>> Welcome back to Supercloud22, #Supercloud22. This is Dave Vellante. In 2019 Oracle and Microsoft announced a collaboration to bring interoperability between OCI, Oracle Cloud Infrastructure and Azure Clouds. It was Oracle's initial foray into so-called multi-cloud and we're joined by Karan Batta, who's the Vice President for Product Management at OCI. And Kris Rice is the Vice President of Software Development at Oracle Database. And we're going to talk about how this technology's evolving and whether it fits our view of what we call supercloud. Welcome gentlemen, thank you. >> Thanks for having us. >> So you recently just last month announced the new service. It extends on the initial partnership with Microsoft Oracle interconnect with Azure, and you refer to this as a secure private link between the two clouds, it cross 11 regions around the world, under two milliseconds data transmission sounds pretty cool. It enables customers to run Microsoft applications against data stored in Oracle databases without any loss in efficiency or presumably performance. So we use this term supercloud to describe a service or sets of services built on hyper scale infrastructure that leverages the core primitives and APIs of an individual cloud platform, but abstracts that underlying complexity to create a continuous experience across more than one cloud. Is that what you've done? >> Absolutely. I think it starts at the top layer in terms of just making things very simple for the customer, right. I think at the end of the day we want to enable true workloads running across two different clouds where you're potentially running maybe the app layer in one and the database layer or the back in another. And the integration I think starts with, you know, making it ease of use. Right. So you can start with things like, okay can you log into your second or your third cloud with the first cloud provider's credentials? Can you make calls against another cloud using another cloud's APIs? Can you peer the networks together? Can you make it seamless? I think those are all the components that are sort of, they're kind of the ingredients to making a multi-cloud or supercloud experience successful. >> Oh, thank you for that, Karan. So I guess there's a question for Chris is I'm trying to understand what you're really solving for? What specific customer problems are you focused on? What's the service optimized for presumably it's database but maybe you could double click on that. >> Sure. So, I mean, of course it's database. So it's a super fast network so that we can split the workload across two different clouds leveraging the best from both, but above the networking, what we had to do do is we had to think about what a true multi-cloud or what you're calling supercloud experience would be it's more than just making the network bites flow. So what we did is we took a look as Karan hinted at right, is where is my identity? Where is my observability? How do I connect these things across how it feels native to that other cloud? >> So what kind of engineering do you have to do to make that work? It's not just plugging stuff together. Maybe you could explain a little bit more detail, the the resources that you had to bring to bear and the technology behind the architecture. >> Sure. I think, it starts with actually, what our goal was, right? Our goal was to actually provide customers with a fully managed experience. What that means is we had to basically create a brand new service. So, we have obviously an Azure like portal and an experience that allows customers to do this but under the covers, we actually have a fully managed service that manages the networking layer, the physical infrastructure, and it actually calls APIs on both sides of the fence. It actually manages your Azure resources, creates them but it also interacts with OCI at the same time. And under the covers this service actually takes Azure primitives as inputs. And then it sort of like essentially translates them to OCI action. So, we actually truly integrated this as a service that's essentially built as a PaaS layer on top of these two clouds. >> So, the customer doesn't really care or know maybe they know cuz they might be coming through, an Azure experience, but you can run work on either Azure and or OCI. And it's a common experience across those clouds. Is that correct? >> That's correct. So like you said, the customer does know that they know there is a relationship with both clouds but thanks to all the things we built there's this thing we invented we created called a multi-cloud control plane. This control plane does operate against both clouds at the same time to make it as seamless as possible so that maybe they don't notice, you know, the power of the interconnect is extremely fast networking, as fast as what we could see inside a single cloud. If you think about how big a data center might be from edge to edge in that cloud, going across the interconnect makes it so that that workload is not important that it's spanning two clouds anymore. >> So you say extremely fast networking. I remember I used to, I wrote a piece a long time ago. Larry Ellison loves InfiniBand. I presume we've moved on from them, but maybe not. What is that interconnect? >> Yeah, so it's funny you mentioned interconnect you know, my previous history comes from Edge PC where we actually inside OCI today, we've moved from Infinite Band as is part of Exadata's core to what we call Rocky V two. So that's just another RDMA network. We actually use it very successfully, not just for Exadata but we use it for our standard computers that we provide to high performance computing customers. >> And the multi-cloud control plane runs. Where does that live? Does it live on OCI? Does it live on Azure? Yes? >> So it does it lives on our side. Our side of the house as part of our Oracle OCI control plane. And it is the veneer that makes these two clouds possible so that we can wire them together. So it knows how to take those Azure primitives and the OCI primitives and wire them at the appropriate levels together. >> Now I want to talk about this PaaS layer. Part of supercloud, we said to actually make it work you're going to have to have a super PaaS. I know we're taking this this term a little far but it's still it's instructive in that, what we surmised was you're probably not going to just use off the shelf, plain old vanilla PaaS, you're actually going to have a purpose built PaaS to solve for the specific problem. So as an example, if you're solving for ultra low latency, which I think you're doing, you're probably no offense to my friends at Red Hat but you're probably not going to develop this on OpenShift, but tell us about that PaaS layer or what we call the super PaaS layer. >> Go ahead, Chris. >> Well, so you're right. We weren't going to build it out on OpenShift. So we have Oracle OCI, you know, the standard is Terraform. So the back end of everything we do is based around Terraform. Today, what we've done is we built that control plane and it will be API drivable, it'll be drivable from the UI and it will let people operate and create primitives across both sides. So you can, you mentioned developers, developers love automation, right, because it makes our lives easy. We will be able to automate a multi-cloud workload from ground up config is code these days. So we can config an entire multi-cloud experience from one place. >> So, double click Chris on that developer experience. What is that like? They're using the same tool set irrespective of, which cloud we're running on is, and it's specific to this service or is it more generic, across other Oracle services? >> There's two parts to that. So one is the, we've only onboarded a portion. So the database portfolio and other services will be coming into this multi-cloud. For the majority of Oracle cloud, the automation, the config layer is based on Terraform. So using Terraform, anyone can configure everything from a mid-tier to an Exadata, all the way soup to nuts from smallest thing possible to the largest. What we've not done yet is integrated truly with the Azure API, from command line drivable. That is coming in the future. It is on the roadmap, it is coming. Then they could get into one tool but right now they would have half their automation for the multi-cloud config on the Azure tool set and half on the OCI tool set. >> But we're not crazy saying from a roadmap standpoint that will provide some benefit to developers and is a reasonable direction for the industry generally but Oracle and Microsoft specifically. >> Absolutely. I'm a developer at heart. And so one of the things we want to make sure is that developers' lives are as easy as possible. >> And is there a metadata management layer or intelligence that you've built in to optimize for performance or low latency or cost across the respective clouds? >> Yeah, definitely. I think, latency's going to be an important factor. The service that we've initially built isn't going to serve, the sort of the tens of microseconds but most applications that are sort of in, running on top of the enterprise applications that are running on top of the database are in the several millisecond range. And we've actually done a lot of work on the networking pairing side to make sure that when we launch these resources across the two clouds we actually picked the right trial site. We picked the right region we pick the right availability zone or domain. So we actually do the due diligence under the cover so the customer doesn't have to do the trial and error and try to find the right latency range. And this is actually one of the big reasons why we only launch the service on the interconnect regions. Even though we have close to, I think close to 40 regions at this point in OCI, this service is only built for the regions that we have an interconnect relationship with Microsoft. >> Okay, so you started with Microsoft in 2019. You're going deeper now in that relationship, is there any reason that you couldn't, I mean technically what would you have to do to go to other clouds? You talked about understanding the primitives and leveraging the primitives of Azure. Presumably if you wanted to do this with AWS or Google or Alibaba, you would have to do similar engineering work, is that correct? Or does what you've developed just kind of poured over to any cloud? >> Yeah, that's absolutely correct Dave. I think Chris talked a lot about the multi-cloud control plane, right? That's essentially the control plane that goes and does stuff on other clouds. We would have to essentially go and build that level of integration into the other clouds. And I think, as we get more popularity and as more products come online through these services I think we'll listen to what customers want. Whether it's, maybe it's the other way around too, Dave maybe it's the fact that they want to use Oracle cloud but they want to use other complimentary services within Oracle cloud. So I think it can go both ways. I think, the market and the customer base will dictate that. >> Yeah. So if I understand that correctly, somebody from another cloud Google cloud could say, Hey we actually want to run this service on OCI cuz we want to expand our market. And if TK gets together with his old friends and figures that out but then we're just, hypothesizing here. But, like you said, it can go both ways. And then, and I have another question related to that. So, multi clouds. Okay, great. Supercloud. How about the Edge? Do you ever see a day where that becomes part of the equation? Certainly the near Edge would, you know, a Home Depot or Lowe's store or a bank, but what about the far Edge, the tiny Edge. Can you talk about the Edge and where that fits in your vision? >> Yeah, absolutely. I think Edge is a interestingly, it's getting fuzzier and fuzzier day by day. I think, the term. Obviously every cloud has their own sort of philosophy in what Edge is, right. We have our own. It starts from, if you do want to do far Edge, we have devices like red devices, which is our ruggedized servers that talk back to our control plane in OCI. You could deploy those things unlike, into war zones and things like that underground. But then we also have things like clouded customer where customers can actually deploy components of our infrastructure like compute or Exadata into a facility where they only need that certain capability. And then a few years ago we launched, what's now called Dedicated Region. And that actually is a different take on Edge in some sense where you get the entire capability of our public commercial region, but within your facility. So imagine if a customer was to essentially point a finger on a commercial map and say, Hey, look, that region is just mine. Essentially that's the capability that we're providing to our customers, where if you have a white space if you have a facility, if you're exiting out of your data center space, you could essentially place an OCI region within your confines behind your firewall. And then you could interconnect that to a cloud provider if you wanted to, and get the same multi-cloud capability that you get in a commercial region. So we have all the spectrums of possibilities here. >> Guys, super interesting discussion. It's very clear to us that the next 10 years of cloud ain't going to be like the last 10. There's a whole new layer. Developing, data is a big key to that. We see industries getting involved. We obviously didn't get into the Oracle Cerner acquisitions. It's a little too early for that but we've actually predicted that companies like Cerner and you're seeing it with Goldman Sachs and Capital One they're actually building services on the cloud. So this is a really exciting new area and really appreciate you guys coming on the Supercloud22 event and sharing your insights. Thanks for your time. >> Thanks for having us. >> Okay. Keep it right there. #Supercloud22. We'll be right back with more great content right after this short break. (lighthearted marimba music)

>>Good morning or good afternoon or good evening, depending on where you are and welcome to this session, reduce claims, fraud, we're data, very excited to have you all here. My name is Winnie castling and I'm Cloudera as managing director for the insurance vertical. First and foremost, we want to let you know that we know insurance. We have done it for a long time. Collectively, personally, I've done it for over 30 years. And, you know, as a proof of that, we want to let you know that we insure, we insure as well as we do data management work for the top global companies in the world, in north America, over property casualty, general insurance health, and, um, life and annuities. But besides that, we also take care of the data needs for some smaller insurance companies and specialty companies. So if you're not one of the huge Glomar conglomerates in the world, you are still perfectly fine with us. >>So >>Why are we having this topic today? Really digital claims and digital claims management is accelerating. And that's based on a couple of things. First and foremost, customers are asking for it. Customers are used to doing their work more digitally over the last descending year or two. And secondly, with the last year or almost two, by now with the changes that we made in our work processes and in society at large around cuvettes, uh, both regulators, as well as companies have enabled digital processing and the digital journey to a degree that they've never done before. Now that had some really good impacts for claims handling. It did meant that customers were more satisfied. They felt they have more control over their processes in the cloud and the claims experience. It also reduced in a lot of cases, both in commercial lines, as well as in personal lines, the, um, the, the time periods that it took to settle on a claim. However, um, the more digital you go, it, it opened up more access points for fraud, illicit activities. So unfortunately we saw indicators of fraud and fraud attempts, you know, creeping up over the last time period. So we thought it was a good moment to look at, you know, some use cases and some approaches insurers can take to manage that even better than they already >>Are. >>And this is how we plan to do that. And this is how we see this in action. On the left side, you see progress of data analytics and data utilization, um, around, in this case, we're talking about claims fraud, but it's a generic picture. And really what it means is most companies that start with data affords pretty much start around data warehousing and we eliminate analytics and all around BI and reporting, which pretty much is understanding what we know, right? The data that we already have utilizing data to understand better what we know already. Now, when we move to the middle blue collar, we get into different types of analytics. We get into exploratory data science, we get to predictions and we start getting in the space of describing what we can learn from what we know, but also start moving slowly into predicting. So first of all, learn and gather insights of what we already know, and then start augmenting with that with other data sets and other findings, so that we can start predicting for the future, what might happen. >>And that's the point where we get to AI, artificial intelligence and machine learning, which will help us predict which of our situations and claims are most likely to have a potential fraud or abuse scenario attached to it. So that's the path that insurers and other companies take in their data management and analytics environments. Now, if you look at the right side of this light, you see data complexity per use cases in this case in fraud. So the bubbles represent the types of data that are being used, or the specific faces that we discussed on the left side. So for reporting, we used a TPA data, policy verification, um, claims file staff data, that it tends to be heavily structured and already within the company itself. And when you go to the middle to the more descriptive basis, you start getting into unstructured data, you see a lot of instructor texts there, and we do a use case around that later. >>And this really enables us to better understand what the scenarios are that we're looking at and where the risks are around. In our example today, fraud, abuse and issues of resources. And then the more you go to the upper right corner, you see the outside of the baseball field, people refer to it, you see new unstructured data sources that are being used. You tend to see the more complex use cases. And we're looking at picture analysis, we're looking at voice analysis there. We're looking at geolocation. That's quite often the first one we look at. So this slide actually shows you the progress and the path in complexity and in utilization of data and analytical tool sets to manage data fraud, fraud, use cases, optimally. >>Now how we do that and how we look at at a Cloudera is actually not as complicated as, as this slight might want to, um, to, to give you an impression. So let's start at the left side at the left side, you see the enterprise data, which is data that you as an organization have, or that you have access to. It doesn't have to be internal data, but quite often it is now that data goes into a data journey, right? It gets collected first. It gets manipulated and engineered so that people can do something with it. It gets stored something, you know, people need to have access to it. And then they get into analytical capabilities who are inside gathering and utilization. Now, especially for insurance companies that all needs to be underpinned by a very, very strong security and governance, uh, environment. Because if not the most regulated industry in the world, insurance is awfully close. >>And if it's not the most regulated one, it's a close second. So it's critically important that insurers know, um, where the data is, who has access to it for Rodriguez, uh, what is being used for so terms like lineage, transparency are crucial, crucially important for insurance. And we manage that in the shared data experience. So it goes over the whole Cloudera platform and every application or tool or experience you use would include Dao. And on the right side, you see the use cases that tend to be deployed around claims and claims fraud, claims, fraud management. So over the last year or so, we've seen a lot of use cases around upcoding people get one treatment or one fix on a car, but it gets coded as a more expensive one. That's a fraud scenario, right? We see also the more classical fraud things and we see anti money laundering. So those are the types of use cases on the right side that we are supporting, um, on the platform, uh, around, um, claims fraud. >>And this is an example of how that actually looks like now, this is a one that it's actually a live one of, uh, a company that had, um, claims that dealt with health situations and being killers. So that obviously is relevant for health insurers, but you also see it in, um, in auto claims and counterclaims, right, you know, accidents. There are a lot of different claims scenarios that have health risks associated with it. And what we did in this one is we joined tables in a complex schema. So we have to look at the claimant, the physician, the hospital, all the providers that are involved procedures that are being deployed. Medically medicines has been utilized to uncover the full picture. Now that is a hard effort in itself, just for one claim and one scenario. But if you want to see if people are abusing, for example, painkillers in this scenario, you need to do that over every instant that is member. >>This claimant has, you know, with different doctors, with different hospitals, with different pharmacies or whatever that classically it's a very complicated and complex, um, the and costly data operation. So nowadays that tends to be done by graph databases, right? So you put fraud rings within a graph database and walk the graph. And if you look at it here in batch, you can see that in this case, that is a member that was shopping around for being killers and went through different systems and different providers to get, um, multiple of the same big LR stat. You know, obviously we don't know what he or she did with it, but that's not the intent of the system. And that was actually a fraud and abuse case. >>So I want to share some customer success stories and recent, uh, AML and fraud use cases. And we have a couple of them and I'm not going to go in an awful lot of detail, um, about them because we have some time to spend on one of them immediately after this. But one of them for example, is voice analytics, which is a really interesting one. And on the baseball slide that I showed you earlier, that would be a right upper corner one. And what happened there is that an insurance company utilized the, uh, the voice records they got from the customer service people to try to predict which one were potentially fraud list. And they did it in two ways. They look at actually the contents of what was being said. So they looked at certain words that were being used certain trigger words, but they also were looking at tone of voice pitch of voice, uh, speed of talking. >>So they try to see trends there and hear trends that would, um, that would bring them for a