware, but again that's come off its highs, HPE with Nimble still has some room to improve, I think it actually will I think that these figures that we're showing here they're are somewhat depressed by the COVID factor, I expect Nimble is going to bounce back in future surveys. Dell and NetApp are the big leaders in terms of presence or market share in the data other than VMware, 'cause VMware has a lot of instances, it's software defined that's why they're so prominent. And with VMware's large share you'd expect them to have net scores that are tepid and you can see a similar pattern with IBM. So Dell, NetApp, tepid net scores as is IBM because of their large market share VMware, kind of a newer entry into the play and so doing pretty well there from a net score standpoint. Now Commvault like Cohesity and Rubrik is really around intelligent data management, trying to go beyond backup into business recovery, data protection, DevOps, bringing that analytics, bringing that to the cloud, we didn't put Veeam in here and we probably should have. They had pre-COVID net scores well in to the thirties and they have a steadily increasing share of the market, so we expect good things from Veeam going forward. They were acquired earlier this year by Insight, capital private equity firm. So big changes there as well, that was their kind of near-term exit maybe more to come. But look, it's all relative, this is a large and mature market that is moving to the cloud and moving to other adjacencies. And the core is still primary storage, that's the main supreme prerequisite and everything else flows from there, data protection, replication, everything else. This chart gives you another view of the competitive landscape, it's that classic XY chart it plots net score in the vertical axis and market share on the horizontal axis, market share remember is a measure of presence in the dataset. Now think about this from the CIO's perspective, they have their on-prem estate, got all this infrastructure and they're putting a brick wall around their core systems. And what do they want out of storage for that class of workload? They want it to perform consistently, they want it to be efficient and they want it to be cost-effective, so what are they going to do? they're going to consolidate, They're going to consolidate the number of vendors, they're going to consolidate the storage, they're going to minimize complexity, yeah, they're going to worry about the blast radius, but there's ways to architect around that. The last thing they want to worry about is managing a zillion storage vendors this business is consolidating, it has been for some time, we've seen the number of independent storage players that are going public as consolidated over the years, and it's going to continue. so on-prem storage arrays are not giving CIOs the innovation and strategic advantage back when things like storage virtualization, space efficient snapshots, data de-duplication and other storage services were worth maybe taking a flyer on a feature product like for example, a 3PAR or even a Data Domain. Now flash gave the CIOs more headroom and better performance and so as I said earlier, they're not just buying spindles to increase performance, so as more and more work gets pushed to the cloud, you're seeing a bunkering in on these large scale mission-critical workloads. As you saw earlier, the legacy storage market is consolidating and has been for a while as I just said, it's essentially becoming a managed decline business where RnD is going to increasingly get squeezed and go to other areas, both from the vendor community and on the buy-side where they're investing on things like cloud, containers and in building new layers in their business and of course the DX, the Digital Transformation. I mentioned VAST Data before, it is a company that's growing and another company that's growing is Infinidat and these guys are traditional storage on-prem models they don't bristle If I say traditional they're nexgen if you will but they don't own a cloud, so they were selling to the data center. Now Infinidat is focused on petabyte scale and as they say, they're growing revenues, they're having success consolidating storage that thing that I just talked about. Ironically, these are two Israeli founder based companies that are growing and you saw earlier, this is a share shift the market is not growing overall the part of that's COVID, but if you exclude cloud, the market is under pressure. Now these two companies that I'm mentioning, they're kind of the exception to the rule here, they're tiny in the grand scheme of things, they're really not going to shift the market and their end game is to get acquired so they can still share, but they're not going to reverse these trends. And every one on this chart, every on-prem player has to have a cloud strategy where they connect into the cloud, where they take advantage of native cloud services and they help extend their respective install bases into the cloud, including having a capability that is physically proximate to the cloud with a colo like an Equinix or some other approach. Now, for example at re:Invent, we saw that AWS has hybrid strategy, we saw that evolving. AWS is trying to bring AWS to the edge and they treat the data center as just another edge note, so outposts and smaller versions of outposts and things like local zones are all part of bringing AWS to the edge. And we saw a few companies Pure, Infinidant, Veeam come to mind that are connecting to outpost. They saw the Qumulo was in there, Clumio, Commvault, WekaIO is also in there and I'm sure I'm missing some so, DM me, email me, yell at me, I'm sorry I forgot you but you get the point. These companies that are selling on-prem are connecting to the cloud, they're forced to connect to the cloud much in the same way as they were forced to join the VMware ecosystem and try to add value, try to keep moving fast. So, that's what's going on here, what's the prognosis for storage in the coming year? Well, where've of all the good times gone? Look, we would never bet against data but the days of selling storage controllers that masks the deficiencies of spinning disc or add embedded hardware functions or easily picking off a legacy install base with flash, well, those days are gone. Repatriation, it ain't happening it's maybe tiny little pockets. CIOs are rationalizing their on-premises portfolios so they can invest in the cloud, AI, machine learning, machine intelligence, automation and they're re-skilling their teams. Low latency high bandwidth workloads with minimal jitter, that's the sweet spot for on-prem it's becoming the mainframe of storage. CIOs are also developing a cloud first strategy yes, the world is hybrid but what does that mean to CIOs? It means you're going to have some work in the cloud and some work on-prem, there's a hybrid We've got both. Everything that can go to the cloud, will go to the cloud, in our opinion and everything that can't or shouldn't won't. Yes, people will make mistakes and they'll "repatriate" but generally that's the trend. And the CIOs they're building an abstraction layer to connect workloads from an observability and manageability standpoint so they can maintain control and manage lock-in risk, they have options. Everything that doesn't go to the cloud will likely have some type of hybridicity to it, the reverse won't likely be the case. For vendors, cloud strategies involve supporting your install basis migration to the cloud, that's where they're going, that's where they want to go, they want your help there's business to be made there so enabling low latency hybrids in accommodating subscription models, well, that's a whole another topic, but that's the trend that we see and you rethink the business that you're in, for instance, data management and developing an edge strategy that recognizes that edge workloads are going to require new architecture and that's more efficient than what we've seen built around general purpose systems, and wow, that's a topic for another day. You're seeing this whole as a service model really reshape the entire cultures in the way in which the on-prem vendors are operating no longer is it selling a box that has dramatically marked up controllers and disc drives, it's really thinking about services that could be invoked in the cloud. Now remember, these episodes are all available as podcasts, wherever you listen, just search Breaking Analysis podcasts and please subscribe, I'd appreciate that checkout etr.plus for all the survey action. We also publish a full report every week on wikibon.com and siliconangle.com. A lot of ways to get in touch. You can email me at david.vellante@siliconangle.com. you could DM me @dvellante on Twitter, comment on our LinkedIn posts, I always appreciate that. This is Dave Vellante for theCUBE Insights Powered by ETR. Thanks for watching everyone stay safe and we'll see you next time. (upbeat music)

