(bright intro music) >> The cloud has popularized many useful concepts in the past decade, working backwards from the customer to pizza teams and DevOps mindset, the shared responsibility model, and security, of course, the shift from CapEx to OPEX, and as a service consumption models. The last item is what we're here to talk about today. Pay for consumption is attractive because you're not over provisioning, at least not the way you used to. You'd have to buy for peak capacity events, but there are always two sides to every story, and while pay for use more closely ties IT consumption to business value, procurement teams don't always love the uncertainty of the cloud bill each month, but consumption pricing and as a service models are here to stay in software and hardware. Hello, I'm Dave Vellante and welcome to "At Your Storage Service" made possible by Pure Storage, and with me is Prakash Darji who's the General Manager of the Digital Experience Business Unit at Pure. Prakash, welcome to the program. >> Thanks Dave. Thanks for having me. >> You bet. Okay, we've seen this shift to as a service, the as a service economy, subscription models, and this as a service movement have gained real momentum. It's clear over the past several years. What's driving this shift? Is it pressure from investors and technology companies that are chasing the all important ARR, their annual recurring revenue stream? Is it customer driven? Give us your insights. >> Well, look, I think we'll do some definitional stuff first. I think we often mix the definition of a subscription and a service, but, you know, subscription is, "Hey, I can go for a pay up front or pay as I go." Service is more about, "How do I not buy something just by the outcome?" So, you know, the concept of delivering storage as a service means, what do you want in storage, performance, capacity, availability? Like that's what you want. Well, how do you get that without having to worry about the labor of planning, capacity management? Those labor elements are what's driving it. So I think in the world where you have to do more with less and in a world where security becomes increasingly important where standardization will allow you to secure your landscape against ransomware and those types of things, those trends are driving the SaaSification of storage, and the only way to deliver that is storage as a service. >> So that's good. You maybe thinking about it differently than some of the other companies that I talked to, but so you've made inroads here, pretty big inroads actually, and changed the thinking in enterprise data storage with a huge emphasis on simplicity. That's really Pure's raison d'etre. How does storage as a service fit in to your innovation agenda overall? >> Well, our innovation agenda started, as you mentioned, with the simplicity, you know, a decade ago with the Evergreen Architecture. That architecture was beyond the box. How do you go ahead and say, "I can improve performance or capacity as I need it." Well, that's a foundational element to deliver a service because once you have that technology, you can say, "Oh, you know what? You've subscribed to this performance level. You want to raise your performance level and yes, that'll be a higher dollar per gig or dollar per terabyte," but how do you do that without a data migration? How do you do that with a non-disruptive service change? How do you do that with a delivery via software update? Those elements of non-disruptive updates, when you think SaaS, Salesforce, you don't know when Salesforce doesn't update. You don't know when they're increasing something, adding a new capability. It just shows up. It's not a disruptive event. So to drive that standardization and SaaSification in service delivery, you need to keep that simplicity of delivery first and foremost, and you can't allow, like, if the goal was, "I want to change from this service here to that service here," and a person needed to show up and do a day data migration, that's kind of useless. You've broken the experience of flexibility for a customer. >> Okay, so I like the Salesforce analogy, but I want to jump out do a little side for a second. So I've got to make some commitment to Pure, right? Some baseline commitment, and if I do, then I can dial up and then pay for what I use, and I can dial it down, correct? >> Correct. >> Okay. I can't do that with Salesforce, all right? I could dial up, but then I'm stuck with those licenses. So you have a better model in Salesforce, I would argue. Okay. >> Yeah. I would agree with that. >> Okay, so, and I got to pay for everything up front. Anyway, let's go back to I was kind of pushing at you a little bit at my upfront, you know, about, you know, the ARR model, the all-important, you know, financial metric, but let's talk from the customer standpoint. What are the benefits of consuming storage as a service from your customer's perspective? >> Well, one is when you start your storage journey, do you really know what you need? And I would argue, most of the time, people are guessing, right? It's like, "Well, I think I need this. This is the performance I think I need or this is the capacity I think I need," and, you know, with the scientific method, you actually deploy something, and you're like, "Do I need more? Do I need less?" You find out as you're deploying. So in a storage as a service world, when you have the ability to move up performance levels or move out capacity levels, and you have that flexibility, then you have the ability to just to meet demand as you deploy, and that's the most important element of meeting business needs today. The applications you deploy are not in your control when you're providing storage to your end consumers. >> Yeah. >> They're going to want different levels of storage. They're going to want different performance thresholds. That's kind of a pay, you know, pay for performance type culture, right? You can use HR analogy for it. You pay for performance. You want top talent, you pay for it. You want top storage performance, you pay for it. You don't, you can pay less, and you can actually get lower performance tiers. Not everything is a tier one application, and you need the ability to deploy it, but when you start, how do you know the way your end customers are going to be consuming or do you need a dictated upfront? 'Cause that's infrastructure dictating business inflexibility, and you never want to be in that position. >> I got another analogy for you. It's like, you know, we do a lot of hosting at our home and you know, like Thanksgiving, right? And you go to the liquor store and say, "Okay, what should I get, should we get red wine? We got to go white wine, we got to get some beer. Should I get bubbles? Yeah, I get some bubbles." 'Cause you don't know what people are going to have, and so you over provision everything and then there's a run on bubbles and you're like, "Ah, we're running the bubbles," so you just over buy, but there's a liquor store that actually will take it back. So I got to do business with those guys every time 'cause it's way more flexible. I can dial up capacity or I can dial up performance, and dial it back down if I don't use it. >> Where you're going to be drinking a lot more the next few weeks. >> Yeah, exactly, like which is the last thing you want. Okay, so let's talk about how Pure kind of meets this as a service demand. You've touched upon your differentiators from others in the market. You know, love to hear about the momentum. What are you seeing out there? >> Yeah, look, our business is growing well largely built on, you know, what customers need. Specifically, where the market is at today is there's a set of folks that are interested in the financial transformation of CapEx to OPEX. Like that definitely exists in the industry around, "How do I get a paper use model?" The next kind of more advanced customer is interested in, "How do I go ahead and remove labor to deliver storage?" And a service gets you there on top of a subscription. The most sophisticated customer says, "How do I separate storage production with consumption and production of storage?" Being a storage producer should be about standardization so I could do policy based management. Why is that important? You know, coming back to some of the things I said earlier, in the world where ransomware attacks are common, you need the standardized security policies. Linux has new vulnerabilities every other day, like find two, three critical vulnerabilities a week. How do you stay on top of it? The complexity of staying on top of it should be, "Look, let's standardize and make it a vendor problem, and assume the vendor's going to deliver this to me." So that standardization allows you to have business policies that allow you to stay current and modern. I would argue in, you know, the traditional storage and appliance world, you buy something and the day after you buy it, it's worthless. It's like driving a car off a lot, right? The very next day, the car's not worth what it was when you bought it. Storage is the same way. So how do you ensure that your storage stays current? How do you ensure that it gets a like a fine line that gets better with age? Well, if you're not buying storage and you're buying a performance SLA, it's up to the vendor to meet that SLA so it actually never gets worse over time. This is the way you modernize technology and avoid technology debt as a customer. >> Yeah, I mean, just even though words you're using and the way you're thinking about this precaution, I think are different, and I love the concept of essentially taking my labor cost and transferring them to Pure's R&D. I mean, that's essentially what you're talking about here. So let's stick with the tech for a minute. What do you see as new or emerging technologies that are helping accelerate this shift toward the as a service economy? >> Well, the first thing is I always tell people you can't deliver a service without monitoring because if you can't monitor something, how you're going to know whether you're meeting your service level obligation, right? So everything starts with data monitoring. The next step layering on the technology differentiation is if you need to deliver a service level obligation on top of that data monitoring, you need the ability to flexibly meet whatever performance obligations you have in a tight time window. So supply chain and being able to deliver anywhere becomes important. So if you use the analogy today of how Tesla works or a IoT system works, you have a SaaS management that actually provides instructions that pushes those instructions and policies to the edge. In Tesla's case, that happens to be the car. It'll push software updates to the car. It'll push new map updates to the car, but the car is running independently. It's not like if the car becomes disconnected from the internet, it's going to crash and drive you off the road. In the same way, what if you think about storage as something that needs to be wherever your application is? So people think about cloud as a destination. I think that's a fallacy. You have to think about the world in the view of an application. An application needs data, and that data needs to sit in storage wherever that application sits. So for us, the storage system is just an edge device. It can be sitting in your data center it can be sitting in a Equinix. It can be sitting in hosted and MSP can run it. It can even be sitting in the public cloud, but how do you have central monitoring and central management where you can push policies to update all those devices, very similar to an IOT system? So the technology advantage of doing that means that you can operate anywhere and ensure you have a consistent set of policies, a consistent set of protection, a consistent set of, you know, prevention against ransomware attack regardless of your application, regardless of, you know, where it sits, regardless of what content in it you're on. That approach is very similar to the way the IoT industry has been updating and monitoring edge devices, nest thermostats, you know, Tesla cars, those types of things. That's the thinking that needs to come to storage, and that's the foundation on which we built Pure as a service. >> So that implies or, at least I infer, that you've, obviously, got control of the experience on prem, but you're extending that into AWS, Google, Azure, which suggests to me that you have to hide the underlying complexity of the primitives and APIs in that world, and then eventually, actually today, 'cause you're treating everything like the edge out to the edge, you know, maybe mini Pure at some point in time, but so I call that super cloud, that abstraction layer that floats above all the clouds on-prem and adds that layer of value and is a singular experience, what you're talking about pushing, you know, policy throughout. Is that the right way to think about it? And how does this impact the ability to deliver true storage as a service? >> Oh, that's absolutely the right way of thinking about it. The things that you think about from an abstraction kind of fall in three buckets. First, you need management. So how do you ensure consistent management experience, creating volumes, deleting volumes, creating buckets, creating files, creating directories like management of objects and create a consistent API across the entire landscape? The second one is monitoring. How do you measure utilization and performance obligations or capacity obligations or, you know, policy violations, wherever you're at? And then the third one is more of a business one, which is procurement because you can't do it independent of procurement, meaning what happens when you run out? Do you need to increase your reserve commits? Do you want to go on demand? How do you integrate it into company's procurement models such that you can say, "I can use what I need," and any, it's not like every change order is a request of procurement. That's going to break an as a service delivery model. So to get embedded in a customer's landscape where they don't have to worry about storage, you have to provide that consistency on management, monitoring, and procurement across the tech, and yes, this is deep technology problems, whether it's running our storage on AWS or Azure or running it on prem or, you know, at some point in the future, maybe even, you know, Pure mini at the edge, right? So, you know, all of those things are tied to our Pure as a service delivery. >> Yeah, technically, non-trivial, but hey, you guys are on it. Well, we got to leave it there, Prakash. Thank you, great stuff, really appreciate your time. >> All right, thanks for having me, man. >> You're very welcome. Okay, in a moment, Steve McDowell. For more insights and strategies, he's going to give us the analyst perspective on as a service. You're watching "theCUBE", the leader in high tech enterprise coverage. (bright outro music)

>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition at fraud waste and abuse per the government accountability office is broad as an attempt to obtain something about a value through unwelcomed misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal, uh, benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically for the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external perpetrators, again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, uh, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, those are broad stroke areas. What are the actual use cases that, um, agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use great, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment integrity. So fraud has its, um, uh, underpinnings in quite a few different government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that Chev is going to talk about later it's how do I look at more, that real, that streaming information? >>How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in essence, looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in, um, some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, uh, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific constituent, are there areas where we're seeing, uh, um, other aspects of a fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at, uh, simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. >>Um, and again, that really lends itself to a new opportunities. And on that, I'm going to turn it over to Shev to talk about, uh, the reference architecture for, uh, doing these baskets. >>Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection using, uh, Cloudera underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our data sets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so then comes clutter's platform and this reference architecture that needs to before you, so, uh, let's start on the left-hand side of this reference architecture with the collect phase. >>So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different porosities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with clutter data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL string builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or exploratory data analysis and visualization, uh, can all be enabled through clever visual patient technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutter's technology, right? And so, uh, the IRS is one of, uh, clutters customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually, uh, recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around, uh, how quad areas working in the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining Chevy and I today, we greatly appreciate your time and look forward to future >>Conversation..