potential bad situation. Now good and bad news of this proof of concept was it's. We learned that it's very difficult just because every human is different to get an indicator for bad behavior out of the pitch or the tone or the voice, you know, or those types of nonverbal communication in voice. But we did learn that it was easier to, to predict if a specific conversation needed to be transferred to somebody else based on emotion. You know, obviously as we all understand life and health situations tend to come with emotions, or so people either got very sad or they got very angry or so the proof of concept didn't really get us to a firm understanding of potential driverless situation, but it did get us to a much better understanding of workflow around, um, claims escalation, um, in customer service to route people, to the right person, depending on what they need. >>And that specific time, another really interesting one was around social media, geo open source, all sorts of data that we put together. And we linked to the second one that I listed on slide here that was an on-prem deployment. And that was actually an analysis that regulators were asking for in a couple of countries, uh, for anti money laundering scams, because there were some plots out there that networks of criminals would all buy the low value policies, surrendered them a couple of years later. And in that way, God criminal money into the regular amount of monetary system whitewashed the money and this needed some very specific and very, very complex link analysis because there were fairly large networks of criminals that all needed to be tied together, um, with the actions, with the policies to figure out where potential pain points were. And that also obviously included ecosystems, such as lawyers, administrative offices, all the other things, no, but most, you know, exciting. >>I think that we see happening at the moment and we, we, you know, our partner, if analytics just went live with this with a large insurer, is that by looking at different types that insurers already have, um, unstructured data, um, um, their claims nodes, um, repour its claims, filings, um, statements, voice records, augmented with information that they have access to, but that's not their ours such as geo information obituary, social media Boyd on the cloud. And we can analyze claims much more effectively and efficiently for fraud and litigation and alpha before. And the first results over the last year or two showcasing a significant degree is significant degrees in claims expenses and, um, and an increase at the right moment of what a right amount in claims payments, which is obviously a good thing for insurers. Right? So having said all of that, I really would like to give Sri Ramaswami, the CEO of infinite Lytics, the opportunity to walk you through this use case and actually show you how this looks like in real life. So Sheree, here >>You go. So >>Insurers often ask us this question, can AI help insurance companies, lower loss expenses, litigation, and help manage reserves better? We all know that insurance industry is majority. Majority of it is unstructured data. Can AI analyze all of this historically and look for patterns and trends to help workflows and improve process efficiencies. This is exactly why we brought together industry experts at infill lyrics to create the industries where very first pre-trained and prebuilt insights engine called Charlie, Charlie basically summarizes all of the data structured and unstructured. And when I say unstructured, I go back to what money basically traded. You know, it is including documents, reports, third-party, um, it reports and investigation, uh, interviews, statements, claim notes included as well at any third party enrichment that we can legally get our hands on anything that helps the adjudicate, the claims better. That is all something that we can include as part of the analysis. And what Charlie does is takes all of this data and very neatly summarizes all of this. After the analysis into insights within our dashboard, our proprietary naturally language processing semantic models adds the explanation to our predictions and insights, which is the key element that makes all of our insights >>Actually. So >>Let's just get into, um, standing what these steps are and how Charlie can help, um, you know, with the insights from the historical patterns in this case. So when the claim comes in, it comes with a lot of unstructured data and documents that the, uh, the claims operations team have to utilize to adjudicate, to understand and adjudicate the claim in an efficient manner. You are looking at a lot of documents, correspondences reports, third party reports, and also statements that are recorded within the claim notes. What Charlie basically does is crunches all, all of this data removes the noise from that and brings together five key elements, locations, texts, sentiments, entities, and timelines in the next step. >>In the next step, we are basically utilizing Charlie's built-in proprietary, natural language processing models to semantically understand and interpret all of that information and bring together those key elements into curated insights. And the way we do that is by building knowledge, graphs, and ontologies and dictionaries that can help understand the domain language and convert them into insights and predictions that we can display on the dash. Cool. And if you look at what has been presented in the dashboard, these are KPIs and metrics that are very interesting for a management staff or even the operations. So the management team can basically look at the dashboard and start with the summarized data and start to then dig deeper into each of the problematic areas and look at patterns at that point. And these patterns that we learn from not only from what the system can provide, but also from the historic data can help understand and uncover some of these patterns in the newer claims that are coming in so important to learn from the historic learnings and apply those learnings in the new claims that are coming in. >>Let's just take a very quick example of what this is going to look like a claims manager. So here the claims manager discovers from the summarized information that there are some problems in the claims that basically have an attorney involved. They have not even gone into litigation and they still are, you know, I'm experiencing a very large, um, average amount of claim loss when they compare to the benchmark. So this is where the manager wants to dig deeper and understand the patterns behind it from the historic data. And this has to look at the wealth of information that is sitting in the unstructured data. So Charlie basically pulls together all these topics and summarizes these topics that are very specific to certain losses combined with entities and timelines and sentiments, and very quickly be able to show to the manager where the problematic areas are and what are those patterns leading to high, severe claims, whether it's litigation or whether it's just high, severe indemnity payments. >>And this is where the managers can adjust their workflows based on what we can predict using those patterns that we have learned and predict the new claims, the operations team can also leverage Charlie's deep level insights, claim level insights, uh, in the form of red flags, alerts and recommendations. They can also be trained using these recommendations and the operations team can mitigate the claims much more effectively and proactively using these kind of deep level insights that need to look at unstructured data. So at the, at the end, I would like to say that it is possible for us to achieve financial benefits, leveraging artificial intelligence platforms like Charlie and help the insurers learn from their historic data and being able to apply that to the new claims, to work, to adjust their workflows efficiently. >>Thank you very much for you. That was very enlightening as always. And it's great to see that actually, some of the technology that we all work so hard on together, uh, comes to fruition in, in cost savings and efficiencies and, and help insurers manage potential bad situations, such as claims fraud batter, right? So to close this session out as a next step, we would really urge you to a Sasha available data sources and advanced or predictive fraud prevention capabilities aligned with your digital initiatives to digital initiatives that we all embarked on over the last year are creating a lot of new data that we can use to learn more. So that's a great thing. If you need to learn more at one to learn more about Cloudera and our insurance work and our insurance efforts, um, you to call me, uh, I'm very excited to talk about this forever. So if you want to give me a call or find a place to meet when that's possible again, and schedule a meeting with us, and again, we love insurance. We'll gladly talk to anyone until they say in parts of the United States, the cows come home about it. And we're dad. I want to thank you all for attending this session and hanging in there with us for about half an hour. And I hope you have a wonderful rest of the day. >>Good afternoon, I'm wanting or evening depending on where you are and welcome to this breakout session around insurance, improve underwriting with better insights. >>So first and >>Foremost, let's summarize very quickly, um, who we're with and what we're talking about today. My name is goonie castling, and I'm the managing director at Cloudera for the insurance vertical. And we have a sizeable presence in insurance. We have been working with insurance companies for a long time now, over 10 years, which in terms of insurance, it's maybe not that long, but for technology, it really is. And we're working with, as you can see some of the largest companies in the world and in the continents of the world. However, we also do a significant amount of work with smaller insurance companies, especially around specialty exposures and the regionals, the mutuals in property, casualty, general insurance, life, annuity, and health. So we have a vast experience of working with insurers. And, um, we'd like to talk a little bit today about what we're seeing recently in the underwriting space and what we can do to support the insurance industry in there. >>So >>Recently what we have been seeing, and it's actually accelerated as a result of the recent pandemic that we all have been going through. We see that insurers are putting even more emphasis on accounting for every individual customers with lotta be a commercial clients or a personal person, personal insurance risk in a dynamic and a B spoke way. And what I mean with that is in a dynamic, it means that risks and risk assessments change very regularly, right? Companies go into different business situations. People behave differently. Risks are changing all the time and the changing per person they're not changing the narrow generically my risk at a certain point of time in travel, for example, it might be very different than any of your risks, right? So what technology has started to enable is underwrite and assess those risks at those very specific individual levels. And you can see that insurers are investing in that capability. The value of, um, artificial intelligence and underwriting is growing dramatically. As you see from some of those quotes here and also risks that were historically very difficult to assess such as networks, uh, vendors, global supply chains, um, works workers' compensation that has a lot of moving parts to it all the time and anything that deals with rapidly changing risks, exposures and people, and businesses have been supported more and more by technology such as ours to help, uh, gone for that. >>And this is a bit of a difficult slide. So bear with me for a second here. What this slide shows specifically for underwriting is how data-driven insights help manage underwriting. And what you see on the left side of this slide is the progress in make in analytical capabilities. And quite often the first steps are around reporting and that tends to be run from a data warehouse, operational data store, Starsky, Matt, um, data, uh, models and reporting really is, uh, quite often as a BI function, of course, a business intelligence function. And it really, you know, at a regular basis informs the company of what has been taken place now in the second phase, the middle dark, the middle color blue. The next step that is shore stage is to get into descriptive analytics. And what descriptive analytics really do is they try to describe what we're learning in reporting. >>So we're seeing sorts and events and sorts and findings and sorts of numbers and certain trends happening in reporting. And in the descriptive phase, we describe what this means and you know why this is happening. And then ultimately, and this is the holy grill, the end goal we like to get through predictive analytics. So we like to try to predict what is going to happen, uh, which risk is a good one to underwrite, you know, watch next policy, a customer might need or wants water claims as we discuss it. And not a session today, uh, might become fraud or lists or a which one we can move straight through because they're not supposed to be any issues with it, both on the underwriting and the claims side. So that's where every insurer is shooting for right now. But most of them are not there yet. >>Totally. Right. So on the right side of this slide specifically for underwriting, we would, we like to show what types of data generally are being used in use cases around underwriting, in the different faces of maturity and analytics that I just described. So you will see that on the reporting side, in the beginning, we start with rates, information, quotes, information, submission information, bounding information. Um, then if you go to the descriptive phase, we start to add risk engineering information, risk reports, um, schedules of assets on the commercial side, because some are profiles, uh, as a descriptions, move into some sort of an unstructured data environment, um, notes, diaries, claims notes, underwriting notes, risk engineering notes, transcripts of customer service calls, and then totally to the other side of this baseball field looking slide, right? You will see the relatively new data sources that can add tremendous value. >>Um, but I'm not Whitely integrated yet. So I will walk through some use cases around these specifically. So think about sensors, wearables, you know, sensors on people's bodies, sensors, moving assets for transportation, drone images for underwriting. It's not necessary anymore to send, uh, an inspection person and inspector or risk, risk inspector or engineer to every building, you know, be insurers now, fly drones over it, to look at the roofs, et cetera, photos. You know, we see it a lot in claims first notice of loss, but we also see it for underwriting purposes that policies out there. Now that pretty much say sent me pictures of your five most valuable assets in your home and we'll price your home and all its contents for you. So we start seeing more and more movements towards those, as I mentioned earlier, dynamic and bespoke types of underwriting. >>So this is how Cloudera supports those initiatives. So on the left side, you see data coming into your insurance company. There are all sorts of different data. There are, some of them are managed and controlled by you. Some orders you get from third parties, and we'll talk about Della medics in a little bit. It's one of the use cases. They move into the data life cycle, the data journey. So the data is coming into your organization. You collected, you store it, you make it ready for utilization. You plop it either in an operational environment for processing or in an analytical environment for analysis. And then you close on the loop and adjusted from the beginning if necessary, no specifically for insurance, which is if not the most regulated industry in the world it's coming awfully close, and it will come in as a, a very admirable second or third. >>Um, it's critically important that that data is controlled and managed in the correct way on the old, the different regulations that, that we are subject to. So we do that in the cloud era Sharon's data experiment experience, which is where we make sure that the data is accessed by the right people. And that we always can track who did watch to any point in time to that data. Um, and that's all part of the Cloudera data platform. Now that whole environment that we run on premise as well as in the cloud or in multiple clouds or in hybrids, most insurers run hybrid models, which are part of the data on premise and part of the data and use cases and workloads in the clouds. We support enterprise use cases around on the writing in risk selection, individualized pricing, digital submissions, quote processing, the whole quote, quote bound process, digitally fraud and compliance evaluations and network analysis around, um, service providers. So I want to walk you to some of the use cases that we've seen in action recently that showcases how this work in real life. >>First one >>Is to seize that group plus Cloudera, um, uh, full disclosure. This is obviously for the people that know a Dutch health insurer. I did not pick the one because I happen to be dodged is just happens to be a fantastic use case and what they were struggling with as many, many insurance companies is that they had a legacy infrastructure that made it very difficult to combine data sets and get a full view of the customer and its needs. Um, as any insurer, customer demands and needs are rapidly changing competition is changing. So C-SAT decided that they needed to do something about it. And they built a data platform on Cloudera that helps them do a couple of things. It helps them support customers better or proactively. So they got really good in pinging customers on what potential steps they need to take to improve on their health in a preventative way. >>But also they sped up rapidly their, uh, approvals of medical procedures, et cetera. And so that was the original intent, right? It's like serve the customers better or retain the customers, make sure what they have the right access to the right services when they need it in a proactive way. As a side effect of this, um, data platform. They also got much better in, um, preventing and predicting fraud and abuse, which is, um, the topic of the other session we're running today. So it really was a good success and they're very happy with it. And they're actually starting to see a significant uptick in their customer service, KPIs and results. The other one that I wanted to quickly mention is Octo. As most of you know, Optune is a very, very large telemedics provider, telematics data provider globally. It's been with Cloudera for quite some time. >>This one I want to showcase because it showcases what we can do with data in mass amounts. So for Octo, we, um, analyze on Cloudera 5 million connected cars, ongoing with 11 billion data points. And really what they're doing is the creating the algorithms and the models and insurers use to, um, to, um, run, um, tell them insurance, telematics programs made to pay as you drive pay when you drive, pay, how you drive. And this whole telemedics part of insurance is actually growing very fast too, in, in, still in sort of a proof of concept mini projects, kind of initiatives. But, um, what we're succeeding is that companies are starting to offer more and more services around it. So they become preventative and predictive too. So now you got to the program staff being me as a driver saying, Monique, you're hopping in the car for two hours. >>Now, maybe it's time you take a break. Um, we see that there's a Starbucks coming up on the ride or any coffee shop. That's part of a bigger chain. Uh, we know because you have that app on your phone, that you are a Starbucks user. So if you stop there, we'll give you a 50 cents discount on your regular coffee. So we start seeing these types of programs coming through to, again, keep people safe and keep cars safe, but primarily of course the people in it, and those are the types of use cases that we start seeing in that telematic space. >>This looks more complicated than it is. So bear with me for a second. This is a commercial example because we see a data work. A lot of data were going on in commercial insurance. It's not Leah personal insurance thing. Commercial is near and dear to my heart. That's where I started. I actually, for a long time, worked in global energy insurance. So what this one wheelie explains is how we can use sensors on people's outfits and people's clothes to manage risks and underwrite risks better. So there are programs now for manufacturing companies and for oil and gas, where the people that work in those places are having sensors as part of their work outfits. And it does a couple of things. It helps in workers' comp underwriting and claims because you can actually see where people are moving, what they are doing, how long they're working. >>Some of them even tracks some very basic health-related information like blood pressure and heartbeat and stuff like that, temperature. Um, so those are all good things. The other thing that had to us, it helps, um, it helps collect data on the specific risks and exposures. Again, we're getting more and more to individual underwriting or individual risk underwriting, who insurance companies that, that ensure these, these, um, commercial, commercial, um, enterprises. So they started giving discounts if the workers were sensors and ultimately if there is an unfortunate event and it like a big accident or big loss, it helps, uh, first responders very quickly identify where those workers are. And, and, and if, and how they're moving, which is all very important to figure out who to help first in case something bad happens. Right? So these are the type of data that quite often got implements in one specific use case, and then get broadly moved to other use cases or deployed into other use cases to help price risks, betters better, and keep, you know, risks, better control, manage, and provide preventative care. Right? >>So these were some of the use cases that we run in the underwriting space that are very excited to talk about. So as a next step, what we would like you to do is considered opportunities in your own companies to advance risk assessment specific to your individual customer's need. And again, customers can be people they can be enterprises to can be other any, any insurable entity, right? The please physical dera.com solutions insurance, where you will find all our documentation assets and thought leadership around the topic. And if you ever want to chat about this, please give me a call or schedule a meeting with us. I get very passionate about this topic. I'll gladly talk to you forever. If you happen to be based in the us and you ever need somebody to filibuster on insurance, please give me a call. I'll easily fit 24 hours on this one. Um, so please schedule a call with me. I promise to keep it short. So thank you very much for joining this session. And as a last thing, I would like to remind all of you read our blogs, read our tweets. We'd our thought leadership around insurance. And as we all know, insurance is sexy.