>> Announcer: From around the globe, it's theCUBE. With digital coverage of VMworld 2020, brought to you by VMware and its ecosystem partners. >> Welcome back, I'm Stu Miniman. This is theCUBE's coverage of VMworld 2020 of course, happening virtually. And there are certain people that we talk to every year at theCUBE, and this guest, I believe, has been on theCUBE at VMworld more than any others. It's actually not Pat Gelsinger, Eric Herzog. He is the chief marketing officer and vice president of global storage channels at IBM. Eric, Mr. Zoginstor, welcome back to theCUBE, nice to see you. >> Thank you very much, Stu. IBM always enjoys hanging with you, John, and Dave. And again, glad to be here, although not in person this time at VMworld 2020 virtual. Thanks again for having IBM. >> Alright, so, you know, some things are the same, others, very different. Of course, Eric, IBM, a long, long partner of VMware's. Why don't you set up for us a little bit, you know, 2020, the major engagements, what's new with IBM and VMware? >> So, a couple of things, first of all, we have made our Spectrum Virtualize software, software defined block storage work in virtual machines, both in AWS and IBM Cloud. So we started with IBM Cloud and then earlier this year with AWS. So now we have two different cloud platforms where our Spectrum Virtualize software sits in a VM at the cloud provider. The other thing we've done, of course, is V7 support. In fact, I've done several VMUGs. And in fact, my session at VMworld is going to talk about both our support for V7 but also what we're doing with containers, CSI, Kubernetes overall, and how we can support that in a virtual VMware environment, and also we're doing with traditional ESX and VMware configurations as well. And of course, out to the cloud, as I just talked about. >> Yeah, that discussion of hybrid cloud, Eric, is one that we've been hearing from IBM for a long time. And VMware has had that message, but their cloud solutions have really matured. They've got a whole group going deep on cloud native. The Amazon solutions have been something that they've been partnering, making sure that, you know, data protection, it can span between, you know, the traditional data center environment where VMware is so dominant, and the public clouds. You're giving a session on some of those hybrid cloud solutions, so share with us a little bit, you know, where do the visions completely agree? What's some of the differences between what IBM is doing and maybe what people are hearing from VMware? >> Well, first of all, our solutions don't always require VMware to be installed. So for example, if you're doing it in a container environment, for example, with Red Hat OpenShift, that works slightly different. Not that you can't run Red Hat products inside of a virtual machine, which you can, but in this case, I'm talking Red Hat native. We also of course do VMware native and support what VMware has announced with their Kubernetes based solutions that they've been talking about since VMworld last year, obviously when Pat made some big announcements onstage about what they were doing in the container space. So we've been following that along as well. So from that perspective, we have agreement on a virtual machine perspective and of course, what VMware is doing with the container space. But then also a slightly different one when we're doing Red Hat OpenShift as a native configuration, without having a virtual machine involved in that configuration. So those are both the commonalities and the differences that we're doing with VMware in a hybrid cloud configuration. >> Yeah. Eric, you and I both have some of those scars from making sure that storage works in a virtual environment. It took us about a decade to get things to really work at the VM level. Containers, it's been about five years, it feels like we've made faster progress to make sure that we can have stateful environments, we can tie up with storage, but give us a little bit of a look back as to what we've learned and how we've made sure that containerized, Kubernetes environments, you know, work well with storage for customers today. >> Well, I think there's a couple of things. First of all, I think all the storage vendors learn from VMware. And then the expansion of virtual environments beyond VMware to other virtual environments as well. So I think all the storage vendors, including IBM learned through that process, okay, when the next thing comes, which of course in this case happens to be containers, both in a VMware environment, but in an open environment with the Kubernetes management framework, that you need to be able to support it. So for example, we have done several different things. We support persistent volumes in file block and object store. And we started with that almost three years ago on the block side, then we added the file side and now the object storage side. We also can back up data that's in those containers, which is an important feature, right? I am sitting there and I've got data now and persistent volume, but I got to back it up as well. So we've announced support for container based backup either with Red Hat OpenShift or in a generic Kubernetes environment, because we're realistic at IBM. We know that you have to exist in the software infrastructure milieu, and that includes VMware and competitors of VMware. It includes Red Hat OpenShift, but also competitors to Red Hat. And we've made sure that we support whatever the end user needs. So if they're going with Red Hat, great. If they're going with a generic container environment, great. If they're going to use VMware's container solutions, great. And on the virtualization engines, the same thing. We started with VMware, but also have added other virtualization engines. So you think the storage community as a whole and IBM in particular has learned, we need to be ready day one. And like I said, three years ago, we already had persistent volume support for block store. It's still the dominant storage and we had that three years ago. So for us, that would be really, I guess, two years from what you've talked about when containers started to take off. And within two years we had something going that was working at the end user level. Our sales team could sell our business partners. As you know, many of the business partners are really rallying around containers, whether it be Red Hat or in what I'll call a more generic environment as well. They're seeing the forest through the trees. I do think when you look at it from an end user perspective, though, you're going to see all three. So, particularly in the Global Fortune 1000, you're going to see Red Hat environments, generic Kubernetes environments, VMware environments, just like you often see in some instances, heterogeneous virtualization environments, and you're still going to see bare metal. So I think it's going to vary by application workload and use case. And I think all, I'd say midsize enterprise up, let's say, $5 billion company and up, probably will have at least two, if not all three of those environments, container, virtual machine, and bare metal. So we need to make sure that at IBM we support all those environments to keep those customers happy. >> Yeah, well, Eric, I think anybody, everybody in the industry knows, IBM can span those environments, you know, support through generations. And very much knows that everything in IT tends to be additive. You mentioned customers, Eric, you talk to a lot of customers. So bring us inside, give us a couple examples if you would, how are they dealing with this transition? For years we've been talking about, you know, enabling developers, having them be tied more tightly with what the enterprise is doing. So what are you seeing from some of your customers today? >> Well, I think the key thing is they'd like to use data reuse. So, in this case, think of a backup, a snap or replica dataset, which is real world data, and being able to use that and reuse that. And now the storage guys want to make sure they know who's, if you will, checked it out. We do that with our Spectrum Copy Data Management. You also have, of course, integration with the Ansible framework, which IBM supports, in fact, we'll be announcing some additional support for more features in Ansible coming at the end of October. We'll be doing a large launch, very heavily on containers. Containers and primary storage, containers in hybrid cloud environments, containers in big data and AI environments, and containers in the modern data protection and cyber resiliency space as well. So we'll be talking about some additional support in this case about Ansible as well. So you want to make sure, one of the key things, I think, if you're a storage guy, if I'm the VP of infrastructure, or I'm the CIO, even if I'm not a storage person, in fact, if you think about it, I'm almost 70 now. I have never, ever, ever, ever met a CIO who used to be a storage guy, ever. Whether I, I've been with big companies, I was at EMC, I was at Seagate Maxtor, I've been at IBM actually twice. I've also done seven startups, as you guys know at theCUBE. I have never, ever met a CIO who used to be a storage person. Ever, in all those years. So, what appeals to them is, how do I let the dev guys and the test guys use that storage? At the same time, they're smart enough to know that the software guys and the test guys could actually screw up the storage, lose the data, or if they don't lose the data, cost them hundreds of thousands to millions of dollars because they did something wrong and they have to reconfigure all the storage solutions. So you want to make sure that the CIO is comfortable, that the dev and the test teams can use that storage properly. It's a part of what Ansible's about. You want to make sure that you've got tight integration. So for example, we announced a container native version of our Spectrum Discover software, which gives you comprehensive metadata, cataloging and indexing. Not only for IBM's scale-out file, Spectrum Scale, not only for IBM object storage, IBM cloud object storage, but also for Amazon S3 and also for