>>Hi, this is Cindy Mikey, vice president of industry solutions at Cloudera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and Shev we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud is an attempt to obtain something about a value through unwelcomed. Misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external are perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's broad stroke areas? What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has its, um, uh, underpinnings in quite a few different on government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to lock at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case, that shadow is going to talk about later it's how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So quite a different variety of data and the, the breadth, um, and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a sense, looking at a more extensive on data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be, um, investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like, uh, constituent, are there areas where we're seeing, uh, um, other aspects of, of fraud potentially being, uh, occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at simulation, Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, the public sector. >>Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to Chevy to talk about, uh, the reference architecture for doing these buckets. >>Sure. Yeah. Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection, using Cloudera as underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or anomalous behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create from normal behavior profiles and these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jace on or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to know downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin is going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer API APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transform data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on from machine learning, right? So the next step in the architecture is to leverage, uh, clutter SQL stream builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real-time. Uh, I'll, you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage Q2, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed, and we've our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, even deep learning techniques with neural networks. Uh, and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the X one, uh, scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. Uh, and this entire pipeline is powered by clutters technology. Uh, Cindy, next slide please. >>Right. And so, uh, the IRS is one of, uh, clutter as customers. That's leveraging our platform today and implementing a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, uh, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around how called areas working in federal government, by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us today. Uh, we greatly appreciate your time and look forward to future conversations. Thank you.

>>Individuals create developers, translate ideas to code, to create great applications and great applications. Touch everyone. A Docker. We know that collaboration is key to your innovation sharing ideas, working together. Launching the most secure applications. Docker is with you wherever your team innovates, whether it be robots or autonomous cars, we're doing research to save lives during a pandemic, revolutionizing, how to buy and sell goods online, or even going into the unknown frontiers of space. Docker is launching innovation everywhere. Join us on the journey to build, share, run the future. >>Hello and welcome to Docker con 2021. We're incredibly excited to have more than 80,000 of you join us today from all over the world. As it was last year, this year at DockerCon is 100% virtual and 100% free. So as to enable as many community members as possible to join us now, 100%. Virtual is also an acknowledgement of the continuing global pandemic in particular, the ongoing tragedies in India and Brazil, the Docker community is a global one. And on behalf of all Dr. Khan attendees, we are donating $10,000 to UNICEF support efforts to fight the virus in those countries. Now, even in those regions of the world where the pandemic is being brought under control, virtual first is the new normal. It's been a challenging transition. This includes our team here at Docker. And we know from talking with many of you that you and your developer teams are challenged by this as well. So to help application development teams better collaborate and ship faster, we've been working on some powerful new features and we thought it would be fun to start off with a demo of those. How about it? Want to have a look? All right. Then no further delay. I'd like to introduce Youi Cal and Ben, gosh, over to you and Ben >>Morning, Ben, thanks for jumping on real quick. >>Have you seen the email from Scott? The one about updates and the docs landing page Smith, the doc combat and more prominence. >>Yeah. I've got something working on my local machine. I haven't committed anything yet. I was thinking we could try, um, that new Docker dev environments feature. >>Yeah, that's cool. So if you hit the share button, what I should do is it will take all of your code and the dependencies and the image you're basing it on and wrap that up as one image for me. And I can then just monitor all my machines that have been one click, like, and then have it side by side, along with the changes I've been looking at as well, because I was also having a bit of a look and then I can really see how it differs to what I'm doing. Maybe I can combine it to do the best of both worlds. >>Sounds good. Uh, let me get that over to you, >>Wilson. Yeah. If you pay with the image name, I'll get that started up. >>All right. Sen send it over >>Cheesy. Okay, great. Let's have a quick look at what you he was doing then. So I've been messing around similar to do with the batter. I've got movie at the top here and I think it looks pretty cool. Let's just grab that image from you. Pick out that started on a dev environment. What this is doing. It's just going to grab the image down, which you can take all of the code, the dependencies only get brunches working on and I'll get that opened up in my idea. Ready to use. It's a here close. We can see our environment as my Molly image, just coming down there and I've got my new idea. >>We'll load this up and it'll just connect to my dev environment. There we go. It's connected to the container. So we're working all in the container here and now give it a moment. What we'll do is we'll see what changes you've been making as well on the code. So it's like she's been working on a landing page as well, and it looks like she's been changing the banner as well. So let's get this running. Let's see what she's actually doing and how it looks. We'll set up our checklist and then we'll see how that works. >>Great. So that's now rolling. So let's just have a look at what you use doing what changes she had made. Compare those to mine just jumped back into my dev container UI, see that I've got both of those running side by side with my changes and news changes. Okay. So she's put Molly up there rather than mobi or somebody had the same idea. So I think in a way I can make us both happy. So if we just jumped back into what we'll do, just add Molly and Moby and here I'll save that. And what we can see is, cause I'm just working within the container rather than having to do sort of rebuild of everything or serve, or just reload my content. No, that's straight the page. So what I can then do is I can come up with my browser here. Once that's all refreshed, refresh the page once hopefully, maybe twice, we should then be able to see your refresh it or should be able to see that we get Malia mobi come up. So there we go, got Molly mobi. So what we'll do now is we'll describe that state. It sends us our image and then we'll just create one of those to share with URI or share. And we'll get a link for that. I guess we'll send that back over to you. >>So I've had a look at what you were doing and I'm actually going to change. I think that might work for both of us. I wondered if you could take a look at it. If I send it over. >>Sounds good. Let me grab the link. >>Yeah, it's a dev environment link again. So if you just open that back in the doc dashboard, it should be able to open up the code that I've changed and then just run it in the same way you normally do. And that shouldn't interrupt what you're already working on because there'll be able to run side by side with your other brunch. You already got, >>Got it. Got it. Loading here. Well, that's great. It's Molly and movie together. I love it. I think we should ship it. >>Awesome. I guess it's chip it and get on with the rest of.com. Wasn't that cool. Thank you Joey. Thanks Ben. Everyone we'll have more of this later in the keynote. So stay tuned. Let's say earlier, we've all been challenged by this past year, whether the COVID pandemic, the complete evaporation of customer demand in many industries, unemployment or business bankruptcies, we all been touched in some way. And yet, even to miss these tragedies last year, we saw multiple sources of hope and inspiration. For example, in response to COVID we saw global communities, including the tech community rapidly innovate solutions for analyzing the spread of the virus, sequencing its genes and visualizing infection rates. In fact, if all in teams collaborating on solutions for COVID have created more than 1,400 publicly shareable images on Docker hub. As another example, we all witnessed the historic landing and exploration of Mars by the perseverance Rover and its ingenuity drone. >>Now what's common in these examples, these innovative and ambitious accomplishments were made possible not by any single individual, but by teams of individuals collaborating together. The power of teams is why we've made development teams central to Docker's mission to build tools and content development teams love to help them get their ideas from code to cloud as quickly as possible. One of the frictions we've seen that can slow down to them in teams is that the path from code to cloud can be a confusing one, riddle with multiple point products, tools, and images that need to be integrated and maintained an automated pipeline in order for teams to be productive. That's why a year and a half ago we refocused Docker on helping development teams make sense of all this specifically, our goal is to provide development teams with the trusted content, the sharing capabilities and the pipeline integrations with best of breed third-party tools to help teams ship faster in short, to provide a collaborative application development platform. >>Everything a team needs to build. Sharon run create applications. Now, as I noted earlier, it's been a challenging year for everyone on our planet and has been similar for us here at Docker. Our team had to adapt to working from home local lockdowns caused by the pandemic and other challenges. And despite all this together with our community and ecosystem partners, we accomplished many exciting milestones. For example, in open source together with the community and our partners, we open sourced or made major contributions to many projects, including OCI distribution and the composed plugins building on these open source projects. We had powerful new capabilities to the Docker product, both free and subscription. For example, support for WSL two and apple, Silicon and Docker, desktop and vulnerability scanning audit logs and image management and Docker hub. >>And finally delivering an easy to use well-integrated development experience with best of breed tools and content is only possible through close collaboration with our ecosystem partners. For example, this last year we had over 100 commercialized fees, join our Docker verified publisher program and over 200 open source projects, join our Docker sponsored open source program. As a result of these efforts, we've seen some exciting growth in the Docker community in the 12 months since last year's Docker con for example, the number of registered developers grew 80% to over 8 million. These developers created many new images increasing the total by 56% to almost 11 million. And the images in all these repositories were pulled by more than 13 million monthly active IP addresses totaling 13 billion pulls a month. Now while the growth is exciting by Docker, we're even more excited about the stories we hear from you and your development teams about how you're using Docker and its impact on your businesses. For example, cancer researchers and their bioinformatics development team at the Washington university school of medicine needed a way to quickly analyze their clinical trial results and then share the models, the data and the analysis with other researchers they use Docker because it gives them the ease of use choice of pipeline tools and speed of sharing so critical to their research. And most importantly to the lives of their patients stay tuned for another powerful customer story later in the keynote from Matt fall, VP of engineering at Oracle insights. >>So with this last year behind us, what's next for Docker, but challenge you this last year of force changes in how development teams work, but we felt for years to come. And what we've learned in our discussions with you will have long lasting impact on our product roadmap. One of the biggest takeaways from those discussions that you and your development team want to be quicker to adapt, to changes in your environment so you can ship faster. So what is DACA doing to help with this first trusted content to own the teams that can focus their energies on what is unique to their businesses and spend as little time as possible on undifferentiated work are able to adapt more quickly and ship faster in order to do so. They need to be able to trust other components that make up their app together with our partners. >>Docker is doubling down and providing development teams with trusted content and the tools they need to use it in their applications. Second, remote collaboration on a development team, asking a coworker to take a look at your code used to be as easy as swiveling their chair around, but given what's happened in the last year, that's no longer the case. So as you even been hinted in the demo at the beginning, you'll see us deliver more capabilities for remote collaboration within a development team. And we're enabling development team to quickly