>> Hi, and this is theCUBE on Cloud. I'm Stu Miniman and really excited to welcome to a special Fireside Chat. CUBE Alumni has been on the program so many times. We always love talking to founders. We like talking to deep thinkers and that's why he was one of the early ones that I reached out to when we were working on this event. When we first started conversations, we were looking at how hyperscalers really were taking adoption of the brand new technologies, things like flash, things like software defined networking, and how that would invade the enterprise. That of course has had a huge impact, help create a category called hyperconverged infrastructure and I'm talking about Dheeraj Pandey. He is the founder, chairman, and CEO of Nutanix, taking HCI from hyperconverged infrastructure to hybrid cloud infrastructure. So Dheeraj, welcome to the Fireside Chat. Thank you so much for joining us. >> Thank you, Stu, and thank you for the last 10 years that we've grown together, both theCUBE and Nutanix and myself as a leader in the last 10 years. So bringing HCI from hyperconverged to hybrid cloud just reminds me of how the more things change, the more they remain the same. So looking forward to a great discussion here. >> So talk about that early discussion, what the hyperscalers were doing, how can the enterprise take advantage of that? Over time, enterprise has matured and looked a little bit more like the hyperscalers. Hybrid cloud of course is on everyone's lip, as well as we've seen the hyperscalers themselves look more and more like the enterprise. So hybrid and multicloud is where we are today. We think it'll be in the future. But give us a little bit as to how you've seen that progression today and where are we going down the road here? >> Yeah, I think I talked about this during my .NEXT keynote. And the whole idea of, in every recession, we make things smaller. In '91 we said we're going to go away from mainframes into Unix servers. And we made the unit of compute smaller. Then in the year 2000 when there was the next bubble burst and the recession afterwards, we moved from Unix servers to Wintel: Windows and Intel, x86 and eventually Linux as well. Again we made things smaller going from million dollar servers to $5,000 servers, shorter lived servers. And that's what we did in 2008/2009. I said, look, we don't even need to buy servers. We can do things with virtual machines which are servers that are an incarnation in the digital world. There is nothing in the physical world that actually went lives. But we made it even smaller. And now with cloud in the last three, four years and what will happen in this coming decade, they're going to make it even smaller, not just in space which is size with functions and containers and virtual machines, but also in time. So space and time, we're talking about hourly billing and monthly billing and a one-year term as opposed to really going and committing to five or seven years of hardware and CapEx. So I think as you make things smaller, I mean, and this is true for as consumers, we have short attention spans, things are going fast. The cycle of creative destruction of virtual machines is shrinking as well. So I think in many cases, we know we've gone and created this autonomy, massive sprawl. Like we created a massive sprawl of Intel servers back in '95 and 2005. Then we have to use virtualization to go and consolidate all of it, created beautiful data centers of Intel servers with VMware software. And then we created a massive sprawl of data centers, of consolidated data centers with one click private cloud in the last five years and hopefully in the next five too. But I think we're also now creating a proliferation of clouds. There is a sprawl, massive sprawl of cost centers and such. So we need yet another layer of software for governance to reign in on that chaos, hence the need for a new HCI, hybrid cloud infrastructure. >> Yeah, it's fascinating to kind of watch that progression over time. There was a phenomenal Atlantic article. I think it was from like the 1940s or 1950s where somebody took what was happening post-World War II and projected things out. We're talking really pre the internet, but just the miniaturization and the acceleration, kind of the Moore's law discussion. If you take things out, where it would go. When I talked to Amazon, they said the one thing that we know for sure, I'm talking to Amazon.com is that people will want it faster and cheaper in the future. I don't know which robot or drone or things that they have. But absolutely there are those certain characteristics. So from a leadership standpoint, Dheeraj, talk about these changes? We had the wave of virtualization, the wave of containerization, you talked about functions in serverless. Those are tools. But at the end of the day, it's about the outcomes and how do we take advantage of things? So how as a leader do you make sure that you know where to take the company as these technology waves and changes impact what you're doing? >> Yeah, it's a great point. I mean, we celebrate things in IT a lot, but we don't talk about what does it take? What's the underlying fabric to really use these things successfully and better than others and not just use buzzwords, because new buzzwords will come in the next three years. For example AI and ML has been a great buzzword for the last three, four years. But there's very few companies, probably less than even half a percent who know how to leverage machine learning, even understand the difference between machine learning and AI. And a lot of it comes down to a few principles. There's a culture principles, not the least of which is how you celebrate failure, because now you're doing shorter, smaller things. You've got a more agile, you'll have more velocity. Gone are the days of waterfall where you're doing yearly planning and pre-year releases and such. So as we get into this new world, not everything will be perfect, and you've got to really learn to pick yourself up and recover quickly, heal quickly and such. So that is the fundamental tenet of Silicon Valley. And we got to really go and use this more outside the Valley as well in every company out there. Whether it's East Coast company, the Midwest company that are outside the U.S. I think this idea that you will be vulnerable, more vulnerable as you go and learn to do things faster and shorter. I think product management is a term that we don't fully understand, and this is about the why before the how and the what. We quickly jump to the what: containers and functions and databases, servers, and AI, and ML, they're the what. But how do you really start with the why? You know my fascination for one of my distant mentors, Simon Sinek and how he thinks about most companies just focusing on the what, while very few actually start with why, then the how, then the what itself. And product management has to play a key role in this, which also subsumes design, thinking about simplification and elegance and reducing friction. I think again, very few companies, probably no more than 1% of the companies really understand what it means to start with design and APIs, user experience APIs for developers before you even get to writing any single line of code. So I think to me, that's leadership. When you can stay away from instant gratification of the end result, but start with the why, then the how, then the what. >> Yeah, as we know in the technology space, oftentimes the technology is the easy part. It's helping to drive that change. I think back to the early days when we were talking, it was, hyperconverge, it was a threat to storage. We're going to put you out of a job. And we'd always go and say, "Look, no, no, no. We're not putting you out of a job. We're going to free you up to do the things that you want to do. That security project that's been sitting on the shelf for six months, you can go do that. Helping build new parts of the business. Those things that you can do." It's that shifting a mindset can be so difficult. And Dheeraj, I mean, you look at 2020, everyone has had to shift their mindset for everything. I was spending half my time on the road. I don't miss the hotels. I do miss seeing lots and lots of people in person. So what's your advice for people, how they can stay malleable, be open to some change? What are you seeing out there? What advice do you give there? >> Yeah, I think, as you said, inertia is at the core of most things in our lives, including what we saw in healthcare for the last 20, 30 years. I mean, there was so much regulation. The doctor's community had to move forward, nurses had to move forward. I mean, not just providers, but insurance companies. And finally, all of a sudden, we're talking about telehealth because of the pandemic. We are talking about online learning. I mean the things that higher ed refused to do. I mean if you think about the last 20 years of what had happened with the cost of higher ed, I mean it's 200% growth when the cost of television has gone down by probably 100, 200% with more features. Healthcare, higher ed, education in general, all of a sudden is coming for this deep shock because of the pandemic. And I think it's these kind of black swan moments that really changed the world. And I know it's a cliche to say this. But I feel like we are going to be in a new normal, and we have been forced to this new change of digital. I mean, you and I are sitting and talking over the internet. It's a little awkward right now because there's a little bit of a delay in the way I'm looking at things. But I know it's going to directionally be right. I mean, we will go in a way where it just become seamless over time. So change is the only constant. And I believe that I think what we've seen in the pandemic is just the beginning of what digital will mean going forward. And I think the more people embrace it, the faster we do it. Speed is going to be the name of the game when it comes to survival and thriving in this new age. >> Dheeraj, it's interesting. We do hope, I'm a technologist. I know you're an optimist when it comes to things. So we always look at those silver linings. Like I hope healthcare and education will be able to move forward fast. Higher education costs, inequity out there for access to medicine. It would be wonderful if we could help solve some of that, despite this global pandemic. One of the other results, Dheeraj, we talked about some very shifts in the marketplace, the large tech players really have emerged in winter so far in 2020. I can't help, but watch the stock market. And Apple is bigger than ever, Amazon, Google, all ended up in front of Congress to talk about if they've gotten too big. You've partnered with Amazon, Microsoft, and Google. They are potentially a threat but also a partner. From your standpoint, have they gotten too much power? Do we have an inequity in the tech world that they are creating the universes that they will just kind of block off and limit innovation? What's your take on big tech? >> Yeah, I mean, I feel like there's always been big something. I mean, if you go back to the '90s, Amazon, not Amazon, IBM was big, and Microsoft was big, and AT&T was big. I mean, there's always been big companies because the consumer effect that they've had as well, I mean. And I think what we're seeing right now is no different. I mean, at the end of the day, the great thing about this country is that there's always disruption happening. And sometimes small is way better and way more competitive than big. Now at the same time, I do look up to the way some of them have organized themselves. Like the way Amazon has organized itself is really unique and creative with general managers and very independent, highly autonomous groups. So some of these organizations will definitely survive and thrive in scale. And yet for others, I think decision-making and staying competitive and staying scrappy will come a lot harder. So to me when I look at these big names and what Congress is talking about and such, I feel like there's no different than 20, 30, 40 years ago. I mean, we talked about Rockefeller and the oil giants back from 100 years ago. And so in many ways, I mean, the more things change, the more they remain the same. All we have to do is we have to walk over to where the customer is. And that's what we've done with the partnerships. Like in Amazon and Azure, we're saying look, we can even use your commits and credits. I mean, that is a very elegant way to go to where the customer is, rather than force them to where we are. And the public cloud is facing this too. They've come to realize in the last two years that they cannot force all of enterprise computing to come to hyperscalers data centers. They'll have to take in these bite-size smaller clouds to where the customer is, where the customer's machines are, where the customers people are, where the customers data is. That's where we also take to disperse the cloud itself. So I think there's going to be a yin yang where we'll try to walk with the customer to where we want them to be, whether it's hyperscaler data center or the notion of hybrid cloud infrastructure. But many a time, we've got to walk over to where they are. I mean, and outside the U.S, I mean, the cloud is such a nuanced word. I mean, we're talking about sovereignty, we're talking about data gravity, we're talking about economics of owning versus renting. This trifecta, the laws of the land, the laws of physics, and the laws of economics will dictate many of these things as well. So I think the big folks are also humble and vulnerable to realize that there's nothing more powerful than market forces. And I think the rest will take care of itself. >> Yeah, my quick commentary on that, Dheeraj, I think most of us look back at AT&T and felt the government got it wrong. The way they broke it up and ended up consolidating back together, it didn't necessarily help consumers. Microsoft on the other hand might've had a little bit too much power and was leveraging that against competition and really squashing innovation. So in general, it's good to see that the politics are looking at that and chore felt. The last time I watched things, they were a little bit more educated than some previous times there, where it was almost embarrassing to watch our representatives fumbling around with technology. So it's always good to question authority, question what they have. And one of the things you've brought up many times is you're open to listening and you're bringing in new ideas. I remember one conversation I had with you is there's that direction that you hold on to, but you will assess and do new data. You've made adjustments in the product portfolio and direction based on your customers, based on the ecosystem. And you've mentioned some of the, bring thoughts that you've brought into the company and you share. So you mentioned black swan that seem to head you brought to one of the European .NEXT shows. It was great to be able to see that author and read through advisors like Condoleezza Rice who you've had at the conferences a couple of times. Where are you getting some of your latest inspiration from, any new authors or podcasts that you'd be recommending to the audience? >> Yeah, I look at adjacencies, obviously Simon has been great. He was .NEXT, talked about the Infinite Game. And we'll talk about the Infinite Game with Nutanix too with respect to also my decision. But Brene Brown was been very close to Nutanix. I was just looking at her latest podcast, and she was sitting with the author of Stretch, Scott Sonnenschein, and it's a fascinating read and a great listen, by the way, I think for worth an hour, talking about scrappiness, and talking about resourcefulness. What does it mean to really be resourceful? And we need that even more so as we go through this recession, as we are sheltered in place. I think it's an adjacency to everything that Brene does. And I was just blown away by just listening to it. I'd a love for others to even have a listen and learn to understand what we can do within our families, with our budgets, with our companies, with our startups. I mean, with CUBE, I mean, what does it mean to be scrappy? And celebrate scrappiness and resourcefulness, more so than AI always need more. I think I just found it fascinating in the last week itself listening through it. >> John Farinacci talk many times that founder, startup, that being able to pull themselves up, be able to drive forward, overcome obstacles. So Dheeraj, do you tee it up? It sounds like is the next step for you. There's a transition under discussion. Bain has made an investment. There's a search for new CEO. Are you saying there's a book club in your future to be able to get things ready? Why don't you explain a little bit, 11 years took the company public, over 6,500 employees public company. So tell us a little bit about that decision-making process and what you expect to see in the future? >> Yeah, it's probably one of the hardest things as an entrepreneur is to let go, because it's a creation that you followed from scratch, from nothing. And it was a process for me to rethink about what's next for the company and then what's next for me? And me and the company were so tightly coupled that I was like, wow, at some point, this has to be a little bit more like the way Bill Gates did it with Microsoft, and there's going to be buton zone and you will then start to realize that your identity is different from the company's identity. And maybe the company is built for bigger, better things. And maybe you're built for bigger, better things. And how do you really start to first do this decoupling of the identity? And it's really hard. I mean, I'm sure that parents go through this. I mean, our children are still very young. Our eldest is nine going on 10 and our twin girls are six. I know at some point in the next 10 years, eight to 10 years, we'll have to figure out what it means to let go. And I'm already doing this with my son. I tell him you're born free. I mean, the word born free which drives my wife crazy sometimes. I say this to them, it's about independence. And I think the company is also born free to really think about a life outside of me, as well outside of founder. And that was a very important process for me as I was talking to the board for the last six, seven, eight months. And when the Bain deal came in, I thought it was a great time. We ended the fiscal really well, all things considered. We had a good quarter. The transition has been a journey of a lifetime, the business model transition I speak of. Really three years, I mean, I have aged probably 10 years in these last three years. But I think I would not replaced it for anything. Just the experience of learning what it means to change as a public company when you have short-term goals and long-term goals, we need the conviction, knowing what's right, because otherwise we would not have survived this cloud movement, all this idea of actually becoming a subscription company, changing the core of the business in the on-prem world itself. It's a king to change the wings of a plane at 40,000 feet where none of the passengers blink. It's been phenomenal ride last 11 years, but it's also been nonstop monomaniacal. I mean, I use the word marathon for this, and I figured it's a good time to say figure out a way to let go of this, and think of what's bigger better for Nutanix. And going from zero to a billion six in annual billings, and looking at billion six to 3 billion to four to five, I think it'd be great &to look at this from afar. And at the same time, I think there's vulnerability. I mean, I've made the company vulnerable. I've made myself vulnerable. We don't know who the next leader will be. And I think the next three to six months is one of the most important baton zones that I have ever experienced to be a part of. So looking forward to make sure that baton doesn't fall, redefine what good to great looks like, both for the company and for myself. And at the same time, go read more. I mean, I've been passionate about developers in the last 10 years, 11 years. I was a developer myself. This company, Nutanix, was really built by developers for IT. And I'm learning more about the developer as a consumer. How do you think about their experience? Not just the things that we throw at them from open source point of view and from cloud and technologies and AI and ML point of view, but really their lives, having them think about revenue and business and really blurring the lines between architects and product managers and developers. I think it's just an unfathomable problem we've created in IT that I would love to go and read and write more about. >> Yeah, so many important things you said there. I absolutely think that there are certain things everybody of course will think of you for a long time with Nutanix, but there is that separation between the role in the company and the person itself, and really appreciated how much you've always shared along those lines. So last question I have and you hit it up a little bit when you talked about developers. Take off your Nutanix hat for a second here, now what do we need to do to make sure that the next decade is successful in this space, cloud as a general guideline? Yes, we know we have skill gap. We know we need more people, we need more diversity. But there's so much that we need and there's so much opportunity, but what do you see and any advice areas that you think are critical for success in the future? >> Yeah, I mean, you hit up on something that I have had a passion for, probably more late in this world, more so than conspicuous, and and you hit upon it right now, diversity and inclusion. It's an unresolved problem in the developer community: the black developer, the woman developer. The idea of, I mean, we've two girls, they're twins. I'd love for them to embrace computer science and even probably do a PhD. I mean, I was a dropout. I'd love for them to do better than I did. Get, embrace things that are adjacent to biology and computer science. Go solve really hard problems. And we've not done those things. I mean, we've not looked at the community of developers and said, you know, they are the maker. And they work with managers and the maker manager world is two different worlds. How do you make this less friction? And how do you make this more delightful? And how do you think of developers as business, as if they are the folks who run the business? I think there's a lot that's missing there. And again, we throw a lot of jargons at them, and we talk a lot about automation and tools and such. But those are just things. I think the last 10, 11 years of me really just thinking about product and product portfolio and design and the fact that we have so many developers at Nutanix. I think it has been a mind-boggling experience, thinking about the why and the how and the what of the day in the life of, the month in the life of, and thinking about simple things like OKRs. I mean, we are throwing these jargons of OKRs at them: productivity, offshoring, remote work, over the zoom design sessions. It's just full of conflict and friction. So I think there is an amazing opportunity for Nutanix. There's an amazing opportunity for the industry to elevate this where the the woman developer can speak up in this world that's full of so many men. The black developer can speak up. And all of us can really think of this as something that's more structured, more productive, more revenue-driven, more customer in rather than developer out. That's really been some of the things that have been in my head, things that are still unresolved at Nutanix that I'm pretty sure at many of the places out there. That's what thinking and reading and writing about. >> Well, Dheeraj, first of all, thank you so much again for participating here. It's been great having you in theCUBE community, almost since the inception of us doing it back in 2010. Wish you the best of luck in the current transition. And absolutely look forward to talking more in the future. >> Thank you. And again, a big fan of the tremor rate of John, Dave, and you. Always learn so much from you, folks. Looking forward to be a constant student. Thank you. >> Thank you for joining us at theCUBE on Cloud. Lots more coverage here. Be sure to look throughout the site, engage in the chats, and give us your feedback. We're here to help you with the virtual events. I'm Stu Miniman as always. Thanks for watching.