NetApp filers and also for EMC Isilon. And it's a container native. So you want to make sure in that case, we have an API. So the AI software guys, or the big data software guys could interface with that API to Spectrum Discover, let them do all the work. And we're talking about a piece of software that can traverse billions of objects in two seconds, billions of them. And is ideal to use in solutions that are hundreds of petabytes, up into multiple exabytes. So it's a great way that by having that API where the CIO is confident that the software guys can use the API, not mess up the storage because you know, the storage guys and the data scientists can configure Spectrum Discover and then save it as templates and run an AI workload every Monday, and then run a big data workload every Tuesday, and then Wednesday run a different AI workload and Thursday run a different big data. And so once they've set that up, everything is automated. And CIOs love automation, and they really are sensitive. Although they're all software guys, they are sensitive to software guys messing up the storage 'cause it could cost them money, right? So that's their concern. We make it easy. >> Absolutely, Eric, you know, it'd be lovely to say that storage is just invisible, I don't need to think about it, but when something goes wrong, you need those experts to be able to dig in. You spent some time talking about automation, so critically important. How about the management layer? You know, you think back, for years it was, vCenter would be the place that everything can plug in. You could have more generalists using it. The HCI waves were people kind of getting away from being storage specialists. Today VMware has, of course vCenter's their main estate, but they have Tanzu. On the IBM and Red Hat side, you know, this year you announced the Advanced Cluster Management. What's that management landscape look like? How does the storage get away from managing some of the bits and bytes and, you know, just embrace more of that automation that you talked about? >> So in the case of IBM, we make sure we can support both. We need to appeal to the storage nerd, the storage geek if you will. The same time to a more generalist environment, whether it be an infrastructure manager, whether it be some of the software guys. So for example, we support, obviously vCenter. We're going to be supporting all of the elements that are going to happen in a container environment that VMware is doing. We have hot integration and big time integration with Red Hat's management framework, both with Ansible, but also in the container space as well. We're announcing some things that are coming again at the end of October in the container space about how we interface with the Red Hat management schema. And so you don't always have to have the storage expert manage the storage. You can have the Red Hat administrator, or in some cases, the DevOps guys do it. So we're making sure that we can cover both sides of the fence. Some companies, this just my personal belief, that as containers become commonplace while the software guys are going to want to still control it, there eventually will be a Red Hat/container admin, just like all the big companies today have VMware admins. They all do. Or virtualization admins that cover VMware and VMware's competitors such as Hyper-V. They have specialized admins to run that. And you would argue, VMware is very easy to use, why aren't the software guys playing with it? 'Cause guess what? Those VMs are sitting on servers containing both apps and data. And if the software guy comes in to do something, messes it up, so what have of the big entities done? They've created basically a virtualization admin layer. I think that over time, either the virtualization admins become virtualization/container admins, or if it's a big enough for both estates, there'll be container admins at the Global Fortune 500, and they'll also be virtualization admins. And then the software guys, the devOps guys will interface with that. There will always be a level of management framework. Which is why we integrate, for example, with vCenter, what we're doing with Red Hat, what we do with generic Kubernetes, to make sure that we can integrate there. So we'll make sure that we cover all areas because a number of our customers are very large, but some of our customers are very small. In fact, we have a company that's in the software development space for autonomous driving. They have over a hundred petabytes of IBM Spectrum Scale in a container environment. So that's a small company that's gone all containers, at the same time, we have a bunch of course, Global Fortune 1000s where IBM plays exceedingly well that have our products. And they've got some stuff sitting in VMware, some such sitting in generic Kubernetes, some stuff sitting in Red Hat OpenShift and some stuff still in bare metal. And in some cases they don't want their software people to touch it, in other cases, these big accounts, they want their software people empowered. So we're going to make sure we could support both and both management frameworks. Traditional storage management framework with each one of our products and also management frameworks for virtualization, which we've already been doing. And now management frame first with container. We'll make sure we can cover all three of those bases 'cause that's what the big entities will want. And then in the smaller names, you'll have to see who wins out. I mean, they may still use three in a small company, you really don't know, so you want to make sure you've got everything covered. And it's very easy for us to do this integration because of things we've already historically done, particularly with the virtualization environment. So yes, the interstices of the integration are different, but we know here's kind of the process to do the interconnectivity between a storage management framework and a generic management framework, in, originally of course, vCenter, and now doing it for the container world as well. So at least we've learned best practices and now we're just tweaking those best practices in the difference between a container world and a virtualization world. >> Eric, VMworld is one of the biggest times of the year, where we all get together. I know how busy you are going to the show, meeting with customers, meeting with partners, you know, walking the hallways. You're one of the people that traveled more than I did pre-COVID. You know, you're always at the partner shows and meeting with people. Give us a little insight as to how you're making sure that, partners and customers, those conversations are still happening. We understand everything over video can be a little bit challenging, but, what are you seeing here in 2020? How's everybody doing? >> Well, so, a couple of things. First of all, I already did two partner meetings today. (laughs) And I have an end user meeting, two end user meetings tomorrow. So what we've done at IBM is make sure we do a couple things. One, short and to the point, okay? We have automated tools to actually show, drawing, just like the infamous walk up to the whiteboard in a face to face meeting, we've got that. We've also now tried to make sure everybody is being overly inundated with WebEx. And by the way, there's already a lot of WebEx anyway. I can think of meeting I had with a telco, one of the Fortune 300, and this was actually right before Thanksgiving. I was in their office in San Jose, but they had guys in Texas and guys in the East Coast all on. So we're still over WebEx, but it also was a two and a half hour meeting, actually almost a three hour meeting. And both myself and our Flash CTO went up to the whiteboard, which you could then see over WebEx 'cause they had a camera showing up onto the whiteboard. So now you have to take that and use integrated tools. One, but since people are now, I would argue, over WebEx. There is a different feel to doing the WebEx than when you're doing it face to face. We have to fly somewhere, or they have to fly somewhere. We have to even drive somewhere, so in between meetings, if you're going to do four customer calls, Stu, as you know, I travel all over the world. So I was in Sweden actually right before COVID. And in one day, the day after we had a launch, we launched our new Flash System products in February on the 11th, on February 12th, I was still in Stockholm and I had two partner meetings and two end user meetings. But the sales guy was driving me around. So in between the meetings, you'd be in the car for 20 minutes or half an hour. So it connects different when you can do WebEx after WebEx after WebEx with basically no break. So you have to be sensitive to that when you're talking to your partners, sensitive of that when you're talking to the customers sensitive when you're talking to the analysts, such as you guys, sensitive when you're talking to the press and all your various constituents. So we've been doing that at IBM, really, since the COVID thing got started, is coming up with some best practices so we don't overtax the end users and overtax our channel partners. >> Yeah, Eric, the joke I had on that is we're all following the Bill Belichick model now, no days off, just meeting, meeting, meeting every day, you can stack them up, right? You used to enjoy those downtimes in between where you could catch up on a call, do some things. I had to carve out some time to make sure that stack of books that normally I would read in the airports or on flights, everything, you