adapt to any team configuration all on prem hybrid, all work from home, helping them remain productive and focused on shipping third ecosystem integrations, those development teams that can quickly take advantage of innovations throughout the ecosystem. Instead of getting locked into a single monolithic pipeline, there'll be the ones able to deliver amps, which impact their businesses faster. >>So together with our ecosystem partners, we are investing in more integrations with best of breed tools, right? Integrated automated app pipelines. Furthermore, we'll be writing more public API APIs and SDKs to enable ecosystem partners and development teams to roll their own integrations. We'll be sharing more details about remote collaboration and ecosystem integrations. Later in the keynote, I'd like to take a moment to share with Docker and our partners are doing for trusted content, providing development teams, access to content. They can trust, allows them to focus their coding efforts on what's unique and differentiated to that end Docker and our partners are bringing more and more trusted content to Docker hub Docker official images are 160 images of popular upstream open source projects that serve as foundational building blocks for any application. These include operating systems, programming, languages, databases, and more. Furthermore, these are updated patch scan and certified frequently. So I said, no image is older than 30 days. >>Docker verified publisher images are published by more than 100 commercialized feeds. The image Rebos are explicitly designated verify. So the developers searching for components for their app know that the ISV is actively maintaining the image. Docker sponsored open source projects announced late last year features images for more than 200 open source communities. Docker sponsors these communities through providing free storage and networking resources and offering their community members unrestricted access repos for businesses allow businesses to update and share their apps privately within their organizations using role-based access control and user authentication. No, and finally, public repos for communities enable community projects to be freely shared with anonymous and authenticated users alike. >>And for all these different types of content, we provide services for both development teams and ISP, for example, vulnerability scanning and digital signing for enhanced security search and filtering for discoverability packaging and updating services and analytics about how these products are being used. All this trusted content, we make available to develop teams for them directly to discover poll and integrate into their applications. Our goal is to meet development teams where they live. So for those organizations that prefer to manage their internal distribution of trusted content, we've collaborated with leading container registry partners. We announced our partnership with J frog late last year. And today we're very pleased to announce our partnerships with Amazon and Miranda's for providing an integrated seamless experience for joint for our joint customers. Lastly, the container images themselves and this end to end flow are built on open industry standards, which provided all the teams with flexibility and choice trusted content enables development teams to rapidly build. >>As I let them focus on their unique differentiated features and use trusted building blocks for the rest. We'll be talking more about trusted content as well as remote collaboration and ecosystem integrations later in the keynote. Now ecosystem partners are not only integral to the Docker experience for development teams. They're also integral to a great DockerCon experience, but please join me in thanking our Dr. Kent on sponsors and checking out their talks throughout the day. I also want to thank some others first up Docker team. Like all of you this last year has been extremely challenging for us, but the Docker team rose to the challenge and worked together to continue shipping great product, the Docker community of captains, community leaders, and contributors with your welcoming newcomers, enthusiasm for Docker and open exchanges of best practices and ideas talker, wouldn't be Docker without you. And finally, our development team customers. >>You trust us to help you build apps. Your businesses rely on. We don't take that trust for granted. Thank you. In closing, we often hear about the tenant's developer capable of great individual feeds that can transform project. But I wonder if we, as an industry have perhaps gotten this wrong by putting so much emphasis on weight, on the individual as discussed at the beginning, great accomplishments like innovative responses to COVID-19 like landing on Mars are more often the results of individuals collaborating together as a team, which is why our mission here at Docker is delivered tools and content developers love to help their team succeed and become 10 X teams. Thanks again for joining us, we look forward to having a great DockerCon with you today, as well as a great year ahead of us. Thanks and be well. >>Hi, I'm Dana Lawson, VP of engineering here at get hub. And my job is to enable this rich interconnected community of builders and makers to build even more and hopefully have a great time doing it in order to enable the best platform for developers, which I know is something we are all passionate about. We need to partner across the ecosystem to ensure that developers can have a great experience across get hub and all the tools that they want to use. No matter what they are. My team works to build the tools and relationships to make that possible. I am so excited to join Scott on this virtual stage to talk about increasing developer velocity. So let's dive in now, I know this may be hard for some of you to believe, but as a former CIS admin, some 21 years ago, working on sense spark workstations, we've come such a long way for random scripts and desperate systems that we've stitched together to this whole inclusive developer workflow experience being a CIS admin. >>Then you were just one piece of the siloed experience, but I didn't want to just push code to production. So I created scripts that did it for me. I taught myself how to code. I was the model lazy CIS admin that got dangerous and having pushed a little too far. I realized that working in production and building features is really a team sport that we had the opportunity, all of us to be customer obsessed today. As developers, we can go beyond the traditional dev ops mindset. We can really focus on adding value to the customer experience by ensuring that we have work that contributes to increasing uptime via and SLS all while being agile and productive. We get there. When we move from a pass the Baton system to now having an interconnected developer workflow that increases velocity in every part of the cycle, we get to work better and smarter. >>And honestly, in a way that is so much more enjoyable because we automate away all the mundane and manual and boring tasks. So we get to focus on what really matters shipping, the things that humans get to use and love. Docker has been a big part of enabling this transformation. 10, 20 years ago, we had Tomcat containers, which are not Docker containers. And for y'all hearing this the first time go Google it. But that was the way we built our applications. We had to segment them on the server and give them resources. Today. We have Docker containers, these little mini Oasys and Docker images. You can do it multiple times in an orchestrated manner with the power of actions enabled and Docker. It's just so incredible what you can do. And by the way, I'm showing you actions in Docker, which I hope you use because both are great and free for open source. >>But the key takeaway is really the workflow and the automation, which you certainly can do with other tools. Okay, I'm going to show you just how easy this is, because believe me, if this is something I can learn and do anybody out there can, and in this demo, I'll show you about the basic components needed to create and use a package, Docker container actions. And like I said, you won't believe how awesome the combination of Docker and actions is because you can enable your workflow to do no matter what you're trying to do in this super baby example. We're so small. You could take like 10 seconds. Like I am here creating an action due to a simple task, like pushing a message to your logs. And the cool thing is you can use it on any the bit on this one. Like I said, we're going to use push. >>You can do, uh, even to order a pizza every time you roll into production, if you wanted, but at get hub, that'd be a lot of pizzas. And the funny thing is somebody out there is actually tried this and written that action. If you haven't used Docker and actions together, check out the docs on either get hub or Docker to get you started. And a huge shout out to all those doc writers out there. I built this demo today using those instructions. And if I can do it, I know you can too, but enough yapping let's get started to save some time. And since a lot of us are Docker and get hub nerds, I've already created a repo with a Docker file. So we're going to skip that step. Next. I'm going to create an action's Yammel file. And if you don't Yammer, you know, actions, the metadata defines my important log stuff to capture and the input and my time out per parameter to pass and puts to the Docker container, get up a build image from your Docker file and run the commands in a new container. >>Using the Sigma image. The cool thing is, is you can use any Docker image in any language for your actions. It doesn't matter if it's go or whatever in today's I'm going to use a shell script and an input variable to print my important log stuff to file. And like I said, you know me, I love me some. So let's see this action in a workflow. When an action is in a private repo, like the one I demonstrating today, the action can only be used in workflows in the same repository, but public actions can be used by workflows in any repository. So unfortunately you won't get access to the super awesome action, but don't worry in the Guild marketplace, there are over 8,000 actions available, especially the most important one, that pizza action. So go try it out. Now you can do this in a couple of ways, whether you're doing it in your preferred ID or for today's demo, I'm just going to use the gooey. I'm going to navigate to my actions tab as I've done here. And I'm going to in my workflow, select new work, hello, probably load some workflows to Claire to get you started, but I'm using the one I've copied. Like I said, the lazy developer I am in. I'm going to replace it with my action. >>That's it. So now we're going to go and we're going to start our commitment new file. Now, if we go over to our actions tab, we can see the workflow in progress in my repository. I just click the actions tab. And because they wrote the actions on push, we can watch the visualization under jobs and click the job to see the important stuff we're logging in the input stamp in the printed log. And we'll just wait for this to run. Hello, Mona and boom. Just like that. It runs automatically within our action. We told it to go run as soon as the files updated because we're doing it on push merge. That's right. Folks in just a few minutes, I built an action that writes an entry to a log file every time I push. So I don't have to do it manually. In essence, with automation, you can be kind to your future self and save time and effort to focus on what really matters. >>Imagine what I could do with even a little more time, probably order all y'all pieces. That is the power of the interconnected workflow. And it's amazing. And I hope you all go try it out, but why do we care about all of that? Just like in the demo, I took a manual task with both tape, which both takes time and it's easy to forget and automated it. So I don't have to think about it. And it's executed every time consistently. That means less time for me to worry about my human errors and mistakes, and more time to focus on actually building the cool stuff that people want. Obviously, automation, developer productivity, but what is even more important to me is the developer happiness tools like BS, code actions, Docker, Heroku, and many others reduce manual work, which allows us to focus on building things that are awesome. >>And to get into that wonderful state that we call flow. According to research by UC Irvine in Humboldt university in Germany, it takes an average of 23 minutes to enter optimal creative state. What we call the flow or to reenter it after distraction like your dog on your office store. So staying in flow is so critical to developer productivity and as a developer, it just feels good to be cranking away at something with deep focus. I certainly know that I love that feeling intuitive collaboration and automation features we built in to get hub help developer, Sam flow, allowing you and your team to do so much more, to bring the benefits of automation into perspective in our annual October's report by Dr. Nicole, Forsgren. One of my buddies here at get hub, took a look at the developer productivity in the stork year. You know what we found? >>We found that public GitHub repositories that use the Automational pull requests, merge those pull requests. 1.2 times faster. And the number of pooled merged pull requests increased by 1.3 times, that is 34% more poor requests merged. And other words, automation can con can dramatically increase, but the speed and quantity of work completed in any role, just like an open source development, you'll work more efficiently with greater impact when you invest the bulk of your time in the work that adds the most value and eliminate or outsource the rest because you don't need to do it, make the machines by elaborate by leveraging automation in their workflows teams, minimize manual work and reclaim that time for innovation and maintain that state of flow with development and collaboration. More importantly, their work is more enjoyable because they're not wasting the time doing the things that the machines or robots can do for them. >>And I remember what I said at the beginning. Many of us want to be efficient, heck even lazy. So why would I spend my time doing something I can automate? Now you can read more about this research behind the art behind this at October set, get hub.com, which also includes a lot of other cool info about the open source ecosystem and how it's evolving. Speaking of the open source ecosystem we at get hub are so honored to be the home of more than 65 million developers who build software together for everywhere across the globe. Today, we're seeing software development taking shape as the world's largest team sport, where development teams collaborate, build and ship products. It's no longer a solo effort like it was for me. You don't have to take my word for it. Check out this globe. This globe shows real data. Every speck of light you see here represents a contribution to an open source project, somewhere on earth. >>These arts reach across continents, cultures, and other divides. It's distributed collaboration at its finest. 