>> Hi, and this is theCUBE on Cloud. I'm Stu Miniman and really excited to welcome to a special Fireside Chat. CUBE Alumni has been on the program so many times. We always love talking to founders. We like talking to deep thinkers and that's why he was one of the early ones that I reached out to when we were working on this event. When we first started conversations, we were looking at how hyperscalers really were taking adoption of the brand new technologies, things like flash, things like software defined networking, and how that would invade the enterprise. That of course has had a huge impact, help create a category called hyperconverged infrastructure and I'm talking about Dheeraj Pandey. He is the founder, chairman, and CEO of Nutanix, taking HCI from hyperconverged infrastructure to hybrid cloud infrastructure. So Dheeraj, welcome to the Fireside Chat. Thank you so much for joining us. >> Thank you, Stu, and thank you for the last 10 years that we've grown together, both theCUBE and Nutanix and myself as a leader in the last 10 years. So bringing HCI from hyperconverged to hybrid cloud just reminds me of how the more things change, the more they remain the same. So looking forward to a great discussion here. >> So talk about that early discussion, what the hyperscalers were doing, how can the enterprise take advantage of that? Over time, enterprise has matured and looked a little bit more like the hyperscalers. Hybrid cloud of course is on everyone's lip, as well as we've seen the hyperscalers themselves look more and more like the enterprise. So hybrid and multicloud is where we are today. We think it'll be in the future. But give us a little bit as to how you've seen that progression today and where are we going down the road here? >> Yeah, I think I talked about this during my .NEXT keynote. And the whole idea of, in every recession, we make things smaller. In '91 we said we're going to go away from mainframes into Unix servers. And we made the unit of compute smaller. Then in the year 2000 when there was the next bubble burst and the recession afterwards, we moved from Unix servers to Wintel: Windows and Intel, x86 and eventually Linux as well. Again we made things smaller going from million dollar servers to $5,000 servers, shorter lived servers. And that's what we did in 2008/2009. I said, look, we don't even need to buy servers. We can do things with virtual machines which are servers that are an incarnation in the digital world. There is nothing in the physical world that actually went lives. But we made it even smaller. And now with cloud in the last three, four years and what will happen in this coming decade, they're going to make it even smaller, not just in space which is size with functions and containers and virtual machines, but also in time. So space and time, we're talking about hourly billing and monthly billing and a one-year term as opposed to really going and committing to five or seven years of hardware and CapEx. So I think as you make things smaller, I mean, and this is true for as consumers, we have short retention spans, things are going fast. The cycle of creative destruction of virtual machines is shrinking as well. So I think in many cases, we know we've gone and created this autonomy, massive sprawl. Like we created a massive sprawl of Intel servers back in '95 and 2005. Then we have to use virtualization to go and consolidate all of it, created beautiful data centers of Intel servers with VMware software. And then we created a massive sprawl of data centers, of consolidated data centers with one click private cloud in the last five years and hopefully in the next five too. But I think we're also now creating a proliferation of clouds. There is a sprawl, massive sprawl of cost centers and such. So we need yet another layer of software for governance to reign in on that chaos, hence the need for a new HCI, hybrid cloud infrastructure. >> Yeah, it's fascinating to kind of watch that progression over time. There was a phenomenal Atlantic article. I think it was from like the 1940s or 1950s where somebody took what was happening post-World War II and projected things out. We're talking really pre the internet, but just the miniaturization and the acceleration, kind of the Moore's law discussion. If you take things out, where it would go. When I talked to Amazon, they said the one thing that we know for sure, I'm talking to Amazon.com is that people will want it faster and cheaper in the future. I don't know which robot or drone or things that they have. But absolutely there are those certain characteristics. So from a leadership standpoint, Dheeraj, talk about these changes? We had the wave of virtualization, the wave of containerization, you talked about functions in serverless. Those are tools. But at the end of the day, it's about the outcomes and how do we take advantage of things? So how as a leader do you make sure that you know where to take the company as these technology waves and changes impact what you're doing? >> Yeah, it's a great point. I mean, we celebrate things in IT a lot, but we don't talk about what does it take? What's the underlying fabric to really use these things successfully and better than others and not just use buzzwords, because new buzzwords will come in the next three years. For example AI and ML has been a great buzzword for the last three, four years. But there's very few companies, probably less than even half a percent who know how to leverage machine learning, even understand the difference between machine learning and AI. And a lot of it comes down to a few principles. There's a culture principles, not the least of which is how you celebrate failure, because now you're doing shorter, smaller things. You've got a more agile, you'll have more velocity. Gone are the days of waterfall where you're doing yearly planning and pre-year releases and such. So as we get into this new world, not everything will be perfect, and you've got to really learn to pick yourself up and recover quickly, heal quickly and such. So that is the fundamental tenet of Silicon Valley. And we got to really go and use this more outside the Valley as well in every company out there. Whether it's East Coast company, the Midwest company that are outside the U.S. I think this idea that you will be vulnerable, more vulnerable as you go and learn to do things faster and shorter. I think product management is a term that we don't fully understand, and this is about the why before the how and the what. We quickly jump to the what: containers and functions and databases, servers, and AI, and ML, they're the what. But how do you really start with the why? You know my fascination for one of my distant mentors, Simon Sinek and how he thinks about most companies just focusing on the what, while very few actually start with why, then the how, then the what itself. And product management has to play a key role in this, which also subsumes design, thinking about simplification and elegance and reducing friction. I think again, very few companies, probably no more than 1% of the companies really understand what it means to start with design and APIs, user experience APIs for developers before you even get to writing any single line of code. So I think to me, that's leadership. When you can stay away from instant gratification of the end result, but start with the why, then the how, then the what. >> Yeah, as we know in the technology space, oftentimes the technology is the easy part. It's helping to drive that change. I think back to the early days when we were talking, it was, hyperconverge, it was a threat to storage. We're going to put you out of a job. And we'd always go and say, "Look, no, no, no. We're not putting you out of a job. We're going to free you up to do the things that you want to do. That security project that's been sitting on the shelf for six months, you can go do that. Helping build new parts of the business. Those things that you can do." It's that shifting a mindset can be so difficult. And Dheeraj, I mean, you look at 2020, everyone has had to shift their mindset for everything. I was spending half my time on the road. I don't miss the hotels. I do miss seeing lots and lots of people in person. So what's your advice for people, how they can stay malleable, be open to some change? What are you seeing out there? What advice do you give there? >> Yeah, I think, as you said, inertia is at the core of most things in our lives, including what we saw in healthcare for the last 20, 30 years. I mean, there was so much regulation. The doctor's community had to move forward, nurses had to move forward. I mean, not just providers, but insurance companies. And finally, all of a sudden, we're talking about telehealth because of the pandemic. We are talking about online learning. I mean the things that higher ed refused to do. I mean if you think about the last 20 years of what had happened with the cost of higher ed, I mean it's 200% growth when the cost of television has gone down by probably 100, 200% with more features. Healthcare, higher ed, education in general, all of a sudden is coming for this deep shock because of the pandemic. And I think it's these kind of black swan moments that really changed the world. And I know it's a cliche to say this. But I feel like we are going to be in a new normal, and we have been forced to this new change of digital. I mean, you and I are sitting and talking over the internet. It's a little awkward right now because there's a little bit of a delay in the way I'm looking at things. But I know it's going to directionally be right. I mean, we will go in a way where it just become seamless over time. So change is the only constant. And I believe that I think what we've seen in the pandemic is just the beginning of what digital will mean going forward. And I think the more people embrace it, the faster we do it. Speed is going to be the name of the game when it comes to survival and thriving in this new age. >> Dheeraj, it's interesting. We do hope, I'm a technologist. I know you're an optimist when it comes to things. So we always look at those silver linings. Like I hope healthcare and education will be able to move forward fast. Higher education costs, inequity out there for access to medicine. It would be wonderful if we could help solve some of that, despite this global pandemic. One of the other results, Dheeraj, we talked about some very shifts in the marketplace, the large tech players really have emerged in winter so far in 2020. I can't help, but watch the stock market. And Apple is bigger than ever, Amazon, Google, all ended up in front of Congress to talk about if they've gotten too big. You've partnered with Amazon, Microsoft, and Google. They are potentially a threat but also a partner. From your standpoint, have they gotten too much power? Do we have an inequity in the tech world that they are creating the universes that they will just kind of block off and limit innovation? What's your take on big tech? >> Yeah, I mean, I feel like there's always been big something. I mean, if you go back to the '90s, Amazon, not Amazon, IBM was big, and Microsoft was big, and AT&T was big. I mean, there's always been big companies because the consumer effect that they've had as well, I mean. And I think what we're seeing right now is no different. I mean, at the end of the day, the great thing about this country is that there's always disruption happening. And sometimes small is way better and way more competitive than big. Now at the same time, I do look up to the way some of them have organized themselves. Like the way Amazon has organized itself is really unique and creative with general managers and very independent, highly autonomous groups. So some of these organizations will definitely survive and thrive in scale. And yet for others, I think decision-making and staying competitive and staying scrappy will come a lot harder. So to me when I look at these big names and what Congress is talking about and such, I feel like there's no different than 20, 30, 40 years ago. I mean, we talked about Rockefeller and the oil giants back from 100 years ago. And so in many ways, I mean, the more things change, the more they remain the same. All we have to do is we have to walk over to where the customer is. And that's what we've done with the partnerships. Like in Amazon and Azure, we're saying look, we can even use your commits and credits. I mean, that is a very elegant way to go to where the customer is, rather than force them to where we are. And the public cloud is facing this too. They've come to realize in the last two years that they cannot force all of enterprise computing to come to hyperscalers data centers. They'll have to take in these bite-size smaller clouds to where the customer is, where the customer's machines are, where the customers people are, where the customers data is. That's where we also take to disperse the cloud itself. So I think there's going to be a yin yang where we'll try to walk with the customer to where we want them to be, whether it's hyperscaler data center or the notion of hybrid cloud infrastructure. But many a time, we've got to walk over to where they are. I mean, and outside the U.S, I mean, the cloud is such a nuanced word. I mean, we're talking about sovereignty, we're talking about data gravity, we're talking about economics of owning versus renting. This trifecta, the laws of the land, the laws of physics, and the laws of economics will dictate many of these things as well. So I think the big folks are also humble and vulnerable to realize that there's nothing more powerful than market forces. And I think the rest will take care of itself. >> Yeah, my quick commentary on that, Dheeraj, I think most of us look back at AT&T and felt the government got it wrong. The way they broke it up and ended up consolidating back together, it didn't necessarily help consumers. Microsoft on the other hand might've had a little bit too much power and was leveraging that against competition and really squashing innovation. So in general, it's good to see that the politics are looking at that and chore felt. The last time I watched things, they were a little bit more educated than some previous times there, where it was almost embarrassing to watch our representatives fumbling around with technology. So it's always good to question authority, question what they have. And one of the things you've brought up many times is you're open to listening and you're bringing in new ideas. I remember one conversation I had with you is there's that direction that you hold on to, but you will assess and do new data. You've made adjustments in the product portfolio and direction based on your customers, based on the ecosystem. And you've mentioned some of the, bring thoughts that you've brought into the company and you share. So you mentioned black swan that seem to head you brought to one of the European .NEXT shows. It was great to be able to see that author and read through advisors like Condoleezza Rice who you've had at the conferences a couple of times. Where are you getting some of your latest inspiration from, any new authors or podcasts that you'd be recommending to the audience? >> Yeah, I look at adjacencies, obviously Simon has been great. He was .NEXT, talked about the Infinite Game. And we'll talk about the Infinite Game with Nutanix too with respect to also my decision. But Brene Brown was been very close to Nutanix. I was just looking at her latest podcast, and she was sitting with the author of Stretch, Scott Sonnenschein, and it's a fascinating read and a great listen, by the way, I think for worth an hour, talking about scrappiness, and talking about resourcefulness. What does it mean to really be resourceful? And we need that even more so as we go through this recession, as we are sheltered in place. I think it's an adjacency to everything that Brene does. And I was just blown away by just listening to it. I'd a love for others to even have a listen and learn to understand what we can do within our families, with our budgets, with our companies, with our startups. I mean, with CUBE, I mean, what does it mean to be scrappy? And celebrate scrappiness and resourcefulness, more so than AI always need more. I think I just found it fascinating in the last week itself listening through it. >> John Farinacci talk many times that founder, startup, that being able to pull themselves up, be able to drive forward, overcome obstacles. So Dheeraj, do you tee it up? It sounds like is the next step for you. There's a transition under discussion. Bain has made an investment. There's a search for new CEO. Are you saying there's a book club in your future to be able to get things ready? Why don't you explain a little bit, 11 years took the company public, over 6,500 employees public company. So tell us a little bit about that decision-making process and what you expect to see in the future? >> Yeah, it's probably one of the hardest things as an entrepreneur is to let go, because it's a creation that you followed from scratch, from nothing. And it was a process for me to rethink about what's next for the company and then what's next for me? And me and the company were so tightly coupled that I was like, wow, at some point, this has to be a little bit more like the way Bill Gates did it with Microsoft, and there's going to be buton zone and you will then start to realize that your identity is different from the company's identity. And maybe the company is built for bigger, better things. And maybe you're built for bigger, better things. And how do you really start to first do this decoupling of the identity? And it's really hard. I mean, I'm sure that parents go through this. I mean, our children are still very young. Our eldest is nine going on 10 and our twin girls are six. I know at some point in the next 10 years, eight to 10 years, we'll have to figure out what it means to let go. And I'm already doing this with my son. I tell him you're born free. I mean, the word born free which drives my wife crazy sometimes. I say this to them, it's about independence. And I think the company is also born free to really think about a life outside of me, as well outside of founder. And that was a very important process for me as I was talking to the board for the last six, seven, eight months. And when the Bain deal came in, I thought it was a great time. We ended the fiscal really well, all things considered. We had a good quarter. The transition has been a journey of a lifetime, the business model transition I speak of. Really three years, I mean, I have aged probably 10 years in these last three years. But I think I would not replaced it for anything. Just the experience of learning what it means to change as a public company when you have short-term goals and long-term goals, we need the conviction, knowing what's right, because otherwise we would not have survived this cloud movement, all this idea of actually becoming a subscription company, changing the core of the business in the on-prem world itself. It's a king to change the wings of a plane at 40,000 feet where none of the passengers blink. It's been phenomenal ride last 11 years, but it's also been nonstop monomaniacal. I mean, I use the word marathon for this, and I figured it's a good time to say figure out a way to let go of this, and think of what's bigger better for Nutanix. And going from zero to a billion six in annual billings, and looking at billion six to 3 billion to four to five, I think it'd be great &to look at this from afar. And at the same time, I think there's vulnerability. I mean, I've made the company vulnerable. I've made myself vulnerable. We don't know who the next leader will be. And I think the next three to six months is one of the most important baton zones that I have ever experienced to be a part of. So looking forward to make sure that baton doesn't fall, redefine what good to great looks like, both for the company and for myself. And at the same time, go read more. I mean, I've been passionate about developers in the last 10 years, 11 years. I was a developer myself. This company, Nutanix, was really built by developers for IT. And I'm learning more about the developer as a consumer. How do you think about their experience? Not just the things that we throw at them from open source point of view and from cloud and technologies and AI and ML point of view, but really their lives, having them think about revenue and business and really blurring the lines between architects and product managers and developers. I think it's just an unfathomable problem we've created in IT that I would love to go and read and write more about. >> Yeah, so many important things you said there. I absolutely think that there are certain things everybody of course will think of you for a long time with Nutanix, but there is that separation between the role in the company and the person itself, and really appreciated how much you've always shared along those lines. So last question I have and you hit it up a little bit when you talked about developers. Take off your Nutanix hat for a second here, now what do we need to do to make sure that the next decade is successful in this space, cloud as a general guideline? Yes, we know we have skill gap. We know we need more people, we need more diversity. But there's so much that we need and there's so much opportunity, but what do you see and any advice areas that you think are critical for success in the future? >> Yeah, I mean, you hit up on something that I have had a passion for, probably more late in this world, more so than conspicuous, and and you hit upon it right now, diversity and inclusion. It's an unresolved problem in the developer community: the black developer, the woman developer. The idea of, I mean, we've two girls, they're twins. I'd love for them to embrace computer science and even probably do a PhD. I mean, I was a dropout. I'd love for them to do better than I did. Get, embrace things that are adjacent to biology and computer science. Go solve really hard problems. And we've not done those things. I mean, we've not looked at the community of developers and said, you know, they are the maker. And they work with managers and the maker manager world is two different worlds. How do you make this less friction? And how do you make this more delightful? And how do you think of developers as business, as if they are the folks who run the business? I think there's a lot that's missing there. And again, we throw a lot of jargons at them, and we talk a lot about automation and tools and such. But those are just things. I think the last 10, 11 years of me really just thinking about product and product portfolio and design and the fact that we have so many developers at Nutanix. I think it has been a mind-boggling experience, thinking about the why and the how and the what of the day in the life of, the month in the life of, and thinking about simple things like OKRs. I mean, we are throwing these jargons of OKRs at them: productivity, offshoring, remote work, over the zoom design sessions. It's just full of conflict and friction. So I think there is an amazing opportunity for Nutanix. There's an amazing opportunity for the industry to elevate this where the the woman developer can speak up in this world that's full of so many men. The black developer can speak up. And all of us can really think of this as something that's more structured, more productive, more revenue-driven, more customer in rather than developer out. That's really been some of the things that have been in my head, things that are still unresolved at Nutanix that I'm pretty sure at many of the places out there. That's what thinking and reading and writing about. >> Well, Dheeraj, first of all, thank you so much again for participating here. It's been great having you in theCUBE community, almost since the inception of us doing it back in 2010. Wish you the best of luck in the current transition. And absolutely look forward to talking more in the future. >> Thank you. And again, a big fan of the tremor rate of John, Dave, and you. Always learn so much from you, folks. Looking forward to be a constant student. Thank you. >> Thank you for joining us at theCUBE on Cloud. Lots more coverage here. Be sure to look throughout the site, engage in the chats, and give us your feedback. We're here to help you with the virtual events. I'm Stu Miniman as always. Thanks for watching.