know. I do enjoy reading a book every now and again, so. Final thing, I guess, Eric. Here at VMworld 2020, you know, give us final takeaways that you want your customers to have when it comes to IBM and VMware. >> So a couple of things, A, we were tightly integrated and have been tightly integrated for what they've been doing in their traditional virtualization environment. As they move to containers we'll be tightly integrated with them as well, as well as other container platforms, not just from IBM with Red Hat, but again, generic Kubernetes environments with open source container configurations that don't use IBM Red Hat and don't use VMware. So we want to make sure that we span that. In traditional VMware environments, like with Version 7 that came out, we make sure we support it. In fact, VMware just announced support for NVMe over Fibre Channel. Well, we've been shipping NVMe over Fibre Channel for just under two years now. It'll be almost two years, well, it will be two years in October. So we're sitting here in September, it's almost been two years since we've been shipping that. But they haven't supported it, so now of course we actually, as part of our launch, I pre say something, as part of our launch, the last week of October at IBM's TechU it'll be on October 27th, you can join for free. You don't need to attend TechU, we'll have a free registration page. So just follow Zoginstor or look at my LinkedIns 'cause I'll be posting shortly when we have the link, but we'll be talking about things that we're doing around V7, with support for VMware's announcement of NVMe over Fibre Channel, even though we've had it for two years coming next month. But they're announcing support, so we're doing that as well. So all of those sort of checkbox items, we'll continue to do as they push forward into the container world. IBM will be there right with them as well because we know it's a very large world and we need to support everybody. We support VMware. We supported their competitors in the virtualization space 'cause some customers have, in fact, some customers have both. They've got VMware and maybe one other of the virtualization elements. Usually VMware is the dominant of course, but if they've got even a little bit of it, we need to make sure our storage works with it. We're going to do the same thing in the container world. So we will continue to push forward with VMware. It's a tight relationship, not just with IBM Storage, but with the server group, clearly with the cloud team. So we need to make sure that IBM as a company stays very close to VMware, as well as, obviously, what we're doing with Red Hat. And IBM Storage makes sure we will do both. I like to say that IBM Storage is a Switzerland of the storage industry. We work with everyone. We work with all these infrastructure players from the software world. And even with our competitors, our Spectrum Virtualized software that comes on our Flash Systems Array supports over 550 different storage arrays that are not IBM's. Delivering enterprise-class data services, such as snapshot, replication data, at rest encryption, migration, all those features, but you can buy the software and use it with our competitors' storage array. So at IBM we've made a practice of making sure that we're very inclusive with our software business across the whole company and in storage in particular with things like Spectrum Virtualize, with what we've done with our backup products, of course we backup everybody's stuff, not just ours. We're making sure we do the same thing in the virtualization environment. Particularly with VMware and where they're going into the container world and what we're doing with our own, obviously sister division, Red Hat, but even in a generic Kubernetes environment. Everyone's not going to buy Red Hat or VMware. There are people going to do Kubernetes industry standard, they're going to use that, if you will, open source container environment with Kubernetes on top and not use VMware and not use Red Hat. We're going to make sure if they do it, what I'll call generically, if they use Red Hat, if they use VMware or some combo, we will support all of it and that's very important for us at VMworld to make sure everyone is aware that while we may own Red Hat, we have a very strong, powerful connection to VMware and going to continue to do that in the future as well. >> Eric Herzog, thanks so much for joining us. Always a pleasure catching up with you. >> Thank you very much. We love being with theCUBE, you guys do great work at every show and one of these days I'll see you again and we'll have a beer. In person. >> Absolutely. So, definitely, Dave Vellante and John Furrier send their best, I'm Stu Miniman, and thank you as always for watching theCUBE. (relaxed electronic music)

>> Announcer: From around the globe, it's theCUBE! With digital coverage of Actifio Data Driven 2020. Brought to you by Actifio. >> We're back, This is theCUBE's coverage, our ongoing coverage of Actifio's Data Driven, of course we've gone virtual this year. Ash Ashutosh is here, he's the founder, president, and CEO of Actifio. Ash, great to see you again. >> Likewise, Dave, always, always good to see you. >> We were at a little meetup, you and I, in Boston, I always enjoy our conversations. Little did we know that a few months later, we'd only be talking at this type of distance, and of course, it's sad, I mean, Data Driven is one of our favorite events, it's intimate, it's customer content-driven. The theme this year is, you call it the next normal. Some people call it the new abnormal. The next normal, what's that all about? >> I think it's pretty fascinating to see, when we walked in in March, all of us were shocked by the effect of this pandemic. And for a while, we all scrambled around, trying to figure out, how do you react to this one? And everybody reacted very differently, but most people had this tendency to think that this is going to be a pretty brutal environment with lots of unknown variables, and it is important for us to try to figure out how to get our hands around this. By the time we came around about six weeks into that, almost all of us have figured out, this is not something you fight against, this is not something you wait for it to go away, but this is one that you figure out how to live it, and you figure out how to work around it. And that, we believe, is the next normal. It's not about trying to create a new abnormal, it's not about creating a new normal, but it's truly one that basically says "There is a path forward, there's a way to create this next normal," and you just figure out how to live with the environment we have, and phenomenal outcomes of companies that have done remarkably well, as a result of these actions, Actifio being one of them. >> It's quite amazing, isn't it, I mean, I've talked to a lot of tech companies, CEOs, and their customers, and it's almost like, the first reaction was of course they cared about their employees and their broader families. Number one, number two was, many companies, as you know, saw a tailwind, and initially didn't want to be seen as ambulance chasing, and then of course the entrepreneurial spirit kicked in, and they said, "Okay, hey, "we can only control what we can control." And tech companies in particular have just done exceedingly well. I mean, I don't think anybody really predicted that early on. >> Yeah. I think at the heart we are all human beings and the first reaction was to take care of, four constituencies, right? One, take care of your family, take care of your community, take care of your employees, take care of your customers. And that was the hardest part. The first four to six weeks was to figure out how do you do each of those four. Once you figured that part out, or you figured out ways to get around to making sure you can take care of those, you really found the next normal. You really started figuring out how to continue to innovate, continue to support each of those four constituencies, and people have done different things. I know it's amazing how CUBE continues to operate. As far as a user is concerned, they're all watching remote. Yes, we don't have the wonderful desk and we all get to chat and look in the eye. But the content, the message is as powerful as what it was a few months ago. So I'm sure this is how we're all going to figure out how to make through. There's a new next normal. >> Yeah, and digital transformation kind of went from push to pull, I mean every conference you'd go to, they'd say, "Well, look at Uber, look at Airbnb," and they put up the examples. "You have to do this too." And then all of a sudden the industry dragged you along. So I'm curious as to how, and I guess the other point there is digital means data. We've said that many, many times, if you didn't have a digital strategy during the height of the lockdown, you couldn't transact business and still many restaurants are still trying to figure this out, but so how did it affect you and your customers? >> Yeah, it's really interesting. And we spend a lot of time with several of our customers who are managing some of the largest IT organizations. And we talk about a very interesting phenomenon that happened somewhere beginning of this year, about 20 years ago, we used to worry about this thing called the digital divide. Those who have access to the network and internet, and those who don't. And now there is this data divide, the divide between organizations that know how to leverage, exploit, and absolutely accelerate the business