20 years ago, we had no concept of dev ops, SecOps and lots, or the new ops that are going to be happening. But today's development and ops teams are connected like ever before. This is only going to continue to evolve at a rapid pace, especially as we continue to empower the next hundred million developers, automation helps us focus on what's important and to greatly accelerate innovation. Just this past year, we saw some of the most groundbreaking technological advancements and achievements I'll say ever, including critical COVID-19 vaccine trials, as well as the first power flight on Mars. This past month, these breakthroughs were only possible because of the interconnected collaborative open source communities on get hub and the amazing tools and workflows that empower us all to create and innovate. Let's continue building, integrating, and automating. So we collectively can give developers the experience. They deserve all of the automation and beautiful eye UIs that we can muster so they can continue to build the things that truly do change the world. Thank you again for having me today, Dr. Khan, it has been a pleasure to be here with all you nerds. >>Hello. I'm Justin. Komack lovely to see you here. Talking to developers, their world is getting much more complex. Developers are being asked to do everything security ops on goal data analysis, all being put on the rockers. Software's eating the world. Of course, and this all make sense in that view, but they need help. One team. I told you it's shifted all our.net apps to run on Linux from windows, but their developers found the complexity of Docker files based on the Linux shell scripts really difficult has helped make these things easier for your teams. Your ones collaborate more in a virtual world, but you've asked us to make this simpler and more lightweight. You, the developers have asked for a paved road experience. You want things to just work with a simple options to be there, but it's not just the paved road. You also want to be able to go off-road and do interesting and different things. >>Use different components, experiments, innovate as well. We'll always offer you both those choices at different times. Different developers want different things. It may shift for ones the other paved road or off road. Sometimes you want reliability, dependability in the zone for day to day work, but sometimes you have to do something new, incorporate new things in your pipeline, build applications for new places. Then you knew those off-road abilities too. So you can really get under the hood and go and build something weird and wonderful and amazing. That gives you new options. Talk as an independent choice. We don't own the roads. We're not pushing you into any technology choices because we own them. We're really supporting and driving open standards, such as ISEI working opensource with the CNCF. We want to help you get your applications from your laptops, the clouds, and beyond, even into space. >>Let's talk about the key focus areas, that frame, what DACA is doing going forward. These are simplicity, sharing, flexibility, trusted content and care supply chain compared to building where the underlying kernel primitives like namespaces and Seagraves the original Docker CLI was just amazing Docker engine. It's a magical experience for everyone. It really brought those innovations and put them in a world where anyone would use that, but that's not enough. We need to continue to innovate. And it was trying to get more done faster all the time. And there's a lot more we can do. We're here to take complexity away from deeply complicated underlying things and give developers tools that are just amazing and magical. One of the area we haven't done enough and make things magical enough that we're really planning around now is that, you know, Docker images, uh, they're the key parts of your application, but you know, how do I do something with an image? How do I, where do I attach volumes with this image? What's the API. Whereas the SDK for this image, how do I find an example or docs in an API driven world? Every bit of software should have an API and an API description. And our vision is that every container should have this API description and the ability for you to understand how to use it. And it's all a seamless thing from, you know, from your code to the cloud local and remote, you can, you can use containers in this amazing and exciting way. >>One thing I really noticed in the last year is that companies that started off remote fast have constant collaboration. They have zoom calls, apron all day terminals, shattering that always working together. Other teams are really trying to learn how to do this style because they didn't start like that. We used to walk around to other people's desks or share services on the local office network. And it's very difficult to do that anymore. You want sharing to be really simple, lightweight, and informal. Let me try your container or just maybe let's collaborate on this together. Um, you know, fast collaboration on the analysts, fast iteration, fast working together, and he wants to share more. You want to share how to develop environments, not just an image. And we all work by seeing something someone else in our team is doing saying, how can I do that too? I can, I want to make that sharing really, really easy. Ben's going to talk about this more in the interest of one minute. >>We know how you're excited by apple. Silicon and gravis are not excited because there's a new architecture, but excited because it's faster, cooler, cheaper, better, and offers new possibilities. The M one support was the most asked for thing on our public roadmap, EFA, and we listened and share that we see really exciting possibilities, usership arm applications, all the way from desktop to production. We know that you all use different clouds and different bases have deployed to, um, you know, we work with AWS and Azure and Google and more, um, and we want to help you ship on prime as well. And we know that you use huge number of languages and the containers help build applications that use different languages for different parts of the application or for different applications, right? You can choose the best tool. You have JavaScript hat or everywhere go. And re-ask Python for data and ML, perhaps getting excited about WebAssembly after hearing about a cube con, you know, there's all sorts of things. >>So we need to make that as easier. We've been running the whole month of Python on the blog, and we're doing a month of JavaScript because we had one specific support about how do I best put this language into production of that language into production. That detail is important for you. GPS have been difficult to use. We've added GPS suppose in desktop for windows, but we know there's a lot more to do to make the, how multi architecture, multi hardware, multi accelerator world work better and also securely. Um, so there's a lot more work to do to support you in all these things you want to do. >>How do we start building a tenor has applications, but it turns out we're using existing images as components. I couldn't assist survey earlier this year, almost half of container image usage was public images rather than private images. And this is growing rapidly. Almost all software has open source components and maybe 85% of the average application is open source code. And what you're doing is taking whole container images as modules in your application. And this was always the model with Docker compose. And it's a model that you're already et cetera, writing you trust Docker, official images. We know that they might go to 25% of poles on Docker hub and Docker hub provides you the widest choice and the best support that trusted content. We're talking to people about how to make this more helpful. We know, for example, that winter 69 four is just showing us as support, but the image doesn't yet tell you that we're working with canonical to improve messaging from specific images about left lifecycle and support. >>We know that you need more images, regularly updated free of vulnerabilities, easy to use and discover, and Donnie and Marie neuro, going to talk about that more this last year, the solar winds attack has been in the, in the news. A lot, the software you're using and trusting could be compromised and might be all over your organization. We need to reduce the risk of using vital open-source components. We're seeing more software supply chain attacks being targeted as the supply chain, because it's often an easier place to attack and production software. We need to be able to use this external code safely. We need to, everyone needs to start from trusted sources like photography images. They need to scan for known vulnerabilities using Docker scan that we built in partnership with sneak and lost DockerCon last year, we need just keep updating base images and dependencies, and we'll, we're going to help you have the control and understanding about your images that you need to do this. >>And there's more, we're also working on the nursery V2 project in the CNCF to revamp container signings, or you can tell way or software comes from we're working on tooling to make updates easier, and to help you understand and manage all the principals carrier you're using security is a growing concern for all of us. It's really important. And we're going to help you work with security. We can't achieve all our dreams, whether that's space travel or amazing developer products ever see without deep partnerships with our community to cloud is RA and the cloud providers aware most of you ship your occasion production and simple routes that take your work and deploy it easily. Reliably and securely are really important. Just get into production simply and easily and securely. And we've done a bunch of work on that. And, um, but we know there's more to do. >>The CNCF on the open source cloud native community are an amazing ecosystem of creators and lovely people creating an amazing strong community and supporting a huge amount of innovation has its roots in the container ecosystem and his dreams beyond that much of the innovation is focused around operate experience so far, but developer experience is really a growing concern in that community as well. And we're really excited to work on that. We also uses appraiser tool. Then we know you do, and we know that you want it to be easier to use in your environment. We just shifted Docker hub to work on, um, Kubernetes fully. And, um, we're also using many of the other projects are Argo from atheists. We're spending a lot of time working with Microsoft, Amazon right now on getting natural UV to ready to ship in the next few. That's a really detailed piece of collaboration we've been working on for a long term. Long time is really important for our community as the scarcity of the container containers and, um, getting content for you, working together makes us stronger. Our community is made up of all of you have. Um, it's always amazing to be reminded of that as a huge open source community that we already proud to work with. It's an amazing amount of innovation that you're all creating and where perhaps it, what with you and share with you as well. Thank you very much. And thank you for being here. >>Really excited to talk to you today and share more about what Docker is doing to help make you faster, make your team faster and turn your application delivery into something that makes you a 10 X team. What we're hearing from you, the developers using Docker everyday fits across three common themes that we hear consistently over and over. We hear that your time is super important. It's critical, and you want to move faster. You want your tools to get out of your way, and instead to enable you to accelerate and focus on the things you want to be doing. And part of that is that finding great content, great application components that you can incorporate into your apps to move faster is really hard. It's hard to discover. It's hard to find high quality content that you can trust that, you know, passes your test and your configuration needs. >>And it's hard to create good content as well. And you're looking for more safety, more guardrails to help guide you along that way so that you can focus on creating value for your company. Secondly, you're telling us that it's a really far to collaborate effectively with your team and you want to do more, to work more effectively together to help your tools become more and more seamless to help you stay in sync, both with yourself across all of your development environments, as well as with your teammates so that you can more effectively collaborate together. Review each other's work, maintain things and keep them in sync. And finally, you want your applications to run consistently in every single environment, whether that's your local development environment, a cloud-based development environment, your CGI pipeline, or the cloud for production, and you want that micro service to provide that consistent experience everywhere you go so that you have similar tools, similar environments, and you don't need to worry about things getting in your way, but instead things make it easy for you to focus on what you wanna do and what Docker is doing to help solve all of these problems for you and your colleagues is creating a collaborative app dev platform. >>And this collaborative application development platform consists of multiple different pieces. I'm not going to walk through all of them today, but the overall view is that we're providing all the tooling you need from the development environment, to the container images, to the collaboration services, to the pipelines and integrations that enable you to focus on making your applications amazing and changing the world. If we start zooming on a one of those aspects, collaboration we hear from developers regularly is that they're challenged in synchronizing their own setups across environments. They want to be able to duplicate the setup of their teammates. Look, then they can easily