>> Hello everyone. On today's panel, the theme is Drug Discovery and how Artificial Intelligence can make a difference. On the panel today, we are honored to have Dr. Ryan Yates, principal scientist at The National Center for Natural Products Research, with a focus on botanicals specifically the pharmacokinetics, which is essentially how the drug changes over time in our body and pharmacodynamics which is essentially how drugs affects our body. And of particular interest to him is the use of AI in preclinical screening models to identify chemical combinations that can target chronic inflammatory processes such as fatty liver disease, cognitive impairment and aging. Welcome, Ryan. Thank you for coming. >> Good morning. Thank you for having me. >> The other distinguished panelist is Dr. Rangan Sukumar, our very own, is a distinguished technologist at the CTO office for High Performance Computing and Artificial Intelligence with a PHD in AI and 70 publications that can be applied in drug discovery, autonomous vehicles and social network analysis. Hey Rangan, welcome. Thank you for coming, by sparing the time. We have also our distinguished Chris Davidson. He is leader of our HPC and AI Application and Performance Engineering team. His job is to tune and benchmark applications, particularly in the applications of weather, energy, financial services and life sciences. Yes so particular interest is life sciences he spent 10 years in biotech and medical diagnostics. Hi Chris, welcome. Thank you for coming. >> Nice to see you. >> Well let's start with your Chris, yes, you're regularly interfaced with pharmaceutical companies and worked also on the COVID-19 White House Consortium. You know tell us, let's kick this off and tell us a little bit about your engagement in the drug discovery process. >> Right and that's a good question I think really setting the framework for what we're talking about here is to understand what is the drug discovery process. And that can be kind of broken down into I would say four different areas, there's the research and development space, the preclinical studies space, clinical trial and regulatory review. And if you're lucky, hopefully approval. Traditionally this is a slow arduous process it costs a lot of money and there's a high amount of error. Right, however this process by its very nature is highly iterate and has just huge amounts of data, right it's very data intensive, right and it's these characteristics that make this process a great target for kind of new approaches in different ways of doing things. Right, so for the sake of discussion, right, go ahead. >> Oh yes, so you mentioned data intensive brings to mind Artificial Intelligence, you know, so Artificial Intelligence making the difference here in this process, is that so? >> Right, and some of those novel approaches are actually based on Artificial Intelligence whether it's deep learning and machine learning, et cetera, you know, prime example would say, let's just say for the sake of discussion, let's say there's a brand new virus, causes flu-like symptoms, shall not be named if we focus kind of on the R and D phase, right our goal is really to identify target for the treatment and then screen compounds against it see which, you know, which ones we take forward right to this end, technologies like cryo-electron, cryogenic electron microscopy, just a form of microscopy can provide us a near atomic biomolecular map of the samples that we're studying, right whether that's a virus, a microbe, the cell that it's attaching to and so on, right AI, for instance, has been used in the particle picking aspect of this process. When you take all these images, you know, there are only certain particles that we want to take and study, right whether they have good resolution or not whether it's in the field of the frame and image recognition is a huge part of this, it's massive amounts of data in AI can be very easily, you know, used to approach that. Right, so with docking, you can take the biomolecular maps that you achieved from cryo-electron microscopy and you can take those and input that into the docking application and then run multiple iterations to figure out which will give you the best fit. AI again, right, this is iterative process it's extremely data intensive, it's an easy way to just apply AI and get that best fit doing something in a very, you know, analog manner that would just take humans very long time to do or traditional computing a very long time to do. >> Oh, Ryan, Ryan, you work at the NCNPR, you know, very exciting, you know after all, you know, at some point in history just about all drugs were from natural products yeah, so it's great to have you here today. Please tell us a little bit about your work with the pharmaceutical companies, especially when it is often that drug cocktails or what they call Polypharmacology, is the answer to complete drug therapy. Please tell us a bit more with your work there. >> Yeah thank you again for having me here this morning Dr. Goh, it's a pleasure to be here and as you said, I'm from the National Center for Natural Products Research you'll hear me refer to it as the NCNPR here in Oxford, Mississippi on the Ole Miss Campus, beautiful setting here in the South and so, what, as you said historically, what the drug discovery process has been, and it's really not a drug discovery process is really a therapy process, traditional medicine is we've looked at natural products from medicinal plants okay, in these extracts and so where I'd like to begin is really sort of talking about the assets that we have here at the NCNPR one of those prime assets, unique assets is our medicinal plant repository which comprises approximately 15,000 different medicinal plants. And what that allows us to do, right is to screen mine, that repository for activities so whether you have a disease of interest or whether you have a target of interest then you can use this medicinal plant repository to look for actives, in this case active plants. It's really important in today's environment of drug discovery to really understand what are the actives in these different medicinal plants which leads me to the second unique asset here at the NCNPR and that is our what I'll call a plant deconstruction laboratory so without going into great detail, but what that allows us to do is through a how to put workstation, right, is to facilitate rapid isolation and identification of phytochemicals in these different medicinal plants right, and so things that have historically taken us weeks and sometimes months, think acetylsalicylic acid from salicylic acid as a pain reliever in the willow bark or Taxol, right as an anti-cancer drug, right now we can do that with this system on the matter of days or weeks so now we're talking about activity from a plant and extract down to phytochemical characterization on a timescale, which starts to make sense in modern drug discovery, alright and so now if you look at these phytochemicals, right, and you ask yourself, well sort of who is interested in that and why, right what are traditional pharmaceutical companies, right which I've been working with for 20, over 25 years now, right, typically uses these natural products where historically has used these natural products as starting points for new drugs. Right, so in other words, take this phytochemical and make chemicals synthetic modifications in order to achieve a potential drug. But in the context of natural products, unlike the pharmaceutical realm, there is often times a big knowledge gap between a disease and a plant in other words I have a plant that has activity, but how to connect those dots has been really laborious time consuming so it took us probably 50 years to go from salicylic acid and willow bark to synthesize acetylsalicylic acid or aspirin it just doesn't work in today's environment. So casting about trying to figure out how we expedite that process that's when about four years ago, I read a really fascinating article in the Los Angeles Times about my colleague and business partner, Dr. Rangan Sukumar, describing all the interesting things that he was doing in the area of Artificial Intelligence. And one of my favorite parts of this story is basically, unannounced, I arrived at his doorstep in Oak Ridge, he was working Oak Ridge National Labs at the time, and I introduced myself to him didn't know what was coming, didn't know who I was, right and I said, hey, you don't know me you don't know why I'm here, I said, but let me tell you what I want to do with your system, right and so that kicked off a very fruitful collaboration and friendship over the last four years using Artificial Intelligence and it's culminated most recently in our COVID-19 project collaborative research between the NCNPR and HP in this case. >> From what I can understand also as Chris has mentioned highly iterative, especially with these combination mixture of chemicals right, in plants that could affect a disease. We need to put in effort to figure out what are the active components in that, that affects it yeah, the combination and given the layman's way of understanding it you know and therefore iterative and highly data intensive. And I can see why Rangan can play a huge significant role here, Rangan, thank you for joining us So it's just a nice segue to bring you in here, you know, given your work with Ryan over so many years now, tell I think I'm also quite interested in knowing a little about how it developed the first time you met and the process and the things you all work together on that culminated into the progress at the advanced level today. Please tell us a little bit about that history and also the current work. Rangan. >> So, Ryan, like he mentioned, walked into my office about four years ago and he was like hey, I'm working on this Omega-3 fatty acid, what can your system tell me about this Omega-3 fatty acid and I didn't even know how to spell Omega-3 fatty acids that's the disconnect between the technologist and the pharmacologist, they have terms of their own right since then we've come a long way I think I understand his terminologies now and he understands that I throw words like knowledge graphs and page rank and then all kinds of weird stuff that he's probably never heard in his life before right, so it's been on my mind off to different domains and terminologies in trying to accept each other's expertise in trying to work together on a collaborative project. I think the core of what Ryan's work and collaboration has led me to understanding is what happens with the drug discovery process, right so when we think about the discovery itself, we're looking at companies that are trying to accelerate the process to market, right an average drug is taking 12 years to get to market the process that Chris just mentioned, Right and so companies are trying to adopt what's called the in silico simulation techniques and in silico modeling techniques into what was predominantly an in vitro, in silico, in vivo environment, right. And so the in silico techniques could include things like molecular docking, could include Artificial Intelligence, could include other data-driven discovery methods and so forth, and the essential component of all the things that you know the discovery workflows have is the ability to augment human experts to do the best by assisting them with what computers do really really well. So, in terms of what we've done as examples is Ryan walks in and he's asking me a bunch of questions and few that come to mind immediately, the first few are, hey, you are an Artificial Intelligence expert can you sift through a database of molecules the 15,000 compounds that he described to prioritize a few for next lab experiments? So that's question number one. And he's come back into my office and asked me about hey, there's 30 million publications in PubMag and I don't have the time to read everything can you create an Artificial Intelligence system that once I've picked these few molecules will tell me everything about the molecule or everything about the virus, the unknown virus that shows up, right. Just trying to understand what are some ways in which he can augment his expertise, right. And then the third question, I think he described better than I'm going to was how can technology connect these dots. And typically it's not that the answer to a drug discovery problem sits in one database, right he probably has to think about uniproduct protein he has to think about phytochemical, chemical or informatics properties, data and so forth. Then he talked about the phytochemical interaction that's probably in another database. So when he is trying to answer other question and specifically in the context of an unknown virus that showed up in late last year, the question was, hey, do we know what happened in this particular virus compared to all the previous viruses? Do we know of any substructure that was studied or a different disease that's part of this unknown virus and can I use that information to go mine these databases to find out if these interactions can actually be used as a repurpose saying, hook, say this drug does not interact with this subsequence of a known virus that also seems to be part of this new virus, right? So to be able to connect that dot I think the abstraction that we are learning from working with pharma companies is that this drug discovery process is complex, it's iterative, and it's a sequence of needle in the haystack search problems, right and so one day, Ryan would be like, hey, I need to match genome, I need to match protein sequences between two different viruses. Another day it would be like, you know, I need to sift through a database of potential compounds, identified side effects and whatnot other day it could be, hey, I need to design a new molecule that never existed in the world before I'll figure out how to synthesize it later on, but I need a completely new molecule because of patentability reasons, right so it goes through the entire spectrum. And I think where HP has differentiated multiple times even the recent weeks is that the technology infusion into drug discovery, leads to several aha! Moments. And, aha moments typically happened in the other few seconds, and not the hours, days, months that Ryan has to laboriously work through. And what we've learned is pharma researchers love their aha moments and it leads to a sound valid, well founded hypothesis. Isn't that true Ryan? >> Absolutely. Absolutely. >> Yeah, at some point I would like to have a look at your, peak the list of your aha moments, yeah perhaps there's something quite interesting in there for other industries too, but we'll do it at another time. Chris, you know, with your regular work with pharmaceutical companies especially the big pharmas, right, do you see botanicals, coming, being talked about more and more there? >> Yeah, we do, right. Looking at kind of biosimilars and drugs that are already really in existence is kind of an important point and Dr. Yates and Rangan, with your work with databases this is something important to bring up and much of the drug discovery in today's world, isn't from going out and finding a brand new molecule per se. It's really looking at all the different databases, right all the different compounds that already exist and sifting through those, right of course data is mind, and it is gold essentially, right so a lot of companies don't want to share their data. A lot of those botanicals data sets are actually open to the public to use in many cases and people are wanting to have more collaborative efforts around those databases so that's really interesting to kind of see that being picked up more and more. >> Mm, well and Ryan that's where NCNPR hosts much of those datasets, yeah right and it's interesting to me, right you know, you were describing the traditional way of drug discovery where you have a target and a compound, right that can affect that target, very very specific. But from a botanical point of view, you really say for example, I have an extract from a plant that has combination of chemicals and somehow you know, it affects this disease but then you have to reverse engineer what those chemicals are and what the active ones are. Is that very much the issue, the work that has to be put in for botanicals in this area? >> Yes Doctor Goh, you hit it exactly. >> Now I can understand why a highly iterative intensive and data intensive, and perhaps that's why Rangan, you're highly valuable here, right. So tell us about the challenge, right the many to many intersection to try and find what the targets are, right given these botanicals that seem to affect the disease here what methods do you use, right in AI, to help with this? >> Fantastic question, I'm going to go a little bit deeper and speak like Ryan in terminology, but here we go. So with going back to about starting of our conversation right, so let's say we have a database of molecules on one side, and then we've got the database of potential targets in a particular, could be a virus, could be bacteria, could be whatever, a disease target that you've identified, right >> Oh this process so, for example, on a virus, you can have a number of targets on the virus itself some have the spike protein, some have the other proteins on the surface so there are about three different targets and others on a virus itself, yeah so a lot of people focus on the spike protein, right but there are other targets too on that virus, correct? >> That is exactly right. So for example, so the work that we did with Ryan we realized that, you know, COVID-19 protein sequence has an overlap, a significant overlap with previous SARS-CoV-1 virus, not only that, but it overlap with MERS, that's overlapped with some bad coronavirus that was studied before and so forth, right so knowing that and it's actually broken down into multiple and Ryan I'm going to steal your words, non-structural proteins, envelope proteins, S proteins, there's a whole substructure that you can associate an amino acid sequence with, right so on the one hand, you have different targets and again, since we did the work it's 160 different targets even on the COVID-19 mark, right and so you find a match, that we say around 36, 37 million molecules that are potentially synthesizable and try to figure it out which one of those or which few of those is actually going to be mapping to which one of these targets and actually have a mechanism of action that Ryan's looking for, that'll inhibit the symptoms on a human body, right so that's the challenge there. And so I think the techniques that we can unrule go back to how much do we know about the target and how much do we know about the molecule, alright. And if you start off a problem with I don't know anything about the molecule and I don't know anything about the target, you go with the traditional approaches of docking and molecular dynamics simulations and whatnot, right. But then, you've done so much docking before on the same database for different targets, you'll learn some new things about the ligands, the molecules that Ryan's talking about that can predict potential targets. So can you use that information of previous protein interactions or previous binding to known existing targets with some of the structures and so forth to build a model that will capture that essence of what we have learnt from the docking before? And so that's the second level of how do we infuse Artificial Intelligence. The third level, is to say okay, I can do this for a database of molecules, but then what if the protein-protein interactions are all over the literature study for millions of other viruses? How do I connect the dots across different mechanisms of actions too? Right and so this is where the knowledge graph component that Ryan was talking about comes in. So we've put together a database of about 150 billion medical facts from literature that Ryan is able to connect the dots and say okay, I'm starting with this molecule, what interactions do I know about the molecule? Is there a pretty intruding interaction that affects the mechanism of pathway for the symptoms that a disease is causing? And then he can go and figure out which protein and protein in the virus could potentially be working with this drug so that inhibiting certain activities would stop that progression of the disease from happening, right so like I said, your method of options, the options you've got is going to be, how much do you know about the target? How much do you know the drug database that you have and how much information can you leverage from previous research as you go down this pipeline, right so in that sense, I think we mix and match different methods and we've actually found that, you know mixing and matching different methods produces better synergies for people like Ryan. So. >> Well, the synergies I think is really important concept, Rangan, in additivities, synergistic, however you want to catch that. Right. But it goes back to your initial question Dr. Goh, which is this idea of polypharmacology and historically what we've done with traditional medicines there's more than one active, more than one network that's impacted, okay. You remember how I sort of put you on both ends of the spectrum which is the traditional sort of approach where we really don't know much about target ligand interaction to the completely interpretal side of it, right where now we are all, we're focused on is, in a single molecule interacting with a target. And so where I'm going with this is interesting enough, pharma has sort of migrate, started to migrate back toward the middle and what I mean by that, right, is we had these in a concept of polypharmacology, we had this idea, a regulatory pathway of so-called, fixed drug combinations. Okay, so now you start to see over the last 20 years pharmaceutical companies taking known, approved drugs and putting them in different combinations to impact different diseases. Okay. And so I think there's a really unique opportunity here for Artificial Intelligence or as Rangan has taught me, Augmented Intelligence, right to give you insight into how to combine those approved drugs to come up with unique indications. So is that patentability right, getting back to right how is it that it becomes commercially viable for entities like pharmaceutical companies but I think at the end of the day what's most interesting to me is sort of that, almost movement back toward that complex mixture of fixed drug combination as opposed to single drug entity, single target approach. I think that opens up some really neat avenues for us. As far as the expansion, the applicability of Artificial Intelligence is I'd like to talk to, briefly about one other aspect, right so what Rang and I have talked about is how do we take this concept of an active phytochemical and work backwards. In other words, let's say you identify a phytochemical from an in silico screening process, right, which was done for COVID-19 one of the first publications out of a group, Dr. Jeremy Smith's group at Oak Ridge National Lab, right, identified a natural product as one of the interesting actives, right and so it raises the question to our botanical guy, says, okay, where in nature do we find that phytochemical? What plants do I go after to try and source botanical drugs to achieve that particular end point right? And so, what Rangan's system allows us to do is to say, okay, let's take this phytochemical in this case, a phytochemical flavanone called eriodictyol and say, where else in nature is this found, right that's a trivial question for an Artificial Intelligence system. But for a guy like me left to my own devices without AI, I spend weeks combing the literature. >> Wow. So, this is brilliant I've learned something here today, right, If you find a chemical that actually, you know, affects and addresses a disease, right you can actually try and go the reverse way to figure out what botanicals can give you those chemicals as opposed to trying to synthesize them. >> Well, there's that and there's the other, I'm going to steal Rangan's thunder here, right he always teach me, Ryan, don't forget everything we talk about has properties, plants have properties, chemicals have properties, et cetera it's really understanding those properties and using those properties to make those connections, those edges, those sort of interfaces, right. And so, yes, we can take something like an eriodictyol right, that example I gave before and say, okay, now, based upon the properties of eriodictyol, tell me other phytochemicals, other flavonoid in this case, such as that phytochemical class of eriodictyols part right, now tell me how, what other phytochemicals match that profile, have the same properties. It might be more economically viable, right in other words, this particular phytochemical is found in a unique Himalayan plant that I've never been able to source, but can we find something similar or same thing growing in, you know a bush found all throughout the Southeast for example, like. >> Wow. So, Chris, on the pharmaceutical companies, right are they looking at this approach of getting, building drugs yeah, developing drugs? >> Yeah, absolutely Dr. Goh, really what Dr. Yates is talking about, right it doesn't help us if we find a plant and that plant lives on one mountain only on the North side in the Himalayas, we're never going to be able to create enough of a drug to manufacture and to provide to the masses, right assuming that the disease is widespread or affects a large enough portion of the population, right so understanding, you know, not only where is that botanical or that compound but understanding the chemical nature of the chemical interaction and the physics of it as well where which aspect affects the binding site, which aspect of the compound actually does the work, if you will and then being able to make that at scale, right. If you go to these pharmaceutical companies today, many of them look like breweries to be honest with you, it's large scale, it's large back everybody's clean room and it's, they're making the microbes do the work for them or they have these, you know, unique processes, right. So. >> So they're not brewing beer okay, but drugs instead. (Christopher laughs) >> Not quite, although there are pharmaceutical companies out there that have had a foray into the brewery business and vice versa, so. >> We should, we should visit one of those, yeah (chuckles) Right, so what's next, right? So you've described to us the process and how you develop your relationship with Dr. Yates Ryan over the years right, five years, was it? And culminating in today's, the many to many fast screening methods, yeah what would you think would be the next exciting things you would do other than letting me peek at your aha moments, right what would you say are the next exciting steps you're hoping to take? >> Thinking long term, again this is where Ryan and I are working on this long-term project about, we don't know enough about botanicals as much as we know about the synthetic molecules, right and so this is a story that's inspired from Simon Sinek's "Infinite Game" book, trying to figure it out if human population has to survive for a long time which we've done so far with natural products we are going to need natural products, right. So what can we do to help organizations like NCNPR to stage genomes of natural products to stage and understand the evolution as we go to understand the evolution to map the drugs and so forth. So the vision is huge, right so it's not something that we want to do on a one off project and go away but in the process, just like you are learning today, Dr. Goh I'm going to be learning quite a bit, having fun with life. So, Ryan what do you think? >> Ryan, we're learning from you. >> So my paternal grandfather lived to be 104 years of age. I've got a few years to get there, but back to "The Infinite Game" concept that Rang had mentioned he and I discussed that quite frequently, I'd like to throw out a vision for you that's well beyond that sort of time horizon that we have as humans, right and that's this right, is our current strategy and it's understandable is really treatment centric. In other words, we have a disease we develop a treatment for that disease. But we all recognize, whether you're a healthcare practitioner, whether you're a scientist, whether you're a business person, right or whatever occupation you realize that prevention, right the old ounce, prevention worth a pound of cure, right is how can we use something like Artificial Intelligence to develop preventive sorts of strategies that we are able to predict with time, right that's why we don't have preventive treatment approach right, we can't do a traditional clinical trial and say, did we prevent type two diabetes in an 18 year old? Well, we can't do that on a timescale that is reasonable, okay. And then the other part of that is why focus on botanicals? Is because, for the most part and there are exceptions I want to be very clear, I don't want to paint the picture that botanicals are all safe, you should just take botanicals dietary supplements and you'll be safe, right there are exceptions, but for the most part botanicals, natural products are in fact safe and have undergone testing, human testing for thousands of years, right. So how do we connect those dots? A preventive strategy with existing extent botanicals to really develop a healthcare system that becomes preventive centric as opposed to treatment centric. If I could wave a magic wand, that's the vision that I would figure out how we could achieve, right and I do think with guys like Rangan and Chris and folks like yourself, Eng Lim, that that's possible. Maybe it's in my lifetime I got 50 years to go to get to my grandfather's age, but you never know, right? >> You bring really, up two really good points there Ryan, it's really a systems approach, right understanding that things aren't just linear, right? And as you go through it, there's no impact to anything else, right taking that systems approach to understand every aspect of how things are being impacted. And then number two was really kind of the downstream, really we've been discussing the drug discovery process a lot and kind of the kind of preclinical in vitro studies and in vivo models, but once you get to the clinical trial there are many drugs that just fail, just fail miserably and the botanicals, right known to be safe, right, in many instances you can have a much higher success rate and that would be really interesting to see, you know, more of at least growing in the market. >> Well, these are very visionary statements from each of you, especially Dr. Yates, right, prevention better than cure, right, being proactive better than being reactive. Reactive is important, but we also need to focus on being proactive. Yes. Well, thank you very much, right this has been a brilliant panel with brilliant panelists, Dr. Ryan Yates, Dr. Rangan Sukumar and Chris Davidson. Thank you very much for joining us on this panel and highly illuminating conversation. Yeah. All for the future of drug discovery, that includes botanicals. Thank you very much. >> Thank you. >> Thank you.