using data, and those who don't. And I think we're seeing this effect show very clearly among organizations that are able to come back and address some of this stuff. And it's fascinating. Yes, we all have the examples of the likes of people who are doing delivery. People who are doing E-tailing, but there are so many little things, you're seeing organizations, and just the other day, we had a video from Sentry Data Systems, which is helping accelerate COVID-19 research because you're able to get copies of the data faster, they're able to get access to data, to their researchers much, much faster, sometimes from several days to a few minutes. It's that level of effect, it's not just down to some subtle, you know, you almost think of it as nice to have, but it's must have life threatening stuff, essential stuff, or just addressing today, I was reading a wonderful article about this supercomputer and that's doing analysis of COVID-19, and how it's figured out most of these symptoms, then able to figure it out by just crunching a ton of data. And almost every one of those symptoms the supercomputer has predicted, has been accurate. It's about data, right? It is absolutely about data, and which is why I think this is a phenomenal time for companies to absolutely go change, make this transformation about data acceleration, data leverage, data exploitation. And there's a ton of it all around us. >> Yeah, and part of that digital transformation, the mandate is to really put data at the core. I mean, we've certainly seen this with the top market cap companies. They've got data at the core, and now, as I say, it's become a mandate. And you know, there's been several things that we've clearly noticed. I mean, you saw the work from home required laptops and, you know, end point security and things of that, VDI made a comeback, and certainly cloud was there, but I've been struck by the reality of multi-cloud. I was kind of a multi-cloud skeptic early on. I said many times, I thought it was more of a symptom than it was a strategy, but that's completely flipped. Recently in our ETR surveys, we saw multicloud popping up all over the place. I wonder what you're seeing when you talk to your customers and other CIOs. >> Yeah. So fascinating. No, we released our first cloud product sometime around 2018, end of 2018. >> Dave: GO, right? >> Yeah, Actifio GO, OnVault, which is a phenomenal way to completely change the way you think about using object storage in the cloud. For over two years, we saw about 20% of our business, by the beginning of this year, 20% of our business was built on leveraging the cloud. Since March, so that was the end of our, almost the end of the Q1, to now, we're just in the middle of Q3. In six months, we added 12 more percent of the business. Literally we did it in six months, what we did not do before for 18 months before that, significantly more than what we did for a year and a half before that. And there are really three reasons, and you see this over and over again, we have a large customer we closed in January. Ironically we were deploying out of UK, a very large marketing organization, got everything deployed. They were running their backup and DR in a separate data center. And they had a practical problem of not being able to access the second site, literally in the middle of deployment, we steer that customer to GCP, or Google Cloud, because there was simply no way for them to continue protecting the data, being able to develop new applications with that data, they simply had no access. So there was, this was the number one reason, the inability for an organization to physically access or put their employees at risk, and have portal for the cloud be the infrastructure. That's number one. So that first of all drove the reason for the cloud. And then there's a second reason. There are practical reasons on why some cloud platforms are good at one workload. The other ones are not so good at some of the workloads. And so if I'm an organization that has, that spans everything, I've got a power PC, an X86 machine, a VM, I've got container platforms, I got Oracle, I got SAP. There is no single cloud platform that supports all my workload as efficiently, it's available in all the regions I want. So inevitably I have to go adopt different cloud platforms. So that's the second practical reason. And then there's a strategic reason. No vendor, no customer, wants to be locked into any one cloud platform. At least two, you're going to go pay, more likely three. So those are the reasons. And then interestingly enough, we were on a panel with us global CIOs. And in addition to just the usual cloud providers that we all know and love inside the US, across the world, in Europe, in Asia, there's a rise of regional cloud providers. So you take all these factors, right? You've got absolute physical necessity. You got practical constraints of what can the cloud provider support, the strategic reasons of why, either because, I don't want to be locked into a cloud provider, or because there's a rise of, you know, data nationalism that's going on, that people want to keep their data within the country bounds. All of these reasons are the foundations of why multicloud is almost becoming a de facto. It's impossible for a decent size organization to assume they would just depend on one cloud anymore. >> The other big trend we're seeing, I wonder if you could comment, is this notion of the data life cycle, of the data pipeline. It's a very complex situation for a lot of organizations. Their data is siloed. We hear that a lot. They have data scientists, data engineers, developers, data quality engineers, just a lot of different constituencies and lines of business, and it's kind of a mess. And so what they're trying to do is bring that together. So they've done that, data scientists complain, they spend all their time wrangling data, but ultimately the ones that are succeeding to putting data at the core as we've just been discussing, are seeing amazing outcomes, by being able to have a single version of the truth, have confidence in that data, create self-serve for their lines of business and actually reduce the end to end cycle times that's driving your major monetization, whether that's cost cutting or revenue. And I'm curious as to what you're seeing, you guys do a lot of work, heavy work in DevOps and hardcore database. Those are key components of that data life cycle. What are you seeing in that regard regarding that data pipeline? >> Yeah, that's a phenomenal point. If you really want to go back and exploit data within an organization, if you really want to be a data driven organization, the very first thing you have to do is break down the silos. Ironically, every organization has all the data required to make the decisions they want to. They just can't either get to it, or it's so hard to break the silos that it's just not worth trying to make it happen. And 10 years ago, we set out on this mission, rather than keep these individual silos of data, why don't we flip it open and make it into a pipeline which looks like a data cloud, where essentially anybody who's consuming it has access to it based on the governance rules, based on the security rules that the operations people have set. And based on the kind of format they want to see data, not everybody may want to see the data in a database format. Maybe you want the database format converted to a CSB format before you run analytics. And this idea of making data the new infrastructure, this idea of having the operations people provide this new layer container. It's finally come to roost. I mean, it's fascinating. I was looking at the numbers last quarter. We just finished up Q2. Now 45% of our customer base uses Actifio for, or reuses the backup data for things that accelerate the business, things that make the business move faster, more productive, or even survive. That was the mission. That was what we set out to do 10 years ago. You know, we were talking to an analyst this morning and now there's this question of, you know, "Hey, looks like there's a theme of backup data being reused." We said, "Yeah, that's kind of what we've been saying for 10 years." Backup cannot be an insurance, backup cannot be a destination. It has to be something that you can use as an asset. And that I think is finally coming to the point where you can use backup as a single source of truth, only if you designed it right from the beginning for that purpose, you cannot just, there are lots of ways to fake it, make it, try to pretend like you're doing it, but that was the true purpose of making data the new infrastructure, making it a cloud, making it something that is truly an asset. And it's fascinating to see our businesses. You take any of our large accounts, and the way they've gone about transforming, not just basic backup and DR. Yes, we are the world's fastest backup and most scalable DR solution. That's a starting point. But to be able to use that to develop applications eight, 10 times faster, to run analytics 100 X faster? The more data you have, the more people who use data you have, the better this return becomes. >> You know, that is interesting to hear you talk about that, because that has been the Holy Grail of backup was to go beyond insurance to actually create business value. And you're actually seeing some underlying trends, we talked about that data pipeline, and one of the areas that is the most interesting is in database, which was so boring for so many years, and you're seeing new workloads