get up and running with the same applications, the same tooling, the same version of the same libraries, the same frameworks. And they want to know if their applications are good before they're ready to share them in an official space. >>They want to collaborate on things before they're done, rather than feeling like they have to officially published something before they can effectively share it with others to work on it, to solve this. We're thrilled today to announce Docker, dev environments, Docker, dev environments, transform how your team collaborates. They make creating, sharing standardized development environments. As simple as a Docker poll, they make it easy to review your colleagues work without affecting your own work. And they increase the reproducibility of your own work and decreased production issues in doing so because you've got consistent environments all the way through. Now, I'm going to pass it off to our principal product manager, Ben Gotch to walk you through more detail on Docker dev environments. >>Hi, I'm Ben. I work as a principal program manager at DACA. One of the areas that doc has been looking at to see what's hard today for developers is sharing changes that you make from the inner loop where the inner loop is a better development, where you write code, test it, build it, run it, and ultimately get feedback on those changes before you merge them and try and actually ship them out to production. Most amount of us build this flow and get there still leaves a lot of challenges. People need to jump between branches to look at each other's work. Independence. Dependencies can be different when you're doing that and doing this in this new hybrid wall of work. Isn't any easier either the ability to just save someone, Hey, come and check this out. It's become much harder. People can't come and sit down at your desk or take your laptop away for 10 minutes to just grab and look at what you're doing. >>A lot of the reason that development is hard when you're remote, is that looking at changes and what's going on requires more than just code requires all the dependencies and everything you've got set up and that complete context of your development environment, to understand what you're doing and solving this in a remote first world is hard. We wanted to look at how we could make this better. Let's do that in a way that let you keep working the way you do today. Didn't want you to have to use a browser. We didn't want you to have to use a new idea. And we wanted to do this in a way that was application centric. We wanted to let you work with all the rest of the application already using C for all the services and all those dependencies you need as part of that. And with that, we're excited to talk more about docket developer environments, dev environments are new part of the Docker experience that makes it easier you to get started with your whole inner leap, working inside a container, then able to share and collaborate more than just the code. >>We want it to enable you to share your whole modern development environment, your whole setup from DACA, with your team on any operating system, we'll be launching a limited beta of dev environments in the coming month. And a GA dev environments will be ID agnostic and supporting composts. This means you'll be able to use an extend your existing composed files to create your own development environment in whatever idea, working in dev environments designed to be local. First, they work with Docker desktop and say your existing ID, and let you share that whole inner loop, that whole development context, all of your teammates in just one collect. This means if you want to get feedback on the working progress change or the PR it's as simple as opening another idea instance, and looking at what your team is working on because we're using compose. You can just extend your existing oppose file when you're already working with, to actually create this whole application and have it all working in the context of the rest of the services. >>So it's actually the whole environment you're working with module one service that doesn't really understand what it's doing alone. And with that, let's jump into a quick demo. So you can see here, two dev environments up and running. First one here is the same container dev environment. So if I want to go into that, let's see what's going on in the various code button here. If that one open, I can get straight into my application to start making changes inside that dev container. And I've got all my dependencies in here, so I can just run that straight in that second application I have here is one that's opened up in compose, and I can see that I've also got my backend, my front end and my database. So I've got all my services running here. So if I want, I can open one or more of these in a dev environment, meaning that that container has the context that dev environment has the context of the whole application. >>So I can get back into and connect to all the other services that I need to test this application properly, all of them, one unit. And then when I've made my changes and I'm ready to share, I can hit my share button type in the refund them on to share that too. And then give that image to someone to get going, pick that up and just start working with that code and all my dependencies, simple as putting an image, looking ahead, we're going to be expanding development environments, more of your dependencies for the whole developer worst space. We want to look at backing up and letting you share your volumes to make data science and database setups more repeatable and going. I'm still all of this under a single workspace for your team containing images, your dev environments, your volumes, and more we've really want to allow you to create a fully portable Linux development environment. >>So everyone you're working with on any operating system, as I said, our MVP we're coming next month. And that was for vs code using their dev container primitive and more support for other ideas. We'll follow to find out more about what's happening and what's coming up next in the future of this. And to actually get a bit of a deeper dive in the experience. Can we check out the talk I'm doing with Georgie and girl later on today? Thank you, Ben, amazing story about how Docker is helping to make developer teams more collaborative. Now I'd like to talk more about applications while the dev environment is like the workbench around what you're building. The application itself has all the different components, libraries, and frameworks, and other code that make up the application itself. And we hear developers saying all the time things like, how do they know if their images are good? >>How do they know if they're secure? How do they know if they're minimal? How do they make great images and great Docker files and how do they keep their images secure? And up-to-date on every one of those ties into how do I create more trust? How do I know that I'm building high quality applications to enable you to do this even more effectively than today? We are pleased to announce the DACA verified polisher program. This broadens trusted content by extending beyond Docker official images, to give you more and more trusted building blocks that you can incorporate into your applications. It gives you confidence that you're getting what you expect because Docker verifies every single one of these publishers to make sure they are who they say they are. This improves our secure supply chain story. And finally it simplifies your discovery of the best building blocks by making it easy for you to find things that you know, you can trust so that you can incorporate them into your applications and move on and on the right. You can see some examples of the publishers that are involved in Docker, official images and our Docker verified publisher program. Now I'm pleased to introduce you to marina. Kubicki our senior product manager who will walk you through more about what we're doing to create a better experience for you around trust. >>Thank you, Dani, >>Mario Andretti, who is a famous Italian sports car driver. One said that if everything feels under control, you're just not driving. You're not driving fast enough. Maya Andretti is not a software developer and a software developers. We know that no matter how fast we need to go in order to drive the innovation that we're working on, we can never allow our applications to spin out of control and a Docker. As we continue talking to our, to the developers, what we're realizing is that in order to reach that speed, the developers are the, the, the development community is looking for the building blocks and the tools that will, they will enable them to drive at the speed that they need to go and have the trust in those building blocks. And in those tools that they will be able to maintain control over their applications. So as we think about some of the things that we can do to, to address those concerns, uh, we're realizing that we can pursue them in a number of different venues, including creating reliable content, including creating partnerships that expands the options for the reliable content. >>Um, in order to, in a we're looking at creating integrations, no link security tools, talk about the reliable content. The first thing that comes to mind are the Docker official images, which is a program that we launched several years ago. And this is a set of curated, actively maintained, open source images that, uh, include, uh, operating systems and databases and programming languages. And it would become immensely popular for, for, for creating the base layers of, of the images of, of the different images, images, and applications. And would we realizing that, uh, many developers are, instead of creating something from scratch, basically start with one of the official images for their basis, and then build on top of that. And this program has become so popular that it now makes up a quarter of all of the, uh, Docker poles, which essentially ends up being several billion pulse every single month. >>As we look beyond what we can do for the open source. Uh, we're very ability on the open source, uh, spectrum. We are very excited to announce that we're launching the Docker verified publishers program, which is continuing providing the trust around the content, but now working with, uh, some of the industry leaders, uh, in multiple, in multiple verticals across the entire technology technical spec, it costs entire, uh, high tech in order to provide you with more options of the images that you can use for building your applications. And it still comes back to trust that when you are searching for content in Docker hub, and you see the verified publisher badge, you know, that this is, this is the content that, that is part of the, that comes from one of our partners. And you're not running the risk of pulling the malicious image from an employee master source. >>As we look beyond what we can do for, for providing the reliable content, we're also looking at some of the tools and the infrastructure that we can do, uh, to create a security around the content that you're creating. So last year at the last ad, the last year's DockerCon, we announced partnership with sneak. And later on last year, we launched our DACA, desktop and Docker hub vulnerability scans that allow you the options of writing scans in them along multiple points in your dev cycle. And in addition to providing you with information on the vulnerability on, on the vulnerabilities, in, in your code, uh, it also provides you with a guidance on how to re remediate those vulnerabilities. But as we look beyond the vulnerability scans, we're also looking at some of the other things that we can do, you know, to, to, to, uh, further ensure that the integrity and the security around your images, your images, and with that, uh, later on this year, we're looking to, uh, launch the scope, personal access tokens, and instead of talking about them, I will simply show you what they look like. >>So if you can see here, this is my page in Docker hub, where I've created a four, uh, tokens, uh, read-write delete, read, write, read only in public read in public creeper read only. So, uh, earlier today I went in and I, I logged in, uh, with my read only token. And when you see, when I'm going to pull an image, it's going to allow me to pull an image, not a problem success. And then when I do the next step, I'm going to ask to push an image into the same repo. Uh, would you see is that it's going to give me an error message saying that they access is denied, uh, because there is an additional authentication required. So these are the things that we're looking to add to our roadmap. As we continue thinking about the things that we can do to provide, um, to provide additional building blocks, content, building blocks, uh, and, and, and tools to build the trust so that our DACA developer and skinned code faster than Mario Andretti could ever imagine. Uh, thank you to >>Thank you, marina. It's amazing what you can do to improve the trusted content so that you can accelerate your development more and move more quickly, move more collaboratively and build upon the great work of others. Finally, we hear over and over as that developers are working on their applications that they're looking for, environments that are consistent, that are the same as production, and that they want their applications to really run anywhere, any environment, any architecture, any cloud one great example is the recent announcement of apple Silicon. We heard from developers on uproar that they needed Docker to be available for that architecture before they could add those to it and be successful. And we listened. And based on that, we are pleased to share with you Docker, desktop on apple Silicon. This enables you to run your apps consistently anywhere, whether that's developing on your team's latest dev hardware, deploying an ARM-based cloud environments and having a consistent architecture across your development and production or using multi-year architecture support, which enables your whole team to collaborate on its application, using private repositories on Docker hub, and thrilled to introduce you to Hughie cower, senior director for product management, who will walk you through more of what we're doing to create a great developer experience. >>Senior director of product management at Docker. And I'd like to jump straight into a demo. This is the Mac mini with the apple Silicon processor. And I want to show you how you can now do an end-to-end arm workflow from my M one Mac mini to raspberry PI. As you can see, we have