>> from the Silicon Angle Media Office in Boston, Massachusetts. It's the cue Now, here's your host. Day Volonte. >> Hi, buddy. This is David Lantz. Welcome to this cube. Conversation with Dr Rico is the CMO of infinite out. It's still I still have a hard time saying that doctor or an engineer and I love having you on because we could talk storage. We could go deep and we could talk trends and marketing trends, too. But so welcome. Thanks for coming on my sled. So tell me what's new since the scale to win launch that you guys had. Tell me what you know. Is everything shipping Now What's the uptake been like with customers? And the reaction? Yeah, >> they're the reaction has been phenomenal. This, as you may recall, you were there. It was biggest launch in our history, which was fantastic. And the reaction has just been overwhelmingly positive, with customers with partners with analysts. Human scum cases with competitors is an interesting you know, we had a lot of things that were already shipping. They were an early customer release. There were a few things that we had started shipping in December on the things that we said we'd be coming in three Q. We G eight on time. So there, there now all generally available except the stuff that we talked about that would be available in 2020 which right now looks like it's on track. It's doing very, very well. >> So VM wear VM world eyes coming up later on this month, things are obviously changing. There was announcement recently that that VM wears gonna choir pivotal. So a little bit of financial engineering going on stock stock rose 77% on the day when the Dow dropped 800. So okay, the funny money. But things are changing in the V m where ecosystem you certainly saw we we This is our 10th year the M world. We go back and you hear Tod Nielsen back in the day, talk about for every dollar spent on a V M where lice and 15 was spent a Negro system, you know, we're kinda del izing vm wear now, which is sort of interesting, but I'm curious as to what you're seeing what that all means to you. I mean, still half a million 600,000 customers, you've got to be there you guys have great success at that show. So your thoughts what's going on? But VM world this year? Yeah, I >> kind of kind of loaded their first of all congratulations on the milestone. That's great. 10 years is super. Remember, probably seeing you with the 1st 1 there. Of course we knew each other longer. Uh, you know, and sure I get the incestuous, you know, money changing of hand there, I think I think it's it's good in one respect. You certainly CBM where, you know, making big inroads with VM wear on AWS. And this isn't now with Pivotal will be a good launching platform for Della's well, a svm where to be a little bit more in control of their own destiny. And it's certainly the way a lot of people are going. We're doing a lot of that ourselves. Not so much, in a sense. We don't have a cloud platform that we sell is a total encompassing platform. But of course, with new tricks cloud on big players and then certainly a large portion of our our customer base, our cloud service providers, they love our stuff. It helps them compete. It actually gives them in some respects, a competitive advantage, but VM world itself. Lots going on there. We have amplified our presence once again because VM where does represent a large portion of our customer base? So we're we're very proud of that. We're very proud to be a technology alliance partner of the M wears Andi. We're expecting to see a really good show in a really good cloud. A cloud crowd has they return back to their home base in San Francisco for us this year, it's It's gonna be a different experience. Were tellingme or of the software story, more of the portfolio story more about how you scare scale the win. We have a virtual presence this year, which is going to be very helpful in telling that story. Customers can come in and they can see more than just a ah box that in our world is really not important because it's for us. It's all about the software and stuff we do. We even in Booth Theater, we have some private meeting spaces well, to take people into a bigger, deeper drill down. But the virtual experience will allow them to touch and feel stuff that maybe they didn't get to do before, and that's gonna be kind of exciting as well. >> So you mentioned C S P s. We had Michael Gray thrive on a while back, and you know, he was saying that Look, he likes your product because it allows him to do other things. And don't worry about, you know, the old sort of tuning and managing and ableto re shift labor. I felt like that was an interesting discussion, primarily because you've got all these cloud service providers that everybody thought aws was just gonna kill. And if anything, it's elevated them. What are you seeing in the CSP space? Yeah, you know, >> Michael had a lot of interesting things to say that definitely love the fact that we enable multiple workloads without them having to do lots of cautious planning and re planning and shifting and shuffling. And we are seeing C S P is becoming more value. Add to a lot of businesses, especially the mid market and the smaller enterprise where people may want more than just infrastructure. You know, they don't they need that application level support and companies like thrive in some of our other really good customer, US signal and you know they're all capable of Flex Central's. Another one they're all capable of providing service is beyond the hardware they're capable of providing that application support the guidance and, in the case of Thrive, the cybersecurity guidance especial Really, which is really, really critical. So they're growing, and they're also, by the way, working with eight of us and Google and Azure to provide that capabilities well, when necessary. >> Well, that leads me to the sort of multi cloud discussion in our industry. We tend to have this alphabet soup of acronyms like another reason I like talking to you because we can kind of cut through that. And, you know, I love the marketing. I think marketing helps people understand what's going on differentiate. It gives you an indication of where the industry is going, and multi cloud is one of those things that I mean. I've kind of said it's a symptom of multi vendor and more so than a strategy. But increasingly it seems like it's becoming a strategy with customers, and you just gave an example of thrive working with multiple cloud vendors. Clearly, VM where wants to be in that business. What your thoughts on multi cloud and and hybrid. What does it mean for for infinite at What's your strategy there? You know, it's it's interesting because I >> just read an article the other day about you know, the definition of multi cloud on whether it's being abused and, you know, I I look at it as someone just trying to tell their story and give it. Give it some favor. I think at the end of the day, uh, every business is going to be talking to multiple platforms whether they want to or not. You know, there are many customers and companies out there, businesses who are in our customers who have gone the way of the cloud and repatriated. Certain things is they've they found that it it may work. It may not work, and there are many cloud providers who were trying to do things to accelerate migration of applications because they see that certain applications don't work. You know, we got one of the cloud providers buying Ah, now as provider, another one buying very recently, you know, an envy me based flash company to try to pick up those loose workloads where they might struggle today. But the end of the day everybody's going to be multiple. And whether it's because they're using cloud service is from from a software perspective or whether they just need to basically broker and maintain sort of that that independence so that they can maintain some cost control, availability, control, security, control and in some cases it will remain on premises. And some of things will be off just so they could get the applications closer to their end users. So you know what is multi Cloud? Multi Cloud really is just one of those terms that literally means what it says. It's your business running in multiple places. It doesn't have to necessarily be simultaneously by the same application. >> A big part of your value proposition is the simplicity. We've heard that from your customers, and you guys obviously push that out there. I want to ask you because you mentioned repatriation and you know, Cloud keeps growing like crazy. Sure, and the on prem not so much. You guys are smaller company. You're growing your stealing share, So yep. So maybe is that simplicity thing. Here's my question. So it's around automation. The cloud providers, generally an Amazon specifically have have driven automation. They've attacked the IittIe labor problem and they're able to charge for that on Dhe. So my question is, are you seeing that you're able to attack that labor problem in a similar sense and bring forth the value proposition to customers is Look, we can create a cloud like experience on Prem if you want MacLeod. Great. But if you want to stay on Prem, you're gonna get the benefit of being able to shift. Resource is two more strategic things and not have to worry about all this heavy, heavy lifting. You You seeing tangible evidence of that? >> We're seeing significant tangible evidence of that on and, you know, a couple of things. You know, you talk about growth, right? And I think when we did the launch, you know, only a few months ago we were at about 4.6 exabytes of capacity shipped. We just passed 5.1. That's some significant growth in in just a few months. It's like a 33% growth just from the same time last year, which is which is fairly significant. And of course, if you're familiar with the way we talk, you know you have an engineer is the head of marketing. We like to tell the truth. You know, we don't like to mask, do many things and confuse people. We don't like talking about effective storage because effective capacity doesn't really mean much to some people. So that's, you know, this is what we This is what we shipped and it's growing rapidly. And a lot of that is growing, in part because of the significance of the message and in part because of this need to control costs, contain costs and really operate in a more modern way. So get back to your comments about cloud and cloud operation. That's really what people want. People like the consumption model of cloud. They don't always like the cost on hidden costs. So simplifying that, but giving them the flexibility Thio have either an op X or cap ex that allows him to grow and shrink as they move workloads around. Because everybody grows even on Prem is growing. It's just, you know, it's the law of numbers, right? Cloud is growing, absolutely. But on Prem really is growing. And then the other thing I want is they want the operational flexibility. And that's what we talked about in our elastic data fabric. They don't like constantly having to re jigger and re balance workloads. Infinite box by itself. The platform of infinite Box takes away a lot of that mystery and magic, because it it kind of hides all of the complexity of that workload. And it, you know, we take the randomness out of the I o. I think maybe Craig Hibbert mentioned in his video is he was describing in detail how that happens. Remember Michael Gray talking about that as well, you know, So those those things come out in a single infinite box. But even if you said well, I still want to move my workload from, uh, you know this data center to an adjacent data center or perhaps a data center in another facility. Um, excuse me, Another city. So that's closer to the end user. Making that transparent to the applications is critically important. >> Yes, he talked about growth in about 1/2 a PETA bite. Sorry, half an exabyte in just a few months. A couple months? Really Right. That's that's growth. But I want to ask you about petabytes. Petabytes scales. Kind of key of companies that don't do that in a year day, eh? Exactly. So that's a petabytes scale. Is big party of marketing two questions? Why is that relevant? Or is that relevant to VM? Where customers? Why so and then, does it scare some people owe you? Asked a great question. >> It absolutely scared some people. And I know that there are some pundits out their industry pundits who who basically don't agree with our messaging. But this is this is the business problem that we we targeted the solve rate. Um, there are a lot of people out there who don't think they're petabytes scale yet because maybe they're individual applications aren't petabytes scale. But when you add it up, they get there and a lot of our customers are existing. Customers didn't start with infinite at at petabytes scale. They started a couple 100 terabytes, perhaps, but they're petabytes skill now. In fact, over 80% of the customers and systems that we have out there today or above the petty bite. We have customers that are in the tens of petabytes. We have customers that are in the hundreds of petabytes. They grow, they grow rapidly on. Why is that? Well, to two factors. Really. Number one, if you go back to. Probably when I first met you back when I had your hair, at least in quantity, way had way. Were kind of crusting that terabyte mark. Right? Right. And what was the problem? The problem was nobody could figure out how to deal with the performance. Nobody wanted to put that much risk on a single platform, so they couldn't deal with the availability. And they really didn't know how to deal with even the serviceability of that scale. So terabyte was a problem solved No, 25 years ago, and then things were rapidly from there. Now we're at the same juncture, just three orders of magnitude later. Right? >> Well, that's interesting, because, you know, you're right. People didn't want to put all all that capacity under an actuator that cost performance problems. They were concerned about, you know, just availability. And then two things happen so simultaneously, flash comes along. And, you know, you would say was put sort of a Band aid to some of the performance problems. Sure. And you guys came up with, like, this magic sauce to actually use spinning disc and get the same performance or better performance you would argue with flash. And so as a result, you were now able to do a lot Maur with the data, the concerns about that much date under the actuator somewhat attenuated because, I mean, you've got now so much data, you've got to do something that's almost that's flywheel effective. You've got tons of data machine intelligence and a I. Now, coming into the picture, you've got Cloud, which has been this huge tail when for the industry and for data creation in general. And so I see. You know, you see, like the I. D. C numbers and for forecasting growth of data and storage could be low. I mean, the curve could be bending, you know, kind of more than exponentially your thoughts on that. >> Yeah, it's an interesting, interesting observation. I think what it really comes down to is our storyline is math is greater than media, all right? And when you when you look at the flash being, you know, the panacea to performance it was just a step in the evolution, right? You go back and and say, spinning disc was the same solution to the performance problem 20 years ago. 25 years ago, even it was 5400 rpm discs and then very rapidly. Servers got faster. The interconnects got a little bit faster. They were still mostly differential. Scuzzy. There was 7200 rpm discs. And I promise you, by the way, that if you're running 5400 rpm desk, you install 7200 rpm. All yours performance problems will go away until the day you install it. And then it was 10,000 rpm discs and I was 15,000 rpm disc, and it still wasn't getting fast enough because, you know, you went to Fibre Channel One Gig Fibre channel and then to Geek Fibre, Channel four, Gig fibre, Channel eight, gig fibre channel. The unified connects got faster. The servers got faster. That was more cash on the servers. Then this thing came along, cuts called solid state disc. Right. And then it was it was SLC single layer cell technology. But don't worry about it's very expensive. Not a problem. You only need 4% of your application, right? Jerry? No, no, I'm sorry. percent. No, I'm sorry. 30%. What the heck? You know, M l c is now a little bit more reliable, so let's just make make it all slash. Right? So that was the end of the story, right? No. Servers continue to get faster. Uh, the media continue to get faster and denser, right? So now the interconnect isn't fast enough, So envy me. Is that the answer to life? The universe and everything? Well, wait. I got a better answer for your test. CIA storage class memory in parallel with that. By the way, there are some vendors out there who said that's still not fast enough. We want to put more d ram and the servers and do things in memory. We went in memory databases. I guarantee whatever you do from a media perspective on my personal guarantee to you, it's obsolete by the time you're up and running. By the time you get your applications migrated, configured and running with business value, it's already obsolete. Some vendors got something better coming out. The right answers. This stuff you talked about, the right answer is everything that you're doing for your business. APs. It's a it's a Mel. It's solving the problems in software and, you know, you said we use disc and make it fast. It's not despite itself, of course, right? It's D Bram. It's a lot of the Ram, which, by the way, is orders of magnitude faster than flash the NAND flash. And even if its ECM and still orders of magnitude faster than that, what we use the disk for today in the architecture is the cost factor. We take the random ization out in the flash and we take the >> end and in the in the diagram >> and we used the SAS in the back end to manage costs. But we use it in a way that it performs well, which is highly sequential, massively parallel. And we take full advantage of that Beck and Ben with to do that with that massive dear am front end. Our cash ratios are unparalleled in the industry and and we use it even more effectively that way. But if architecture already evolves, so if if SCM becomes more stable and becomes more cost effective, we can replace that that S S D layer with the cm. And if you know, if the economics of Q L C or something beyond that. Come down will replace the back end with that, do you? Do >> you ever look at what you're doing today as sort of a modern day symmetric. So I mean, a lot of things you just said. I mean, you've got a lot of memory. You've got a massive back end. You know, those were two of the characteristics of symmetric snow. Of course. Fast forward. Whatever. 30 years, right. But a lot of it was sort of intelligence and understanding. Sure. So how data works, is it Is it a fair sort of, or is it radically different? Well, in terms of mindset, I mean, I know the implementation is >> right, right? >> Yeah. I mean, it's not an unfair comparison. I mean, tiered storage was around before some metrics. Right? So it's certainly existed existed then, too. It was just at the time. It was a significant innovation course to layer at the time, right? A big cash front, ending some slower media and then taking advantage of the media on the back end. The big difference today is that if you look at what some metrics became through its Evolution's DMX and V Max and now Power Max. It's still tiered storage, you know, you still have some cash. That's that's for unending some faster media with power. Max, you're you're dealing now with us with an SS a back end. But what happened with those types of architectures is the tearing became more automated. But you're still moving information around. You're still moving Information from one said it This to another set of this leader in the cycle. You're still trying to promote things you know, to to the cash up front. We're doing it in real time. We're >> doing it by analyzing >> the data on the way it comes in. We're reassembling it again, taking the random ization out we're reassembling it and storing it across multiple disks in a way that it it increases our probability of pulling that information associated information back when we need it later. So there's there's no movement. Once its place, we don't have to replace it. You know it's already associated with other data that makes sense, and that gives us a lot of value. >> And secret sauce is the outcome of the secret sauce is you're able to very efficiently. Well, historically, you haven't been able to do a lot of garbage collection, a lot of data movement, and that just kills performance. There's >> really no garbage collection necessary in our in our world way. Also use very modern data structures or patents. Ah, lot of them on our neural cash Deal with the fact that we use a try data structure. So we're not using old fashioned hash tables and you know, l are you algorithms, You know it Sze very, very rapid traverse a ll of these trees >> and you're taking advantage of machine intelligence inside the software architecture. That really is some of the new innovation that really wasn't around to be able to take advantage of that 20 years ago. Maybe it was it was just not cost effective. Do the math was there, put it that the math of the mouth was there and >> there there There's been lots of evolutions of that over the years, a swell, but we continue to evolve and innovate. And, you know, one of the one of the cool things I think about working infinite at is is the multiple multiple generations of engineer where you've got people who understand that math they understand the real nuances of what it means to operate in a world of storage, which is quite a bit different than operating, saying networks or proceed be used because data integrity is paramount. There's lots of lots of things that go on there as well. But we also have younger generations, generations who like new challenges and like to re invent things so they find newer and greater ways to do things. >> This is exciting. So systems, thinkers and I mean server thinkers. I mean, people who understand, you know, systems designed it all the way through and and, you know, newbies who are super smart like you say, wanna learn and solve problems? Go back to the petabytes scale discussion, >> solve problems at petabytes scale, right? Even if the customer doesn't need that necessarily to solve that problem is critically important because even if you look at Les, just take, you know NFS, for example, most NFS systems deal with thousands of objects. Hundreds to thousands of objects are an F s. Implementation deals with billions, right? Do you need billions? How many applications you know that have billions of objects, But being able to do that in a way where performance doesn't degrade over time and also do it in a way where we say our nlm implementation isn't impacted by any any type of service events, we can take a note out, and it doesn't impact in ln There's no no degradation and performance. There's no impact or outage in service. All that's important. Even when you're dealing with smaller application sizes because they add up, they really do add up. He also brought up the point about, you know, density and actually intensity. Great. You know, back 25 years ago, when we were dealing with, you know, the first terabyte storage system, you know, how much how much stories did you have on your laptop? How much you have today, right? You know, you're probably more than a terabyte. They were laughing about putting things terabyte on the floor. And now you get more than a terabyte on your laptop. Things changing? >> Yeah. Um, I wanna ask you where you see the competition. We talked about all flash. We've had a long conversation, long, many conversations in the past about this, But you really, you know, the all flashy kind of described it as a Band Aid, essentially my words, but it was sort of a step function. Okay, great. Um, you have one company, really us who achieve escape velocity in that business in terms of pure But is that where you see in competition and you're seeing it from, you know, the hyper scale er's where you Yeah, you know, >> it's interesting. You know, you look at companies like, you know, we admire what they dio, especially with regard to marketing. They do a really good job of that. They also, um I have some really interesting ideas innovating the media, which is which is great. It helps us in the long run as well. Um, we just look at it as a component of our system, not these system, which makes it different. We don't really see the A f a. You know, the small scale a FAA is are the majority of our competition. We do run into them, but typically it the lower end of the opportunity. Even within the bigger companies that have competitors to those products, we run into them and smaller opportunities, not bigger opportunities where we run into them where there's a significant performance advantage as long as you don't mind the scale out approach to solving the problem. Unfortunately, when you're using a phase two skill out, you know you're putting all of the intelligence requirements on some poor storage administrator or system administrator to figure out what those where right, we take all of that away. So once it starts to scale, that's where we come in a plan. We don't see tons of competition there. Certainly, we're seeing competition from the clouds. And the competition from the clouds is more born of customer mandates and company mandates. Sometimes they I'm not quite sure that everybody knows why there who think to the cloud and we're problem they're trying to solve. But once they start to see a story that says, Hey, if the reasons are and you do understand those reasons, if the reasons are agility and financial flexibility and operational agility not as well as his acquisition agility, you know, we have answers to that and it starts to become a little bit more interesting and compelling. >> All right. One of the highlights of the M world each year is your dinner. Your customer I crashed in a couple of years ago when there were no other analysts there. And then last year again, it was in Vegas. Shows a nice steak house. This year we're in San Francisco, but But I had some great conversations with customers. I remember speaking to one customer about juxtaposing the sand thio to infinite debts platform. And you know the difference. The Sands taken off doing really well, but But he helped me understand the thinking from their standpoint of how they're applying it to solve problems and why v san wasn't a good fit. Your system was, um that was just one of many conversations last year had again other great conversations with customers. What do you do in this year? You have a customer dinner. We are? Yeah. We love to have you in and gave the invitation there. Yeah, the invitation. Is that definitely there? You know, a couple of >> years ago we didn't invite analysts, and you know what it was? It was a mistake. We and we learned that lesson into a large part. We credit you for for showing us how wrong we are. Our customers are very loyal. They're some of the most loyal in the industry. Don't take my word for it going. The gardener Pierre Insights and and look at our numbers compared to everybody else's any pick. Pick a vendor. We're at the top of the list with regard to not only the ratings but, more importantly, the customers willingness to recommend in every category, too. By the way, it's It's not just product quality and performance, and it's it's service support. It's easy doing business. It's an entirely different experience. So we love having the customers there, and the customers love having you there, too. They love having you and your appears in the industry there because they love learning from you and they love answering the questions and getting new insights. And we'd love to have you there. We're gonna be in the Mint this year. San Francisco meant not the not the current one that that's pretty coins, but the original historical site on duh. You know we have. We have invitations out thio to about 130 people because there's only so much room we have it at the event, but we're looking forward to a great time and a great meal and good conversation. >> That's great. Well, VM World is obviously one of the marquee events in our industry. It's the It's the fat middle of where the IittIe pro goes on dhe We're excited. Used to be Labor Day started the fall season. Now it's VM world. Well, Doc will see you out there. Thanks very much for your good to see you. All right. Excellent. All right. Thank you for watching everybody. This is day Volonte in the Cube will see you next time we'll see you at the M World 2019.