emerge. They take the data warehouse beyond, you know, reporting, never really lived up to its promise of 360 degree view. You mentioned analytics, that's really starting to happen. And it's all about data. You know, John Furrier used to say that data is the new development kit. You call it the new infrastructure and it's sort of the same type of theme. So maybe some of the trends you're seeing in database, I'd love to talk about that for a little bit, and then pick your brains on some other tech like object storage is another one that we've really seen take off. >> Yeah, so I think our journey with object storage began in 2016, 2017, as we started to adopt cloud platform in response to the user requirements, we did more like most companies have done and unfortunately continue to do, we take the on-prem product and then just move it onto the cloud. And one of the things we saw was there was a fundamental difference of how the design points of a cloud engineering is all about, what the design it for. Object storage is one of those primitives, the fundamental storage primitives that the cloud providers actually produced, that nobody really exploited. It was used as a graveyard for data. It's a replacement for the place where data goes to die. And then we look at it really closely and say, "Well, this is actually a massively scalable, very low cost storage, but it has some problems." It has an interface that you cannot use with traditional servers. It has some issues around, you know, not being able to read, modify, write the data, so it feels like you're consuming a lot of storage. So we went on to solve those problems. It took us a good two years to come back with something called OnVault, that fundamentally treats object storage like this massively scalable high performing disk. Except for just ridiculous low cost and optimize the capacity. So this thing called OnVault, as we patent it, has really become the foundation of how everything in cloud works without using CPU. Today there is simply nothing at a lower TCO, that actually, if you want to do basic backup, the more importantly use that to do this massive analytics. Now you're talking about data warehouse, data lakes, right? Because now there's something called data lakehouse. All of these are still silos. All of these are people trying to take some data from somewhere, put it into another new construct and have it be controlled by somebody else. This is autosync, it's just, you just move the silos from someplace to another place, and sort of creating a pipeline. If you want to really create a pipeline, object storage has been an integral part of that pipeline, not a separate bucket by itself. And that's what we did. And same thing with databases. You know, most business, most of the critical business runs on databases, and the ability to find a way to leverage those and move them around, leverage in terms of whichever format the database is accessed, whichever location it's accessed, doesn't matter how big it is. Lots of work has gone into trying to figure that one out. And we had some very, very good partners in some of our largest customers who helped take the journey with us. Pretty much all of the global 2000 accounts you see across the board, were an integral part of our process. >> You know, you mentioned the word journey and it triggered a thought, your discussion with Ravi, the CIO of Seagate, who's a customer of yours. And what he said, I liked what he said, he, of course he used the term journey, we all do. But he said, "You know what? I kind of don't like that term because I want to inject a sense of urgency," essentially what he was saying, "I want speed." You know, journey's like, "Okay, kids get in the car, we're going to drive across country. We're going to make some stops." And so while there's a journey, he also was really trying to push the organization hard. And he talked about culture as some of the most difficult things. Like many CIOs said, "No, the technology is almost the easy part. It's true when it works." >> That's true. >> I thought that was a great discussion that you had. What were your, some of your takeaways? >> I think Ravi's a very astute IT executive who's been around the block for so long. And one of the fascinating things, when I asked him this question about, "Hey, what's the biggest challenge, we've just gone through this a couple of times, what is the biggest challenge?" Taking an organization as venerable, as well known as Seagate is, I mean, this is a data company. This is at the heart of half the world's data is on Seagate stuff. How do you take this old company that's been around for long, in the middle of Silicon Valley, and make it into a fast growing transformation company that's responding to the newer challenges? And I thought he was going to come back with, "Well, you know, I got to go through these pieces, I pick this technology that technology," and surely that's exactly what I expected he would end up with. He goes "It has nothing to do with technology." In this day and age, when you can have an Elon Musk can send a car to Mars, there's not many technologies that we can't really solve. Maybe COVID-19 is the next frontier we got to go solve. But frankly, he hit upon the one thing that matters to every company. It is the fundamental culture to create a bias to action. It's a fundamental culture where you have to come back and have a deliverable that moves the ball forward every day, every month, every quarter, as opposed to have this series of, like you said, a journey that says, and we all know this, right? People talk about, "Oh, we're going to do this in phase one, we're going to do this in phase two and do this in phase three," nothing ever happens in phase three. Nobody gets around to phase three. So I think he did a great job of saying, "I fundamentally had to go change the culture." That was my biggest takeaway. And this, I've heard this so many times, the most effective IT execs who've made the transformation, it actually shows in the people that they have. It's not the technology, it's the people. And his history is replete with organizations that have done remarkably well, not by leveraging the heck out of the technology, but truly by leveraging the change in the people's mindset. And of course that mindset leverages technology where appropriate, but Ravi is a insightful person, always such a delight to talk to him, it's a delight for him to have chosen us as a foundational technology for him to go pull his data warehouses and completely transform how he's doing manufacturing across the globe. >> Yeah, I want to add some color to what you just said, because some key takeaways from what you just said, Ash, is, you know, you're right. When you look back at the history of the computer industry, there used to be very well known processes, but the technology was the big mystery and the big risk. And you think about with COVID, were it not for technology, we didn't know what was coming. We were inventing new processes literally every day, every week, every month. And so technology was pretty well understood, and enabled that. And when you think, when we talked earlier about putting data at the core, it was interesting to hear Ravi. He basically said, "Yeah, we had a big data team in the US, a big data team in Europe." We actually organized around silos. And so you guys played a role, you were very respectful about, you know, touting Actifio with him. You did ask him, you know, what role you play, but it was interesting to hear him talk about how he had to address that both culturally and of course, there's technology underneath to enable that unification of data, that silo busting, if you will. And you guys played a role in that. >> Yeah, well, I always enjoy conversation with the folks who have taken a problem, identified what needs to be done, and then just get it done. And that's more fascinating than, yeah, of course Actifio plays a small part in a lot of things, and we're proud to have played a small part in his big initiative. And that's true of the thousands of customers we talk about, but it's such a fascinating story to have leaders who come back and make this transformation happen and to understand how they went about making those decisions, how they identified where the problem was. These are so hard when we all see them in our own lives. We see there's a problem, but sometimes it takes a while to try to understand how do you identify them and what do you have to do? And more importantly, actually do it. And so whenever I get an opportunity with people like Ravi, I think understanding that, and if there's a way to help, we always make sure that we play our own small part and we're privileged to be a part of those kinds of journeys. >> I think what's interesting about Actifio and the company that you created is essentially that we're talking about the democratization of data, that whole data pipeline, that discussion that we had, the self service of that data to the lines of business, and, you know, you guys clearly play a role there. The multicloud discussion fits into that. I mean these are all trends that are tailwinds for companies that can help sort of flatten the data globe, if you will. Your final thoughts, Ash? >> Yeah, you said something that is so much at the heart of every IT exec that we are talking to. If data truly is the fundamental asset that I finally end up with as an organization, then democratization of data, where I do not lock this into another silo, another platform, another cloud, another