vs code and Docker desktop installed on a, my, the Mac mini. I have a small example here, and I have a raspberry PI three with an led strip, and I want to turn those LEDs into a moving rainbow. This Dockerfile here, builds the application. We build the image with the Docker, build X command to make the image compatible for all raspberry pies with the arm. 64. Part of this build is built with the native power of the M one chip. I also add the push option to easily share the image with my team so they can give it a try to now Dr. >>Creates the local image with the application and uploads it to Docker hub after we've built and pushed the image. We can go to Docker hub and see the new image on Docker hub. You can also explore a variety of images that are compatible with arm processors. Now let's go to the raspberry PI. I have Docker already installed and it's running Ubuntu 64 bit with the Docker run command. I can run the application and let's see what will happen from there. You can see Docker is downloading the image automatically from Docker hub and when it's running, if it's works right, there are some nice colors. And with that, if we have an end-to-end workflow for arm, where continuing to invest into providing you a great developer experience, that's easy to install. Easy to get started with. As you saw in the demo, if you're interested in the new Mac, mini are interested in developing for our platforms in general, we've got you covered with the same experience you've come to expect from Docker with over 95,000 arm images on hub, including many Docker official images. >>We think you'll find what you're looking for. Thank you again to the community that helped us to test the tech previews. We're so delighted to hear when folks say that the new Docker desktop for apple Silicon, it just works for them, but that's not all we've been working on. As Dani mentioned, consistency of developer experience across environments is so important. We're introducing composed V2 that makes compose a first-class citizen in the Docker CLI you no longer need to install a separate composed biter in order to use composed, deploying to production is simpler than ever with the new compose integration that enables you to deploy directly to Amazon ECS or Azure ACI with the same methods you use to run your application locally. If you're interested in running slightly different services, when you're debugging versus testing or, um, just general development, you can manage that all in one place with the new composed service to hear more about what's new and Docker desktop, please join me in the three 15 breakout session this afternoon. >>And now I'd love to tell you a bit more about bill decks and convince you to try it. If you haven't already it's our next gen build command, and it's no longer experimental as shown in the demo with built X, you'll be able to do multi architecture builds, share those builds with your team and the community on Docker hub. With build X, you can speed up your build processes with remote caches or build all the targets in your composed file in parallel with build X bake. And there's so much more if you're using Docker, desktop or Docker, CE you can use build X checkout tonus is talk this afternoon at three 45 to learn more about build X. And with that, I hope everyone has a great Dr. Khan and back over to you, Donnie. >>Thank you UA. It's amazing to hear about what we're doing to create a better developer experience and make sure that Docker works everywhere you need to work. Finally, I'd like to wrap up by showing you everything that we've announced today and everything that we've done recently to make your lives better and give you more and more for the single price of your Docker subscription. We've announced the Docker verified publisher program we've announced scoped personal access tokens to make it easier for you to have a secure CCI pipeline. We've announced Docker dev environments to improve your collaboration with your team. Uh, we shared with you Docker, desktop and apple Silicon, to make sure that, you know, Docker runs everywhere. You need it to run. And we've announced Docker compose version two, finally making it a first-class citizen amongst all the other great Docker tools. And we've done so much more recently as well from audit logs to advanced image management, to compose service profiles, to improve where you can run Docker more easily. >>Finally, as we look forward, where we're headed in the upcoming year is continuing to invest in these themes of helping you build, share, and run modern apps more effectively. We're going to be doing more to help you create a secure supply chain with which only grows more and more important as time goes on. We're going to be optimizing your update experience to make sure that you can easily understand the current state of your application, all its components and keep them all current without worrying about breaking everything as you're doing. So we're going to make it easier for you to synchronize your work. Using cloud sync features. We're going to improve collaboration through dev environments and beyond, and we're going to do make it easy for you to run your microservice in your environments without worrying about things like architecture or differences between those environments. Thank you so much. I'm thrilled about what we're able to do to help make your lives better. And now you're going to be hearing from one of our customers about what they're doing to launch their business with Docker >>I'm Matt Falk, I'm the head of engineering and orbital insight. And today I want to talk to you a little bit about data from space. So who am I like many of you, I'm a software developer and a software developer about seven companies so far, and now I'm a head of engineering. So I spend most of my time doing meetings, but occasionally I'll still spend time doing design discussions, doing code reviews. And in my free time, I still like to dabble on things like project oiler. So who's Oberlin site. What do we do? Portal insight is a large data supplier and analytics provider where we take data geospatial data anywhere on the planet, any overhead sensor, and translate that into insights for the end customer. So specifically we have a suite of high performance, artificial intelligence and machine learning analytics that run on this geospatial data. >>And we build them to specifically determine natural and human service level activity anywhere on the planet. What that really means is we take any type of data associated with a latitude and longitude and we identify patterns so that we can, so we can detect anomalies. And that's everything that we do is all about identifying those patterns to detect anomalies. So more specifically, what type of problems do we solve? So supply chain intelligence, this is one of the use cases that we we'd like to talk about a lot. It's one of our main primary verticals that we go after right now. And as Scott mentioned earlier, this had a huge impact last year when COVID hit. So specifically supply chain intelligence is all about identifying movement patterns to and from operating facilities to identify changes in those supply chains. How do we do this? So for us, we can do things where we track the movement of trucks. >>So identifying trucks, moving from one location to another in aggregate, same thing we can do with foot traffic. We can do the same thing for looking at aggregate groups of people moving from one location to another and analyzing their patterns of life. We can look at two different locations to determine how people are moving from one location to another, or going back and forth. All of this is extremely valuable for detecting how a supply chain operates and then identifying the changes to that supply chain. As I said last year with COVID, everything changed in particular supply chains changed incredibly, and it was hugely important for customers to know where their goods or their products are coming from and where they were going, where there were disruptions in their supply chain and how that's affecting their overall supply and demand. So to use our platform, our suite of tools, you can start to gain a much better picture of where your suppliers or your distributors are going from coming from or going to. >>So what's our team look like? So my team is currently about 50 engineers. Um, we're spread into four different teams and the teams are structured like this. So the first team that we have is infrastructure engineering and this team largely deals with deploying our Dockers using Kubernetes. So this team is all about taking Dockers, built by other teams, sometimes building the Dockers themselves and putting them into our production system, our platform engineering team, they produce these microservices. So they produce microservice, Docker images. They develop and test with them locally. Their entire environments are dockerized. They produce these doctors, hand them over to him for infrastructure engineering to be deployed. Similarly, our product engineering team does the same thing. They develop and test with Dr. Locally. They also produce a suite of Docker images that the infrastructure team can then deploy. And lastly, we have our R and D team, and this team specifically produces machine learning algorithms using Nvidia Docker collectively, we've actually built 381 Docker repositories and 14 million. >>We've had 14 million Docker pools over the lifetime of the company, just a few stats about us. Um, but what I'm really getting to here is you can see actually doctors becoming almost a form of communication between these teams. So one of the paradigms in software engineering that you're probably familiar with encapsulation, it's really helpful for a lot of software engineering problems to break the problem down, isolate the different pieces of it and start building interfaces between the code. This allows you to scale different pieces of the platform or different pieces of your code in different ways that allows you to scale up certain pieces and keep others at a smaller level so that you can meet customer demands. And for us, one of the things that we can largely do now is use Dockers as that interface. So instead of having an entire platform where all teams are talking to each other, and everything's kind of, mishmashed in a monolithic application, we can now say this team is only able to talk to this team by passing over a particular Docker image that defines the interface of what needs to be built before it passes to the team and really allows us to scalp our development and be much more efficient. >>Also, I'd like to say we are hiring. Um, so we have a number of open roles. We have about 30 open roles in our engineering team that we're looking to fill by the end of this year. So if any of this sounds really interesting to you, please reach out after the presentation. >>So what does our platform do? Really? Our platform allows you to answer any geospatial question, and we do this at three different inputs. So first off, where do you want to look? So we did this as what we call an AOI or an area of interest larger. You can think of this as a polygon drawn on the map. So we have a curated data set of almost 4 million AOIs, which you can go and you can search and use for your analysis, but you're also free to build your own. Second question is what you want to look for. We do this with the more interesting part of our platform of our machine learning and AI capabilities. So we have a suite of algorithms that automatically allow you to identify trucks, buildings, hundreds of different types of aircraft, different types of land use, how many people are moving from one location to another different locations that people in a particular area are moving to or coming from all of these different analyses or all these different analytics are available at the click of a button, and then determine what you want to look for. >>Lastly, you determine when you want to find what you're looking for. So that's just, uh, you know, do you want to look for the next three hours? Do you want to look for the last week? Do you want to look every month for the past two, whatever the time cadence is, you decide that you hit go and out pops a time series, and that time series tells you specifically where you want it to look what you want it to look for and how many, or what percentage of the thing you're looking for appears in that area. Again, we do all of this to work towards patterns. So we use all this data to produce a time series from there. We can look at it, determine the patterns, and then specifically identify the anomalies. As I mentioned with supply chain, this is extremely valuable to identify where things change. So we can answer these questions, looking at a particular operating facility, looking at particular, what is happening with the level of activity is at that operating facility where people are coming from, where they're going to, after visiting that particular facility and identify when and where that changes here, you can just see it's a picture of our platform. It's actually showing all the devices in Manhattan, um, over a period of time. And it's more of a heat map view. So you can actually see the hotspots in the area. >>So really the, and this is the heart of the talk, but what happened in 2020? So for men, you know, like many of you, 2020 was a difficult year COVID hit. And that changed a lot of what we're doing, not from an engineering perspective, but also from an entire company perspective for us, the motivation really became to make sure that we were lowering our costs and increasing innovation simultaneously. Now those two things often compete with each other. A lot of times you want to increase innovation, that's going to increase your costs, but the challenge last year was how to do both simultaneously. So here's a few stats for you from our team. In Q1 of last year, we were spending almost $600,000 per month on compute costs prior to COVID happening. That wasn't hugely a concern for us. It was a lot of money, but it wasn't as critical as it was last year when we really needed to be much more efficient. >>Second one is flexibility for us. We were deployed on a single cloud environment while we were cloud thought ready, and that was great. We want it to be more flexible. We want it to be on more cloud environments so that we could reach more customers. And also eventually get onto class side networks, extending the base of our customers as well from a custom analytics perspective. This is where we get into our traction. So last year, over the entire year, we computed 54,000 custom analytics for different users. We wanted to make