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody welcome to this cube conversation Michael Gray is here is the chief technology officer of Boston based thrive Michael good to see you coming on glad to be here so tell us about thrive what do you guys all about you know thrive started almost 20 years ago as a traditional managed service provider but really in the past four to five years transformed into a next generation managed service provider primarily now we're focusing on cybersecurity cloud hosting and public cloud hosting as well as disaster recovery so dig into that next generation yeah people use that term but what does it mean well the needs of our customers really changed over time before you could maybe simply roll out some antivirus and do some desktop management some server management but with the way some of the innovation is exploded in the cloud and the way application development has changed all of our businesses we've noticed that our customers have all kinds of new needs that includes much higher focus on cybersecurity these things can't be an after afterthought the other things with all the data that we see coming from our customers they may need a much higher level of performance than they ever did before from their their local hosting or or in the cloud so what Amazon Web Services came out you know 2006 timeframe every set up ms P's like drive they're in big trouble the exact opposite is happening for your business yeah yeah yeah you know why is that number one and number two how do you compete with the big cloud providers you know somebody like Amazon or even Azure those services are not easy to roll out you still need someone to understand what the businesses are and then translate those into technology solutions for us when someone starts asking how do I transform my business whether it be in the public cloud or the private cloud that's a tremendous opportunity to bring our knowledge and all of our engineering support to those customers to help them transform so I mean I I liken it to you know I could hire a plumber I could hire an electrician I could hire but I don't want to be the general contractor I'm happily happy to pay an expert at that who's got contacts deep expertise and and push the responsibility on them is that a fair analogy yeah I do think it is fair you know obviously it's a it's a much more technical environment than something like that so it's much more complicated you know the other thing is when we start to understand some of these business problems and pull the pieces apart we have a tremendous amount of expertise and experience where we can help those customers understand how to solve those business problems how to implement the technology and then how to be successful in whatever way they're trying to transform their business so you sort of touched on some of the trends in your business did talk more about your customers it's my understanding is it's mostly small and mid-sized customers is that correct you know there's far more mid-market than there ever were before I think people in the mid market are realizing that they do need to take some of these services outside their walls I notice a lot of mid-market customers that are focusing on their core business if you're a manufacturing company a biotech financial services company you can realize very quickly that you're not in the cloud hosting business and no matter how many people they hire grow your staff can be very difficult to actually be successful in these technologies despite all the different pieces that Amazon or Azure offers in the public cloud you still have to figure out how these systems work and how they apply to your business well to midsize companies and especially small companies they obviously aren't the resources that a large company has so you bring a lot of that infrastructure expertise along yeah and I think part of it is you know we have such a big exposure to a very large customer base so a problem that a customer may see that they think is maybe perhaps special to them we've solved that problem maybe hundreds of times and we can give them a lot of insight into how other companies of similar verticals have solved those problems you start out as sort of a local MSP and that have expanded over time yeah that's correct so we've expanded pretty rapidly over the past three to four years now we're we have five offices primarily in the East Coast and really started to help the mid-market who's now started to understand that they need to frankly outsource some of these solutions or get in business with a partner like us who can help them take those outside their walls and provide them a much higher level of service often at the end of the day the investments much lower for the customer so paint a picture of your your infrastructure what that look like yep so we have a data centers you know I have three primary data centers in New England the New Jersey New York area and then in the south all those data centers actually have infinite at storage which is you know something that I'm a huge fan of and one of the things that I like to offer in all of our data centers is I don't necessarily it doesn't matter to me geographically where the customer would like their workloads that's one of the things that the public cloud offers you can move resources around geographically depending maybe where your headquarters is or some of your branch offices we provide the same solutions at often a much higher performance level and we've extracted all the complications of where to put these so if a customer is in San Francisco and they'd like to dr2 New England not an issue but all of a sudden if they change their headquarters or maybe they do an acquisition and they need to change that footprint I can change that on the fly for them so and I've walked through many data centers of MSPs over the years yeah and ten years ago yeah you had one of everything yeah yeah compact server yeah yeah yes so I would imagine you had similar challenges you mentioned Infini debt yeah trying to essentially run your entire storage yeah yeah so we've um we've acquired several other MSPs over the past several years we had a lot of disparate storage platforms a lot of investments made some of them hung on to maybe for too long some of them you know were purchased for a specific business reason that might not be there anymore at this point we've standardized on Infini debt it's enabled our business to do a lot of new and innovative services so high performance storage replication similar to what you'd see in the public cloud but also we can support very complicated very data hungry clothes so you're sexually replacing so older storage systems with infinite at maybe you can describe the before and after you know frankly with with acquiring a lot of msps you name a storage platform we had it at some point through this standardization the the beauty of it is a consolidation so I can leverage the folks that manage our infinite at across the country all right so my TCO on something like this is is is really kind of amazing I can leverage a lot of experience with the defender that when I go in and need to do a data center consolidation I have some things that are knowns there's a lot of unknowns and acquisitions and all the due diligence in the world there's still going to be things that maybe not every detail has been figured out but when I roll out an infinite at I know I've solved one very foundational problem right out of the gate so and I want to come back on the TCO but before I do when I talk to people like you and I'm not a CTO but a lot of times I infer that people are comparing the the latest and greatest in this case infinite at yeah with what they had that's five six seven years old sure of course the TCO is the share that okay so I'm a push a little bit is is I presume you looked at infinite ad and other storage suppliers and I'm interested in what you found in those comparisons is it is it is it just great TCO relative to what you had that was five years old or is it real after the other yeah yeah so you know when it comes right down to it I've seen every marketing pitch for a storage platform you can possibly imagine I've seen every bullet list of features I've seen every we have proprietary technology that does X&Y you know eventually when you put it on the floor it's not everything that was in the sales process maybe there's something that was uncovered on a licensing side maybe the performance wasn't quite what someone said it would be the thing about infinite at is they've delivered on everything they've said in the sales process and you don't find that very often the other thing I need to mention too is that even post sale the discussion about the technology continues it's always a discussion about how the technology is built and how it enables you it's not we have a new feature coming on the roadmap that is gonna solve X&Y problem they've worked out the very foundational problems you know the other thing I do want to mention about Infini debt is being such a strong engineering company I know the best an engineer I can rely on them to make good engineering decisions so I want to ask you about performance because when I first saw infinite out you know we were on the on the flash bandwagon we got early on that yeah and these guys came in and said actually we can beat flash performance using our architecture and software and so forth yeah be like really so I'll ask you yeah have you found that from a performance standpoint so I have and you know I run into a lot of situations where there's technology leaders that are maybe buying into a specific brand name you know if we put X technology in I know for a fact that it's gonna beat the performance of an infinite at my approach with that is I have seen all the platforms and I agree there's a lot of great products out there high performance sit down and take a look at the way the technology has been built and have an open mind and you'll most likely be convinced that that technology is the right answer a lot of times I like to sit back and and say look I'm not gonna push any vendor any software partner any manufacturer on you take a step back and have an open mind of technology it'll make a big difference when you actually listen well I'm sure you've heard the sales pitches are you using those slow spinning business mic spinning discs or mechanical yeah yeah yeah yeah your experience has been and we've had Brian Carmody on yep yes of others yeah so then we have Moshe come in here yes Blaine that's sure and so but I always like to talk to the customer and get the affirmation yes yeah well again to me the the conversation with infinitive is always about engineering you know it's not a great deal of marketing first of course everybody does marketing that happens on a regular you have to do that to run a business but if you want to talk purely about how things have been designed that conversation often eclipses a lot of other marketing from other storage vendors so talk about your your how you spend your time yeah it's acting you know infrastructure roadmaps and so forth to get more sort of I got to get this stuff up and running today describe yeah you know we've set a path to build a very high performance nationwide cloud we are going up against the public cloud by the way I'm a public cloud partner right I do both we do hybrid hosting I want to give the customer the best of both worlds which may be a cliche but we really are aiming to get there that's one of my primary tasks is establishing a technology vision you know I can describe to a customer where our cloud is going and I can stand behind that with the public cloud we do have to Lou a little bit of reading the tea leaves so I I help people with trying to understand what you know maybe the public cloud vision might be but also how I fit together with that that public cloud with private cloud hosting and the other thing primary goal of mine is bringing in some of these different functions of IT so for instance high-performance cloud private cloud Plus cyber security I can bring those two together for you in a cohesive solution that that's what I spend a lot of my time so as you look out you know put on your your your binoculars maybe even your telescope big trend in one of the big trends is hyper-converged in bringing in storage compute and networking all together yep if I'm inferring correctly you're going for more of a Best of Breed approach yet and yet in you guys have the engineering expertise you have to do that can you can you talk about the philosophy there sure sure well one of the things that I like to do is just abstract some of these confusing and complicated conversations from our customers you know if we're gonna talk about SD win and make sure I have SD weigh in in my data center I can tell the customer I can give you that functionality and you don't have to worry about how these different pieces go together I'm happy to be transparent you know there's a lot of things in the public cloud that simply information you can't get I'm actually willing to share how those solutions that I built go together because I want people to see that transparent I want them to trust us so you know when when we go and start putting these together these are things where when the customer does have a question they want to drill in because they have concerns I can eliminate those very quickly you talking about private cloud earlier I want to come back to share and just so we always say on the cube bring the cloud experience to your data wherever it lives yeah it's all about that operating mom yep yeah so as you see tool chains like kubernetes yep yeah a cloud native stuff yeah come in you want to have that cloud experience you want to have yourselves a fantasy pass that on yep do you have customers yeah how do you look at that yeah what role does storage infrastructure playing to me and this is something that's primary to thrive focuses application enablement we're an application enablement company so if your application is best run in Azure and then we want to put it there a lot of times we'll find that just due to business problems or legacy technologies we have to build private clouds or even for security reasons we want to build private cloud or purely just because we're running into a lot of public cloud refugees you know they didn't realize a lot of the maybe incidental fees along the way actually climbed up to be a fairly big budget number so you know we want to really look at people's applications and enable them to be highly high-performance but also highly secure I want to come back to the TCO I said oh yeah sure when you do the total cost of ownership analysis yeah what you find is it really boils down to the to the labor yeah piece of yep and see I'm curious as to when you brought in Infini debt yeah what the business impact was you know economically yeah no there's other non TCO thing yeah more so was it the labor cost that got reduced did you redeploy those resources well actually Hardware first and foremost and you know this is going back many years but and and I think I would say this is true for any datacenter cloud provider the minute the phone rings and someone says my storage is slow we're losing money okay because we've had to pick up the if someone needs to address that we have eliminated all storage performance helpdesk issues it's now one thing I don't need to think about anymore we have we know that we can rely on our performance and we know we don't need to worry about that on a day to day basis and that is not in question now the other thing is really as we started to expand our infinite at footprint geographically we suddenly started to realize not only do we have this great foundation built but we can the leverage and invest when we made to do things that we couldn't do before maybe we could do them but they required another piece of technology maybe we could do them or they required some more licensing something like that but really when we started the standardization we did it for operational efficiency reasons and then suddenly realize that we had other opportunities here and I have to hand it to infinity they're actually the ones that helped us craft this story not only is this just a solid foundation but it's something you can build on top of so talk about the performance I want to ask you yeah I've had certainly Brian Carmody Craig Hobart and I have sat down and Craig actually made the statement you know the only bottleneck really is when the the system gets filled yeah you just dive in the architecture has that been your experience if this so reduced or eliminated traditional storage bottlenecks oh absolutely and you know I mentioned before that this is sort of formance is now becoming afterthought to me you know and a little bit the way we look at our storage platform is weet from a performance standpoint not a capacity standpoint we can throw whatever we want at the infinite at and sort of the running joke internally is it will just smile and say is that all you got you mean like mixed workload so you don't have to sort of tune each array for a particular workload yeah yeah and you know I can imagine as someone that might be listening to what I'm saying well hey come on you know they can't really be that good and I'm I'm telling you from seeing a day-to-day again you can just throw the workloads at it and it will do what it says it does you don't see that every day now as far as capacity goes you know they there's capacity on demand model which you know we're a huge fan of they also have some other models the flex model which is very useful for budgeting purposes what I will tell you is you have to sacrifice at least one floor tile for an affinity it's very off-putting at first on day one and I remember my reaction but again as I saying earlier when you start peeling back two pieces of the technology and why these things are and the different flexibility on the financial side you realize that this actually isn't a downside it's an upside so the asset leverage of that floor tile as well exactly also make a big deal about a petabyte yes Gail is it important to you or what kind of scale are we talking about in terms of if you can share yeah absolutely so you know we obviously have multiple petabytes of storage for thrive for our customers again you know when someone has a large data set if we were to say we cannot handle that we're gonna be out of business pretty quickly this is one of the things the infinite flexibility of the public cloud again if you consider the public cloud both our competition and our partner you know we need to be able to offer that same kind of electricity in that same kind of endless capacity and at this point although I don't have completely unless capacity I have a tremendous amount of options I have workloads I can move different places and again a lot of times now it's more about performance than it is capacity oh you gotta give me something okay something that you wanna that should be doing to make your life better yeah I mean I gotta tell you it solves so many problems that is actually hard to come up with and again I'm smiling here because I've been down this road with those storage providers I've been let down by other storage writers I guess the son degree I maybe I'm waiting for them to let me down but I don't think they're going to that's a really interesting part I think that I'm you know the new trees cloud which is something that's been added over time you know a public cloud interaction is something that is desperately needed in the storage space so I'm interested to see how that product grows if I'm gonna give you something you know but again these are enablement platforms these aren't you know we need to do a feature comparison between a cloud and a public cloud and a private cloud last question some gifts are stuff you're working on yes II always like the SCT oh is that question yeah you know one of the the really interesting things to me is that we're finally getting there with anomaly detection not only you know just pure we found one event that that went out somewhere that doesn't make sense but we're profiling user behavior now AI and machine learning has been one of the big items that we've been promised for years but a lot of times it was just a tag line I think a lot of things that are happening in the public cloud computing space around profiling users and being able to reduce the amount of noise in the security space I think we're finally here and I think you know in the next 12 to 18 months AI isn't gonna become a cool feature said it's going to become a standard of a lot of security products so applying machine intelligence to a lot of the data that you have a lot of metadata yeah infrastructure metadata yeah yeah and you know even if you take for instance you know I'll pull it back to our storage conversation earlier if there's a storage activity is some sort of activity that's outside the norm that actually could be a security incident itself so you know pulling in data feeds is something that we've conquered its what are you gonna do with it now and we needed some humans to be able to pull that off before I think AI and machine learning is finally at the point where it's not out of reach for your average customer it doesn't take someone with a data analytics degree or something like that we can now buy these kind of products off the shelf and and leverage them for a lot of value oh Michael you've been a great guest thanks so much if you're welcome back anytime all right happy to be here all right and thank you for watching everybody this is Dave Volante in the cube we'll see you next time

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody this is Dave lotta a and this is the cube the leader in live tech coverage this cube conversation I'm really excited Craig Hibbert is here he's a vice president of infinite at and he focuses on strategic accounts he's been in the storage business for a long time he's got great perspectives correct good to see you again thanks for coming on good to say that good to be back so there's a there's a saying don't fight fashion well you guys fight fashion all the time you got these patents you got this thing called neuro cache you're your founder and chairman mo che has always been - cutting against the grain and doing things his own way but I'd love for you to talk about some of those things the patents that you have some architecture the neuro cache fill us in on all that sure so when we go in we talk to customers and we say we have a hundred and thirty-eight patents a lot of them say well that's great but you know how does that relate to me a lot of these are and or gates and certain things that they don't know how it fits into the day to day life so I think this is a good opportunity to talk about several of those that do and so obviously the neural cache is something that is is dynamic instead of having a key in a hash which all the other vendors have just our position in that table allows us to determine all the values and things we need from it but it also monitors this is an astounding statement but from the moment that array is powered on every i/o that flows through it we track data for the life of the reins for some of these customers it's five and six years so you know those blocks of data are they random are they sequential are they hot are they cold when was the last time was accessed and this is key information because we bring intelligence to the lower level block layer where everybody else has just done they just ship things things come into acutely moving they have no idea what they are we do and the value around that is that we can then predict when workloads are aging out today you have manual people writing things in in things like easy tier or faster or competing products or two stories right and all these things that that manage all these problems are the human intervention we do it dynamically and that feeds information back into the Ray and helps to determine which virtual ray group it should reside on and where on the discipline Dalls based upon the age of the the application how it's trending the these are very powerful things in a day where we need eminent information send in to a consumer in a store I'd it's all all this dynamic processing and the ability to bring that in so that's that's one of the things we do another one is that the catalyst for our fast rebuilds we can rebuild two failed full 12 terabyte drives in under 80 minutes if those drives are half full then it's nine minutes and this is by understanding where all the data is and sharing the rebuild process from the drives that's another one of our patterns perhaps one of the most challenging that we have is that storage vendors tend to do error correction at the fibre channel layer once that data enters into the storage array there is no mechanism to check the integrity of that data and a couple of vendors have an option to do this but they can only do it for the first right and they also recommend you to turn that feature off because it slows down the box so we're infinite out is unique and I think this is for me one of the the most important paths that we have is that every time we ride a 64k slice in the system we assign some metadata to that and obviously it has a CRC check sum but more importantly it has the locality of reference so if we subsequently go back and do a reread and the CRC matches but the location has changed we know that corruption has happened sometimes a bit flipped on right all of these things that constitute sound data corruption that's not just the impressive part what we do at that point is we dynamically deduce that the data has been corrupt and using the parity in the quorum where it were a raid 6 like a dual parity configuration we rebuild that data on the fly without the application or the end-user knowing that there was a problem and that way served back the data that was actually written we guarantee that were the only array that does that today there's massive for our customers I mean the time to rebuild you said 12 terabyte drive I mean I yeah I would have thought I mean they always joke how long do you think it takes to rebuild a 30 terabyte drive because eventually you know sure you know it's like a month with us it's the same so if you look at our three terabyte drives it was 18 minutes the four terabyte drives 18 minutes the 618 minutes 812 will be good all the way up to 20 terabyte drives figuration we have no what I came back to a conversation we've had