application, has to be part of my foundation design. And therefore my ability to use each of these cloud platform for the services they provide while I am able to move the data to where I need it to be, that is so critical. So you almost start to think about the one position an organization now has. And we talked about this with a group of CIOs. There might be some pretty soon, not too far off, but if data is truly an asset, I might actually have a data market, just like you have a stock market, where I can start to sell my data, imagine a COVID-19, there's so many organizations that have so much data, and many of them have contributed to this research because this is an existential issue, but you can see this turning into a next level. So yes, we have got activists help move the data to one level higher where it's become a foundational construct for an organization. The next part is, can I actually turn this into an asset where I actually monetize some of this stuff? And it will be not too long when you and I could talk about how there's this new exchange and what's the rate of data for this company versus that company, and there'll be future trading options, who knows, it's going to be very interesting. >> Well, I think you're right on, this notion of a data marketplace is coming and it's not that far away. Well, Ash, it's always great to talk to you. I hope next year at Data Driven, we can be face to face, but I mean, look, this has been, we've dealt with it. It's actually created opportunities for us to kind of reinvent ourselves. So congratulations on the success that you've had and thank you for coming on theCUBE. >> No, thank you for hosting us and always a big fan of theCUBE. You guys, we've engaged with you since the early days, and it is fascinating to see how this company has grown. And it's probably many people don't even know how much you've grown behind the scenes and all the technologies and culture that you've created yourself. So it's hopefully one day we'll switch the table and then I'd be on the other side and ask you about transformation, digital transformation of CUBE itself. >> I'd love to do that, and thanks again, and thank you everybody for watching our continuous coverage of Actifio Data Driven. Keep it right there. We'll be back with our next guest right after this short break. >> Ash: Thank you, Dave. (calm music)

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi and welcome to the cube Studios for another cube conversation where we go in-depth with thought leaders driving innovation across the tech industry I'm your host Peter Burris if there's one thing we know about cloud it's that it's going to drive new data and a lot of it and that places a lot of load on storage technologies who have to be able to capture persist and ultimately deliver that data to new classes of applications in support of whatever the digital business is trying to do so how is the whole storage industry and the relationship between data and storage going to evolve I can't think of a better person to have that conversation with in stanley's a phos senior vice president product marketing infinite dad Stan welcome to the cube thank you for it's my pleasure to be here and I'm flattered with that introduction well hold on look you and I have known each other for a long time we have been walking into user presentations and you've been walking out until recently though you were generally regarded as the thought leader when it came to user side concerns about storage what is that problem that users are fundamentally focused on as they think about their data data management and storage requirements fundamental problems and this afflicts all classes of users whether in a financial institution at university government small business medium-sized businesses is that they're coping with the number of primal forces that don't change and the first is that the environment is becoming ever more competitive and with the environment being ever more competitive that means that they're always under budget constraints they're usually suffering from skill shortages especially now when we see so many new technologies and the realization that we can coax value out of the information that we capture and store creating new demands elsewhere within the IT organization so what we see historically is that uses understand that there you have an insatiable demand for capacity they have finite budgets they have limited skills and they realize that recovering from a loss of data integrity is a far more painful process than recovering from an application blowing up or a networking issue and they got to do it faster and they have to do it faster so what we see in some ways is in effect the perfect storm and this is part of the reason that we've seen a number of the technical evolutions that we've witnessed over the past decade or two decades or however long we'd like to admit we've been tracking this industry occurring and growing in importance what we've also seen is that many of the technologies that are useful in helping to deliver usable availability to the application are in some ways becoming more commoditized so when we look across these industries some of the things that we're looking for is cost efficiency we're looking at increasing levels of automation we're looking of increases in data mobility with the ultimate objective being of course to allow data to reside where it naturally belongs and we're trying to deliver these new capabilities at scale in infrastructures that were built with storage arrays that would design for a terabyte world instead of a petabyte world and it won't be too long before we start talking about exabytes as we're already seeing so to be able to satisfy new scale problems with traditional and well understood issues is there are three basic types of storage companies that are targeting this problem the first of the established storage companies the incumbents the incumbents and the incumbents I really don't envy them because they have to maintain backwards compatibility which limits their ability to innovate at the same time they're competing against privately held newer companies that aren't constrained by the need for backwards compatibility and therefore able to take better advantage of the technology improvements that we're seeing to live it and when I say technology improvements not just in hardware but also in terms of software also in terms of management and government and governing philosophies so beginning with the point that all companies large small have some basic problems that are similar what we then see is there are three types of storage companies addressing them one of the in established and common vendors the other and they've gotten a lot of press or the companies that realize that flash media very media that delivers one to two orders of magnitude improvements in terms of performance in terms of bandwidth in terms of environmental x' that they could create storage solutions that address real pain points within a data center within an organization but at a very high price point and then it was the third approach and this is the approach that infinite I chose to take and that is to define the customer problem to find the customer market and then create an architecture which is underpinned by brilliant software to solve these problems in a way that is both cost-effective and extensible and of course meeting all of the critical capabilities that users are looking for so we've got the situation where we've got the incumbents who have install bases and are trying to bring their customers forward but right I have to do so within the constraints of past technology choices we've got the new folks who are basically technology first and saying jump to a new innovation curve and we've got other companies that are trying to bring the best of the technology to the best of the customer reality and marry it and you're asserting that's what infinite at ease and then it's precisely what we've done so let's talk about why did you then come to infinite at what is it about infinite act that gets you excited well one of the things that got well your number of things that got me excited about it so the first is that when I look at this and I approach these things as an engineer who's steeped in aerospace and weapon systems design so you look at the problem you superimpose capabilities there and then you blow it up and then if well we do blow it up but we blow it up using economics we blow it up using superior post-sale support effectiveness we blow it up with a fundamentally different approach to how we give our install base access to new capabilities so we're established storage companies and to some extent media based storage companies of forcing upgrades to avoid architectural obsolescence that is to gain access to new features and functions that can improve their staff productivity or deliver new capabilities to support new applications and workloads we're not forcing a cadence of infrastructure refreshes to gain access to that so if you take a look at our history our past behavior we allow today we're allowing current software to run on n minus 2 generation hardware so that now when you're doing a refresh on your hardware you're doing a refresh on the hardware because you've outgrown it because it's so old that it's moved past its useful service life which hasn't happened to us yet because that's usually on the order of about eight years and sometimes longer if it's kept in a clean data center and we have a steady cadence of