sure that this number was steadily increasing despite us trying to lower our costs. So we didn't want the lowering cost to come as the sacrifice of our user base. Lastly, of particular percentage here that I'll say definitely needs to be improved is 75% of our projects never fail. So this is where we start to get into a bit of stability of our platform. >>Now I'm not saying that 25% of our projects fail the way we measure this is if you have a particular project or computation that runs every day and any one of those runs sale account, that is a failure because from an end-user perspective, that's an issue. So this is something that we know we needed to improve on and we needed to grow and make our platform more stable. I'm going to something that we really focused on last year. So where are we now? So now coming out of the COVID valley, we are starting to soar again. Um, we had, uh, back in April of last year, we had the entire engineering team. We actually paused all development for about four weeks. You had everyone focused on reducing our compute costs in the cloud. We got it down to 200 K over the period of a few months. >>And for the next 12 months, we hit that number every month. This is huge for us. This is extremely important. Like I said, in the COVID time period where costs and operating efficiency was everything. So for us to do that, that was a huge accomplishment last year and something we'll keep going forward. One thing I would actually like to really highlight here, two is what allowed us to do that. So first off, being in the cloud, being able to migrate things like that, that was one thing. And we were able to use there's different cloud services in a more particular, in a more efficient way. We had a very detailed tracking of how we were spending things. We increased our data retention policies. We optimized our processing. However, one additional piece was switching to new technologies on, in particular, we migrated to get lab CICB. >>Um, and this is something that the costs we use Docker was extremely, extremely easy. We didn't have to go build new new code containers or repositories or change our code in order to do this. We were simply able to migrate the containers over and start using a new CIC so much. In fact, that we were able to do that migration with three engineers in just two weeks from a cloud environment and flexibility standpoint, we're now operating in two different clouds. We were able to last night, I've over the last nine months to operate in the second cloud environment. And again, this is something that Docker helped with incredibly. Um, we didn't have to go and build all new interfaces to all new, different services or all different tools in the next cloud provider. All we had to do was build a base cloud infrastructure that ups agnostic the way, all the different details of the cloud provider. >>And then our doctors just worked. We can move them to another environment up and running, and our platform was ready to go from a traction perspective. We're about a third of the way through the year. At this point, we've already exceeded the amount of customer analytics we produce last year. And this is thanks to a ton more albums, that whole suite of new analytics that we've been able to build over the past 12 months and we'll continue to build going forward. So this is really, really great outcome for us because we were able to show that our costs are staying down, but our analytics and our customer traction, honestly, from a stability perspective, we improved from 75% to 86%, not quite yet 99 or three nines or four nines, but we are getting there. Um, and this is actually thanks to really containerizing and modularizing different pieces of our platform so that we could scale up in different areas. This allowed us to increase that stability. This piece of the code works over here, toxin an interface to the rest of the system. We can scale this piece up separately from the rest of the system, and that allows us much more easily identify issues in the system, fix those and then correct the system overall. So basically this is a summary of where we were last year, where we are now and how much more successful we are now because of the issues that we went through last year and largely brought on by COVID. >>But that this is just a screenshot of the, our, our solution actually working on supply chain. So this is in particular, it is showing traceability of a distribution warehouse in salt lake city. It's right in the center of the screen here. You can see the nice kind of orange red center. That's a distribution warehouse and all the lines outside of that, all the dots outside of that are showing where people are, where trucks are moving from that location. So this is really helpful for supply chain companies because they can start to identify where their suppliers are, are coming from or where their distributors are going to. So with that, I want to say, thanks again for following along and enjoy the rest of DockerCon.

(upbeat music) >> Welcome to the BizOps Manifesto Power panel talking about, "Embracing Agility Across the Business." I'm Lisa Martin, there are three guests here with me today, to break down this topic. Pierre Viljoen, CTO at Global Head of Enterprise Technology and Governance at HTL Enterprise Studio. Hey Pierre, welcome. >> Thank you >> Lisa: Dave West is also here, the CEO of Scrum.org. Hey Dave, good to have you with us. >> Hi Lisa, hi everybody. >> Lisa: And Serge Lucio is here as well, the general manager of Broadcom's Enterprise Software Division. Hey Serge, good to have you on the program. >> Thank you, good to be here. >> So we're going to be talking about the people and the process and technology requirements that businesses need to adopt to be able to embrace agility across the business. We're going to also be talking a lot about this inaugural BizOps industry research survey, on the state of digital business. A lot of very interesting findings that we're going to go through in the next 20 minutes or so. So the first question guys is, the BizOps survey found that over 519 individuals over five countries business and technology executives. This survey found, most organizations still expect this year to be as challenging as last year. I want you to kind of walk us through why that is, and how is that going to impact digital transformation initiatives? Pierre, we'll start with you, then Dave, then Serge. >> Sure, thank you Lisa. So, I think these days, disruption is no longer an exception. It's kind of become the norm or the rule, in terms of how we operate. And as executives in companies have learned over the last year, with everything that's happened is that, you can only modernize to a point, and then you need to do a little bit more. And what really is needed is for us to understand, going forward, how we're actually going to remodel our business by harnessing the resources that we have in a much more agile way, in a more fluent way, from an organizational perspective. And I think our current midterm goal, probably, is that we're capable of remodeling how we can remove roadblocks. These kinds of roadblocks in the future, and get us in a better position where we are. I don't expect things to change dramatically over the next year. More in line with us making sure that we're more future proof in the way in which we're working. >> We still have remote workers, global uncertainty, the vaccine. Dave, what are your thoughts on the impact of this year on digital transformation initiatives? >> Yeah, it's funny. When I think of, sort of uncertainty and chaos, I think that COVID really started it rolling down a hill, but unfortunately it's literally like rolling down a hill, these chaos and complexity. It's getting faster and faster and harder and harder. We're talking about the new norm, right? What is the new normal? We just don't know. And I think the reality is that most organizations were surprised by the impact of COVID-19 and because of that, they responded very quickly. Many of them, people were working at home, they're looking at their supply chain, looking at localization, all sorts of really important things happen, but very quickly, not very strategically. I think the next few years we're going to see, hopefully, some of that realizing into strategy and actually starting to fundamentally change how the business is looking at the world. We've sort of entered the digital age, Lisa, this next age of innovation, we have moved out of mass production and the age of oil, into something very, very different. And I think those organizations, every organization out there is going to have to get a handle on that and COVID was the wake up, right? And I think the next five years are going to be very interesting. >> I agree with you that that accelerant was, I didn't think of it before as a big ball rolling downhill. And now I don't think I'm going to be able to get that out of my head. But Serge, talk to us about your thoughts, the impacts to digital transformation initiatives. >> Yeah, I think back to what Dave was describing. The big challenge is the uncertainty. Many organizations are faced with, currently, a lot of unknowns about, if and when things will go back to, quote unquote, some kind of normality. And with that kind of uncertainty, there's a lot of challenges to the planning from an investment point of view. So, Dave was talking about a short-term versus long-term Like, a lot of these organizations are basically focused on just getting by over the next 12 months and trying to figure out what needs to happen over the next 12 months. At the same time, there's a lot of challenges with respect to readiness and uncertainty. And so, in that context, you got kind of this tension between, "How much do I invest short term "on basically tactical initiatives, "How do I care about teams? "How do I enable these teams "to deliver in weeks as opposed to months? "And then at the same time, "how do I continue to invest "to fundamentally change my operating model?" And that tension is very real. Within many of the organizations we serve. >> One of the things that the survey found was that most of the respondents were very willing to embrace being more agile in order to be able to better respond to rapidly changing market conditions. But I want to get your opinion on what that actually really means, that willingness to embrace being agile. What does it really mean? And what do organize organizations have to do differently? Pierre, I'll start with you. >> Sure, I think we had a discussion a while back and Dave actually got into something interesting where he said, without quoting a famous sneaker brand, just go out and do it. I think that's probably the most important part of this. Most organizations are struggling to figure out, "How should we embrace Agile? "Should we jump in at full scale now? "Should we be looking Scrum? "Should we be doing Scrum-Falls? "Should we be falling over our own feet?" Nobody knows exactly, what might be the right sector? I think the most important part is to pick up a pair of solid principles that you're going to embrace, start executing on them, start learning as you go, and basically improve as you move forward. Over the last year, we've embraced digital product management quite a lot on our side. And it's had tremendous benefits without us, per se, aiming for those benefits at the end of the day. And these are things that you learn as you go. And if you're going to wait around, analysis paralysis is going to be the killer of Agile at the end of day >> "Just do it," I like that. Good advice. Dave, what are your thoughts? >> Yeah, so, I think that what's really interesting is, Agile has been around for 20 years. The manifesto was signed 20 years ago. Scrum came into the world 25 years ago. All of these sort of Agile approaches, but they were predominantly focused on technology. And I think that one thing that I've noticed, over and over again, is that the realization by C-level executives, level sevens, or whatever they're called, they've realized that it's not about technology. (chuckles) It's great, the technologies. I guess the technology's always worked in this complex world because customers never knew what they wanted. We didn't know how are we going to do it. We'd never worked together before. We didn't know how much it was going to cost. So, because of that (chuckles) we had to work in an agile way in technology. But ultimately, I think, one of the big differences going forward, is going to be that, there I say that intersection of business and technology, that BizOps kind of model that we talked about in the manifesto, and what the survey was really trying to tease out. I think that's really, really going to be interesting. And I don't know what that actually means, in terms of the execution. I hope it means that we're going to see teams better aligned to business outcomes. I hope it means that we're going to actually allow those teams that are actually know what they're approaching to make decisions. I hope it means that planning is going to be more directional rather than task level. I hope it means that we're going to start measuring the success in terms of business outcomes, not in terms of the work that we do. I hope it means all of these things. But we will wait and see, because experience would indicate that after a big disaster, lots of people tend to go back to exactly how they worked before, with that sort of emus kind of mentality or ostrich or whatever things sticks its head in the ground. I don't know. >> Sometimes we just want to go back to when things were safe and normal. But in terms of kind of following on, Dave, what you said, 94%, in this survey, 94% of respondents said we should adopt BizOps to increase competitiveness. So, that willingness is there in a vast majority of the respondents. So, I'd like to get your thoughts on what that willingness actually means and what they need to do differently. >> Yes. The problem is that, I think everybody understand that you have to be agile, right? You need to be able to respond quickly to your customer needs. You need to put the customer at the center of everything you do, right? So, conceptually, everybody understands that. The problem is really the operating model that many of these large organizations are dealing with to this day, right? So, you have these sort of Berkeley, kind of organized organization, with functional roles, specialized roles. And when you think about kind of generally, well, one of the big challenges is that you need to start to think horizontally, right? You need to start to start to think about what kind of value streams and what part of the cross functional teams