many many times we've shown you guys we were early on in the flash storage trend and we saw the prices coming down we done like high-speed spinning disks were there days were numbered and sure correct in that prediction but then you know disk drives have kept that distance yeah you guys have a skewed going all flash because the economics but help us understand this because you've got this mechanical device and you yet you guys are able to claim performance that's equal to or oftentimes much much better than a lot of your all flash competitors and I want to understand that a little bit it suggests to me that there's so much other overhead going on and other ball necks in the system that you guys are dealing with both architectural II and through your intelligence software can you talk about that absolutely absolutely the software is the key right we are a software company and we have some phenomenal guys that do the software piece so as far as the performance goes the the backend spinning discs are really obfuscated by two layers of virtualization and we ensure that because we have massive amounts of DRAM that all of that data flows into DRAM it will sit in DRAM for an astonishing five minutes I say astonishing because most of our vendors try to evict cache straight away so they've got room for the next one and that does not facilitate a mechanism by which you can inspect those dumb pieces of data and if you get enough dumb data you can start to make him intelligent right you can go get discarded data from cell phone towers and find out we know where people go to work and what time they worker because of that what demographic at the end and you know now you're predicting the election based upon discarding itself on talladega so so if you can take dumb data and put patterns around it and make it sequential which we do we write out a log structured right so we're really really fast at the front-end and some customers say well how do you manage that on the backend here's something that our designers and architects did very very well the the speed of the of ddr3 is about 15k per second which is what Cindy REM right now we have 480 spindles on the backend if you say each one of them can do a hundred 100 mics per second which they can do more than that 200 that gives us a forty eight gigabit gigabyte sorry per second backplane D stage ability which is three times faster than the DRAM so when you look at it the box has been designed all the way so there is no bottleneck through flowing through the DRAM anything that still been access that comes out of that five minute window once it's D stays to all the spindles incidentally analog structured right so right now it over 480 spindles all the time and then you've got the random still on the SSD which will help to keep that response time around about 2 milliseconds and just one last point on there I have a customer that has 1.2 petabytes written on a 1.3 a petabyte box and is still achieving a 2 millisecond response time and that's unheard of because most block arrays as you fill them up to 60 70 % that the performance starts going in the tank so I go down memory lane here so the most successful you know storage array in the history of the industry my opinion probably fact it was symmetric sand mosha a designed that he eschewed raid5 everybody was on the crazy about raid 5 is dead no no just mirror it yeah and that's gonna give us the performance that we need and he would write they would write 2d ran and then then of course you'd think that the D stage bandwidth was the bottleneck because they had such a back high a large number of back-end spindles the bandwidth coming out of that DRAM was enormous you just described something actually quite similar so that I was going to ask you is it the D stage bandwidth the bottleneck and you're saying no because your D stage being what there's actually three tighter than the D rate up it is so with the symmetric some typical platforms you would have a certain amount of disk in a disk group and you would assign a phase and Fiber Channel ports to that and there'd be certain segments in cash that would dedicated those discs we have done away with that we have so many well with two layers of the virtualization at the front as we talked about but because nothing is a bottleneck and because we've optimized each component the DRAM and I talked about the SSDs we don't write heavily over those we write in a sequential pattern to the SSD so that the wear rate is elongated and so because of that and we have all the virtualized raid groups configured in cache so what happens is as we get to that five-minute window we're about 2 D state all of the raid groups the al telling the cash how to lay out the virtual raid structure based on how busy or the raid groups are at the time so if you were to pause it and ask us where it's going we can tell you it's the Machine line it's the artificial intelligence of saying this raid group just took a D stage you know or there's a lot of data in the cache that's heading for these but based upon the the prediction of the heart the cold that I talked about a few months ago and so it will make a determination to use a different virtual rater and that's all done in memory as opposed to to rely on the disk so we're not we don't have the concept of spare disk we have the concept of spare capacity it's all shared and because it's all shared it's this very powerful pool that just doesn't get bogged down and continues to operate all the way up to the full capacity so I'm struggling with this there is no bottleneck because there's always a problem that can assure them so where is the bottleneck the ball net for us is when the erase fault so if you overwrite the maximum bandwidth and that historically you know in in 2016-2017 was a roughly 12 cube per second we got that in the fall 2018 to roundabout 15 and we're about to make the announcement that we've made tectonic increases in that where will now have right bandwidth approach in 16 gig per second and also read bandwidth about 25 K per second that 16 is going to move up to 20 remember what I said we release a number and we gradually grow into it and and and maximize and tweak that software when you think that most or flash arrays can do maybe one and a half gig per second sustained writes that gives us a massive leg up over our competition instead of buying an all flash array for this and another mid-tier array for this and coal social this you can just buy one platform that services at all all the protocols and they're all access the same way so you write an API one way mark should almost as big fan of this about writing code obviously was spinnaker and some of those other things that he's been involved in and we do the same thing so our API is the same for the block as it is for the NAS as it is for the ice cozy so it's it's very consistent you write it once and you can adapt multiple products well I think you bring about customers for short bit everybody talks about digital transformation and it's this big buzzword but when you talk to customers they're all going through some kind of digital transformation oh they want to get digital right let's put it that way yeah I don't want to get disrupted they see Amazon buying grocers and while getting into the financial services and content and it's all about the data so there's a real disruption scenario going on for every business and and the innovation engine seems to be data okay but data just sitting there and a data swamp is no good so you got to apply machine intelligence for that to that data and you got to have scale mm-hmm do you guys make a big deal about about petabyte scale yeah what are your customers telling you about the importance of that and how does it fit into that innovation sandwich that I just laid out sure no it's great question so we have some very because we're so have 70 petabytes of production over those 70 yep we have a couple of those both financial institutions very very good at what they do we worked with them previously with a with another product that really kind of introduced another one of most Shea's products that was XIV that introduced the concepts of self-healing and no tuning and things like we don't even talked about that there's no tuning knobs on the infinite I probably should mention that but our customers said have said to us we couldn't scale you know we had a couple hundred terabyte boxes before there were okay you know you've brought you've raised the game by bringing in a much higher level of availability and much higher capacity we can take one of our but I'm in this process right now the customer we can take one of our boxes and collapse three vmax 20 of VMAX 40s on it we have numerous occassions gone into establishments that have 11 12 23 inch cabinets two and a half thousand spindles of the old DMC VMO station we've replaced it with one 19-inch rack of arts right that's a phenomenal state when you think about it and that was paid for you think some of these v-max 47 it's 192 ports on them Fiber Channel ports we have 24 so the fibre channel port reduction the power heating and cooling over an entire row down to one eight kilowatt consumption by the way our power is the same whether it's three four terabytes six eight twelve they all use the same power plan so as we increase the geometry capacity of the drives we decrease the cost per usable well we're actually far more efficient than all fly sharing with the most environmentally friendly hybrids been in this planet on the array so asking about cloud so miss gray on the planet that would be yeah so when cloud first sort of came out of the division Financial Services guys are like no clouds that's a bad word they're definitely you know leaning into that adopting it more but still there's a lot of workloads that they're gonna leave on Prem they want to that cloud experience to the data what are you hearing from the financial services customers in particular and I and I've single them out because they're they're very advanced they're very demanding they are they a lot of dough and so what do you see in terms of them building cloud hybrid cloud and and what it means for for them and specifically the storage industry yeah so I'm actually surprised that they've adopted it as much as they have to be honest with you and I think the the economics are driving that but having said that whenever they want to get the data back or they want to bring it back home prime for various reasons that's when they're running into problems right it's it's like how do I get my own data back well you've got to open up the checkbook and write big checks so I think infini debt has a nice strategy there where we have the same capabilities that you have on prime you having the cloud don't forget nobody else has that one of the encumbrances to people move into the cloud has been that it lacks the enterprise functionality that people are used to in the data center but because our cost point is so affordable we become not only very attractive or four on Prem but for cloud solutions as well of course we have our own new tricks cloud offering which allows people to use as dr or replications and so however you want to do it where you can use the same api's and code that your own dis and extrapolate that out to the cloud I was there which is which is very helpful and so we have the ability if you take a snapshot on Amazon it may take four hours and it's been copied over to an s3 device that's the only way they can make it affordable to do it and then if you need that data back it's it's not it's not imminent you've got to rehydrate from s3 and then copy it back over your snapshot with infinite data its instantaneous we do not stop i/o when we do snapshots and another one the patterns we use the time synchronous mechanism every every AO the rise has a timestamp and we when we take a snapshot we just do a point in time and in a timestamp that's greater than that instantiation point is for the volume and previous is for the snapshot we can do that in the cloud we can instantly recover hundreds of terabytes worth of databases and make them instantly available so our story again with the innovation our innovation wasn't just for for on pram it was to be facilitated anyway you are and that same price point carries forward from here into the cloud when Amazon and Microsoft wake up and realized that we have this phenomenal story here I think they'll be buying from us in leaps and bounds it's it's the only way to make the cloud affordable for storage vendors so these are the things you talk about you know bringing bringing data back and bringing workloads back and and there are tool chains that are now on Prem the kubernetes is a great example that our cloud like and so when you bring data back you want to have that cloud experience so automated operations plays into that you know automation used to be something that people are afraid of and they want to do do manual tearing member they wanted their own knobs to turn those days are gone because people want to drive digital transformations they don't want to spend time doing all this heavy lifting I'm talk about that a little bit and where you guys fit yeah I mean you know I say to my customers to not to knock our competition but you can't have a service processor as the inter communication point between what the customer wants and it deciding where it's going to talk to the Iranian configure it's going to be instantaneous and so we all we have we don't have any Java we don't have any flash we don't have any hosts we don't have massive servers around the data center collecting information we just have an html5 interface and so our time to deployment is very very quick when we land on the customer's dark the box goes in we hook up the power we put the drives in we're Haiti's the word V talk because it brings back memories for a lot of course I am now we're going back in time right knowing that main here and so we're very dynamic both in how we forward face the customers but also on the backend for ourselves we eat our own dog food in the sense that we are we have an automation team we've automated our migration from non infinite out platforms towards that uses some level of artificial intelligence we've also built a lot of parameters around things like going with ServiceNow and custom sites because well you can do with our API what other people take you know page and page of code I'll give you an example one of our customers said I need OC i the the let-up management product we called met up and they said hey listen you know it usually takes six months to get an appointment and that it takes at least six months to do the comb we said no no we're not like any other storage render we don't have all these silly raid groups and spare disk capacity you know this weave three commands we can show in the API and we showed them the light Wow can you send us an array we said no we can do something better we were designed SDS right when when infinite out was coded there was no hardware and the reason we did that is because software developers will always code to the level of resilience of the hardware so if you take away that Hardware the software developers have to code to make something to withstand any type of hardware that comes in and at the end of the coding process that's when we started bringing in the hardware pieces so we were written STS we can send vendors and customers a an OVA a virtual appliance of our box they were able to the in a week they told the custom we have to go through full QA no reason why it wouldn't work and they did it for us and got it was a massive customer of theirs and ours that's a powerful story the time to deployment for your homegrown apps as well as things like ServiceNow an MCI incredible infinite out three API calls we were done so you guys had a little share our partnership with met up in the field we did yeah I mean was great they had a massive license with this particular customer they wanted our storage on the platform and we worked very very quickly with them they were very accommodating and we'd love to get our storage qualified behind their behind their heads right now for another customer as well so yeah there's definitely some sooner people realize what we have a Splunk massive for us what we're able to do was plunk in one box where people the competitors can't do in a row so it so it's very compelling what we actually bring in how we do it and that API level is incredibly powerful and we're utilizing that ourselves I would like to see some integration with canonical Marshall what these guys have done a great job with SDS plays we'd like to bring that here do spinnaker do collect if I could do some of those things as well that we're working on the automation we just added another employee another FTE to the automation team and infinite out so we do these and we engage with customers and we help you get out of that trench that is antiquity and move forward into the you know into the vision of how you do one thing well and it permeates the cloud on primary and hybrid all those guys well that API philosophy that you have in the infrastructure is code model that you just described allows you to build out your ecosystem in a really fast way so Greg thanks so much for coming on thank you and doing that double click with this really I'd love to have you back great thanks a lot Dave all right thank you welcome thank you for watching you're watching the cube and this is Dave Volante we'll see you next time

it's the cue now here's your host stool minimun hi this is Stu miniman with Wikibon with a special presentation of the cube here at the ribbon cutting at infinite at new briefing center in Waltham Massachusetts excited to have with me Brian Carmody who is the CTO of infinite at Brian thanks for joining me hey Stuart how you doing I'm doing great so we've talked to some of your team here got on the inside so here we're outside but we're going to be digging into some of the innards of what's going on in the industry so yeah Brian you know not much has been going on in the storage industry let me see in the last month we had the you know finalization of the largest kind of acquisition / merger in the industry of technology with Dell buying EMC and Newt annex just IP ode so you know when we've got the CTO we always want to kind of dig in you know what what's in your head what what are the big kind of mega trends that you're seeing and how's that impact what you're doing yeah so this was obviously it's been a crazy era of innovation for us I would even just looking back at the past two years you know 2016 or let's say 2015 was kind of the year that storage got fast it was the year that NAND flash at the knee of the adoption curve and every marketing person everywhere was hashtag AFA and then 2016 was kind of the year we think that storage got commoditized this was when software-defined in hyper converged technologies kind of hit the knee of the adoption curve it was capped just like 2015 was capped by the pure IPO 2016 was capped by the mechanics IPO telling is a really interesting question of what comes next like what's the next mountain that we're going to climb as an industry and we're hearing really interesting things from customers about what their priorities and what their challenges are so first off going into 2017 one of the really interesting phenomenon is that the requirements from traditional enterprise let's say a CTO of a bank that's building a next-generation data center versus a mega cloud provider those requirements are starting to converge so this idea that the cloud is one thing an enterprise is something else I think we're starting to kind of move past that obviously there's a huge trend still going on for compute heavy workloads to move off premise into into public clouds and kind of a net flow of storage heavy workloads tend to move on premise but I think we're at the point now where we're kind of reaching an equilibrium point between those two so that is certainly one trend another thing that we're hearing very much about is the the rise of new KPIs for measuring IT systems acquisition cost is still a big piece of the equation but new technologies are new new new metrics like power space and cooling are becoming very critically important because with the transition to the cloud even CIOs of very large fortune 500 companies their computations are often happening for the first time now in spaces where they are a tenant in someone else's get they are renting space or renting capacity so all this is putting a lot of pressure on systems designers to really focus on density of storage and density of computation and you know we see that this is contributing to the rise of a new class of storage technologies called hyper storage systems which are designed to to meet those goals all right so so Brian I'm I've tried to create different market categories before when I join Wikibon it was hyperscale invades the enterprise when people before were they were talking about hyper converged asthma much we talked about we called it server San actually because it was you know the benefits of ass and brought to the server but you know so you've got that term hyper in there is that hyper scales and hyper converge is it some other you know hyperness you know what what's what's the general idea you're trying to get food this new category it so let's take a look at kind of the existing commercially available technologies and it's kind of interesting to look at it and think about it on a two-dimensional axis of looking at the density and then the latency so you have the for example the traditional monoliths these are latency that's low enough for primary storage they do not tend to be very dense you know they're under a petabyte of storage per rack and that's where the industry began that's what a lot of us kind of cut our teeth on there being superseded by all-flash arrays these are higher density because they have data reduction technologies built-in natively into their data paths they have better latency so they're kind of moving up and to the right with respect to the monoliths but they come at a price they tend to be exceedingly expensive and relatively small systems then you have the SDS and the server storage and the scale out stuff they tend to be very close to the density of the monoliths again a half about half a petabyte to a petabyte per rack a regression on latency but they're being widely adopted because the costs are just so much better than the monoliths and the AFA is and that's the entire enterprise storage industry right in this area here now all the way down if you move to higher density you have systems like the Facebook open vault so this is you know an awesome open source storage system that I'm that Facebook developed it's the basis of haystack and f4 two of the largest storage systems in the world right now these are incredibly dense storage systems multiple petabytes per rack but they're very high latency they're used for cold data only and other things like Amazon glacier and whatnot are kind of all clustered down in that high latency but super dense so hyper storage is if you move around that two-dimensional chart is the upper right-hand quadrant it is storage technology that has the reliability of monoliths it has the cost structure the programmability and the the ability to run on any type of hardware that the SDS systems have but it has the density and the data center profile of the hyper scalar storage so this is completely uncharted territory this is where all of the R&D spend companies in like Google and Amazon everybody's racing to try to go figure this out and this is the kind of wild west where we operate we have a three year head start I'm a on-prem part of this but it's not going to last because this is you know it's the remaining uncharted territory in the industry really interesting so you've heard it first hyper storage definitely something I've been hearing for number of years is especially the big financial guys have been they've had hyperscale Envy is really what it was there like you know we spent huge amounts of budgets on IT you know we know our stuff how dare you know a bunch of retail guys basically you know come in here and think that they understand this space so it sounds like you're bringing some of that back to them um you know is infinite out the only ones you know you mentioned some of the you know kind of Google and Facebook are there anybody else that's kind of packaging this for the enterprise other than infinite at not yet so the sum of the of our competitors that are building their systems out of all flash technologies are moving in the right direction but their their media itself has to become a lot more dense and the cost has to drop significantly until they can be realistic plays and until then it's going to be differentiated by scale when you want to do petabyte scale stuff you do it with art with hyper storage architectures and one you want to be small and tight and fast you do it with a FAS but I think those lines will be blurred over time great so it sounds like you've got a clear differentiation compared to kind of just the the software-defined pieces don't deliver on you know some of the density that you're talking about or some of the high level performance when we start getting things like nvme i hear is going to be a game changer for the the hyperscale pieces do we start to see the blurring of lines between some of these architectures or is this something that you know two years from now you're going to say ok here's the last wave and here's the next wave yeah so I mean if you take a longer view instead of two years I mean if we if we look at a five and a ten year view this is all there is going forward there's hyperscale architectures or disaggregated architectures which are for compute heavy storage light workloads and then you have hyper storage which is for storage heavy compute neutral workloads and going forward if you take a 10-year window that's all there is out there well Brian I'm hoping we can do a whiteboard with you sometime in the future or maybe there'll be some kind of you know thesis on you know the kind of the hyperscale the hyper storage category but appreciate you here sharing it with our audience here I want to give you the final word as to kind of you know that the hard work that still needs to be done in the storage industry over the next few years go Patriots alright well we'll drop the mic there thank you brian says is so much for joining us and thank you for watching the cube