product announcements and we understood some underlying economics so whether I talk to banking institutions colleges manufacturing companies telcos service providers everybody's in general agreement that roughly two-thirds of the data that they have online and accessible is stale data meaning that it hasn't been accessed in 60 to 90 days and then when I take a look at industry forecasts in terms of dollar per terabyte pricing for HD DS for disk drives and I look at dollar per terabyte forecast for flash technologies there's an order of magnitude difference in meaning 10x and even if you want to be a pessimist call it only 5x what you see is that we have a built-in advantage for storing 60% of the data that's already up and spinning and there are those questions of whether or not the availability of flash is going to come under pressure over the next few years as because we're not expanding another fabs out there they're generating flash so let me come back right it's kind of core points out there so we have quality yeah the right now you guys are trying to bring the economics of HDD to the challenges are faster more reliable more scalable data delivery right so that you can think about not only persisting your data from transactional applications but also delivering that data to the new uses new requirements new applications new business needs so you've made you know infinite out has made some choices about how to bring technology together that are some somewhat that are unique first thing is the team that did this tell us a little bit about the team and then let's talk about some of those torches so one of the draws for me personally is that we have a development team that has had the unique possibly the unique experience of having done three not one not two but three clean sheet designs of storage arrays now if you believe that practice makes perfect and you're starting off with very bright people that experience before they designed a storage array when we look at the InfiniBand when we look at in Finnegan what we see is the benefit of three clean sheet designs and what does that design look like what is it how did you guys bring these different senses of technology together to improve the notion of it all right so what we looked at we looked at trends instead of being married to a technology or married to an architecture we were we define the users problem we understood that they have an insatiable need for data we can argue whether they're growing at fifteen percent 30 percent or 100 percent per year but data growth is insatiable stale data being a constant megive n' and of course now with digital business initiatives and moving the infrastructure to the edge where we could capture ever more data if anything the amount of stale data that was storing is likely to increase so we've all seen survey after survey that 80% of all the data created is unstructured data meaning we're collecting it we know that may be a value at some point but we're not quite sure when so this is not data that you want to store in the most expensive media that we know how to manufacture or sell right not happening so we have a built-in economic advantage for this at least 60% of the data that users want to keep online we understand that if you implement an archiving solution that archive data still has to be stored somewhere and for practical purposes that's either disk or tape and we're not here to talk about the fact that I can take tape and store in a bunker for years but if I want to recover something if I have to answer a problem I want it on disk so the economic gap the price Delta between an archive storage solution per se and our approach is much narrower because we're using a common technology and when Seagate or West and digital a Toshiba cell and HDD they're not asking you where you're putting it they're saying you want this capacity this rpm this mean time between face its this is how much it's going to cost so when we take a look at a lot of the innovation and go to market models what they really are or revenue protection schemes for the existing established vendors and for the emerging companies the difference is there are in the problems that they're solving am i creating a backup restore solution the backup and restore is always a high impact pain point am i creating a backup restore solution am i building a system for primary storage a my targeting virtualized environments my targeting VDI now our install base the bulk of our install base I'm not sure we actually we should share percentages but it's well north of 50 and if you take a look at some virtualized estimates probably 80% of workloads today are virtualized we understood that to satisfy this environment and to have a built-in advantage that's memorable after the marketing presentations are done in other words treating these things as black boxes so if we take a look at my high-level description of an infinite box array installed at a customer site consistent sub-millisecond response times and we're able to do that because we service over 80% of all iOS out of DRAM which is probably about four orders of magnitude faster than NAND flash and then we have a large read cache to increase our cache hit ratio even further and when I say large we're not talking about single digits of terabytes we're talking about 20 plus terabytes and that can grow as necessary so that when we're done we're achieving cache hit ratios that are typically in excess of 90% now if I'm servicing iOS out of cache do I really care what's on the back end the answer is no but what I do care about for certain analytics applications is I want lots of bandwidth and I want and if I have workloads with high right content I don't want to be spending a lot of time paying my raid right penalty so what we've done is to take the obvious solution and coalesce rights so that instead of doing partial stripe rights we're always doing full stripe rights so we have double bit protection on data stored on HD DS which means that the world is likely to come to an end before we lose this slight exaggeration I think we're expecting the world to come to an end in 14 billion years yeah yeah let's do so so if I'm wrong get back to me in a Bay and it's a little bit less than that but it doesn't matter yeah okay high on that all so we've got a so we've got a built in economic advantage we've got a built in performance advantage because when I'm servicing most iOS out of DRAM which is for does magnitude faster than NAND flash I've got a lot of room to do a lot of very clever things in terms of metadata and still be faster so and you got a team that's done it before and we've got a team that's done it before and experimented because remember this is a team that has experience with scale-up architectures as in symmetric s-- they have experience with scale-out architectures which is XIV which was very disruptive to the market well so was it symmetric spec and now of course we've got this third bite at the Apple with infinite at where they also understood that the rate of microprocessor performance improvement was going up a lot faster than than our ability to transfer data on and off of HD DS or SSDs so what they realized is that they could change the ratio they can have a much lower microprocessor or controller to back-end storage ratio and still be able to deliver this tremendous performance and now if you have fewer parts and you're not affecting the ID MTBF by driving more iOS through I've lowered my overall cost of goods so now I've got an advantage in back-end media I have a bag I have an advantage in terms of the number of controllers I need to deliver sub sillas eken response time I have an advantage in terms of delivering usable availability so I'm now in a position to be able to unashamedly compete on price unashamedly compete on performance unashamedly compete on a better post sale support experience because remember if there's less stuff they had a break we're taking less calls and because of the way we're organized our support generally goes to what other vendors might think of it's third level support because of a guided answer answers the phone from us doesn't solve the problem he's calling development so if you take a look at gotten apear insights we're off the scale in terms of having great reviews and when you have I think it's 99% I may be off by a percent ninety eight to a hundred percent of our customers saying they'd recommend our kit to their to their peers that's a pretty positive endorsement yeah so let me let me break in and and kind of wrap up a little bit let me make this quick observation because the other thing that you guys have done is you've demonstrated that you're not bound to a single technology so smart people with a great architecture that's capable of utilizing any technology to serve a customer problem at a price point that reflects the value of the problem that's being solved right and in fact we it's very insightful observation because when you recognize that we've built a multimedia integrated architecture that makes our that makes very easy for us to include storage class memory and because of the way we've done our drivers we're also going to be nvme over if ready when that starts to gain traction as well excellent Stanley Zappos senior vice president product management Infini debt thanks very much for being in the cube we'll have you back oh it's my pleasure there's been a blast and once again I want to thank you for joining us for another cube conversation on Peterborough's see you next time [Music]