that need to be organized and integrated to deliver on specific business outcomes. You need to start shifting from the traditional contract-based model that(indistinct) to a model which is much more based on trust, right? And we need to move away from vanity measurements and KPIs that many of the organizations typically lead by, to really focus on one thing and one thing only, which is that business value has been delivered. So, fundamentally, I think it requires a bit of a redesign of the operating model in these organizations. And one where, especially when you have a risk adversed kind of organizations, you need to start to be more accepting of risk, fundamentally. >> More accepting of risk. You brought something up there starts that I want to tackle in the next question with respect to culture. But one of the things that the survey uncovered was an interesting kind of seeming contradiction. The majority of respondents said, "We agreed, digital transformation "is about business outcomes "more than it is about technology." But 62% said, "We're still adopting technology for technology's sake." What does that actually mean? And what's the kind of cultural impact there for organizations to really get that more aligned on the digital transformation and the technology and the business outcomes? Pierre, we'll start with you. >> Sure. So, I think there were a number of reports this year talking about what's happened, what's not happened, and the majority of them focused on the fact that, as tech leaders, for years we've been praying to the gods to get budget approved to do all kinds of modernization activities to our infrastructure, our IT, tools, et cetera. And, lo and behold, the ball comes rolling down the hill, smashes a few things and we basically get some blank checks. So, we run around and we buy a whole bunch of stuff to modernize and to embrace this ability to do things differently. And in that whole process, what we basically did was buy more tools and buy more technology. And in that whole process, we didn't really embrace what it is that we're trying to achieve. So, basically aligning the technology to the actual business requirements getting closer to the customer, being able to understand where our market's moving, how we're capable of reducing the journey, if I can put it that way, and make sure that we're more aligned to where we need to be. So, although a lot of CIOs and CTOs got away with doing a lot of great stuff over the last year and users like me are like, "Ooh! I don't have to worry "about stupid VPNs and things anymore." That all went away. But in the same instance, I didn't really get anything that changed the organizational dynamic, which is a challenge. We still have the fundamental problems we have because the business leaders are not yet embracing the deep monitor of the processes that are supported by the technology. And then driving that in such a way that we can gain more business value which is important. To Serge's previous point, we're doing all these great things but we're not focusing on the incremental value that we're supposed to be getting. >> Dave, did it surprise you that there was this seemingly contradictory response? Yes, it's more about business outcomes and technology, but we're still adopting technology for technology's sake. What are your thoughts on that? And how can organizations actually start to move the needle on that? >> E-comm by cultural change, right? But you do know that your board and your leadership want you to do something, and the easiest thing you can do is buy something. I'm a sort of now an American, so, that's kind of my mantra in life, right? "When in doubt go shopping." Which is fantastic, just for the record. (Lisa laughs) But so, you've got to be seen to be doing something, whether it's replacing a VPN, which is always a fun thing to do, or whether it's getting on Slack. Everyone's going to be on Slack. that's going to help. But actually the core is that, exactly what Serge and Pierre have been saying all along, it's that, "Okay. So what is our business all about? "What are our customers? "what did they actually need? "What do our employees need? "How do we build a better value stream "from customer to the organization? "How do we align our teams to that? "How do we incentivize correctly "both the employees that are working "and our partners that are providing things "in this supply chain. How do we do all of those things?" Ultimately though, that means that we have to take a step back which is a very frustrating thing at the moment. And actually look at what is our business all about? What is the mission of it? Who are the customers? Take a moment to find what those are. And then, soon as we have that, and we don't have to do it. As Pierre said, we don't have to do it completely. We can do it incrementally. Organizations are very inward looking. That is the industrial mindset. That is that paradigm. It's looking, as Serge talked about, silos, "optimizing my department," "optimizing my budget, optimizing my kingdom." And what we're talking about is something that cross cuts all of that. So, the decision making is going to change around where the investments go and that's going to be really, really challenging. So, I'm not surprised, I'm not at all surprised that everybody says we should be doing this. And it's like classic. Everybody says we need to be fitter, but we're still all not fit. (Serge laughs) It's sort of, that's just the reality of the world that we live in, right? But we have to start making a stand. And the place we begin is customers. That's the place. And as soon as we start doing that, then everything else just becomes quite easy, actually. >> I like that. Focus on customers and it becomes easy. Serve, I'm kidding. What are your thoughts on this? >> Yeah, I think Dave summarized it well. It's very easy to just buy a tool or buy something, right? Fundamentally changing kind of an operating model is very difficult, but you need to fundamentally rethink for instance, all the responding initiatives. So, something as mundane as, You know, as a leader in my organization I have a budget, right? What's my incentive of collaborating with my peers in terms of delivering credible analysis form. And so, that to me kind of a fundamental shift that we need to operate, and that's probably one of the reasons why many of our largest organizations that we're serving are starting to introduce some new roles like a Chief Digital Officer, as kind of a way to kind of bring kind of a slightly different organization design. The challenge, though, is that, well, all of these teams are still kind of integrated with this fabric of these large systems which exist. So when we look at these value streams, in fact they're not independent from one another. You have a bunch of interdependencies. You are looking at kind of networks of these value streams. But the fundamental shift that we need to see is what we want these organizations to think about, ultimately with the part of the products or services that need to be focused on, all of these become kind of the primary things that we measure from point of view, and how do we align teams and projects and funding along these kinds of outcomes? >> So being customer focused, also being more broadly focused you mentioned the Chief Digital Officer role, which has an interesting role. It's supposed to look more, holistically, internally and externally. And we know that these organizations know we need to be better at this. like Dave's joke about we know we need to be more fit. But what's it going to take to actually create that collaboration, so that IT and business leaders are really working in lockstep and doing so in a timely fashion, so, that they're able to stay competitive. I do want to know from each of you, are you seeing examples of this already in progress? Pierre, let's start with you. >> I can only give you another example and say, one of the interesting things that we did was we try to embrace the delivery of services at HR in kind of a different frame this year, and kind of productize the services that we deliver. Now, if you're most people, you're trying to think about, "How do I set up things like communities "of practice and collaboration between people so "that they can work together on developing new services "new features, new products, et cetera." And we set out with creating this agile way of working. What we didn't anticipate, which was a very nice side effect, is that, because of COVID, because of the catalyst that it provided us, the remote working, people sense of ownership is inherently there. Meaning that self-organization of teams started happening. Nobody needed to crack a whip to get a bunch of guys to talk together with one another to figure out how to get stuff done. It's not like you could walk over to the water cooler and have a chat to Bob. Bob is a thousand miles away, or Bobby's sitting in another State. So, all of a sudden, all dynamic changed. And I have to say, people are a lot more resilient than what they're being given credit for. And if, as organizations, we embrace the culture in such a way and harness it in a positive way, we can actually get this movement to happen. And we actually can make the sum of the parts to be more than the whole. And this year we've seen that happen. And by no means, are we done 'cause we still have a lot of work to do, like Serge said, we have budgets, and budgets give you finite amount of movement left or right. Then you have to do what's best and possible within the frame that you're given. But I think embracing the cultural change and helping people to really excel at that and empowering them makes a huge difference in the way that you can get stuff done. >> Absolutely. Dave, what are your thoughts on this? >> I'm going to say something a little bit controversial, I think. I'm not a big fan of Chief Digital Officers. It just seems like we've got a problem. And some would argue that, "Well, if you've got a problem with somebody "you should get a coach" and all this stuff "and you get it sorted." And that's probably a good thing. But most digital officers, they're going to build a long-term career and create yet another stove pipe and that stove pipe's responsible for bringing all the other stove pipes together. It sounds a bit odd. If a digital officer is really there as a short term enabler, 'cause you asked IT and business leaders, trying to get them to work together better. The best business leaders (bell dings) know about IT, right? The best business leaders are IT sanctuary. Elon Musk or Jeff Bezos are great business leaders, but they know about technology, right? That's what brings them together. Technology is an asset and they may not be the most biggest expert in it, but they care deeply about learning about that stuff. So, I think the next few years we're going to see a lot of C-level and leaders in organizations become a lot more tech savvy, and maybe hire coaches to help them navigate. And the Chief Digital Officer will become more of a coach rather than a person that rolls out Slack or something, you know? (Pierre laughs) So, I think that is going to be the next big jump, really, when we realize that you don't get an additional thing. It's just part of what you do. >> Serge, agree, disagree? >> I agree. The reality is that it is happening, right? Don't get me wrong. We see that every day that so many States are highly integrated, organizations and teams are measuring business value, business outcomes. The problem is that it's oftentimes a very small subset of what these organizations are doing. And so, it's almost like the CEO is coming as kind of these new kind of, as Dave described. And it's got this new style organization which is really there to kind of scale what has been working with these organizations, but we're kind of creating this kind of almost shadow organization, as opposed to fundamentally rethinking and redesigning the organization and redesigning kind of the operating model. And so, we're kind of layering new stuff as opposed to fundamentally transforming. So, as long as it is just kind of just a step towards kind of a true transformation, I think that's fine. The challenge is to, again, create kind of a new set of silos, which are now called value streams, as opposed to young functional silos that we have today. >> So a lot of opportunities identified in this survey but there are still a lot of challenges there. So, I'd love to get you guys and our final question here in this panel to help us understand, from the BizOps coalition's perspective, how are you helping organizations to navigate these challenges, so, they can become successful, transform and actually become agile to respond to rapidly changing market conditions? Pierre, kick us off. >> Sure, from a coalition perspective, we're just trying to make sure that there's a set of sensible principles. That people can look at, can adopt that I think Dave mentioned it in another discussion, that give you that clarity of thought and mind in terms of what should you be thinking? How should you be thinking about it? What are the various aspects you need to consider? And then from that perspective, how do you implement these things in a sensible way for your organization? By no means is this this like, "Here are the 10 steps, you do them, and you're done." You'll be rich beyond your wildest dreams. It's not how it work. You're still going to have to work at it. You're still going to have to figure some stuff out. You're going to have to deep in yourself in your organizational policies, procedures, understand how the organization is actually working. You can't strap a V8 to Mini Cooper and expect to break the land speed record, without the wheels falling off, or something going wrong. So, you really need to harness that in a more sensible manner to move forward. And I think the coalition is on the right path to help organizations realize, "what is the sensible way to go?" "What are principles we can adopt "that we can abide by that will help us drive business "in a different way and close this chasm of disparity "between business and IT?" >> And Dave, your perspective on the BizOps coalition, helping organizations to sort through these challenges. >> Yeah. I'm going to share a little bit of a personal story. So, I must admit that I wasn't keen on the whole idea, and Serge sent me some stuff and he's like, "Could you just provide some feedback." And I did, and then there was a press release with my name on it. I saw, I was like, "Oh my God! "I better get involved because I don't want to "have my name associated "with something that doesn't make sense" But I've actually been surprisingly, I've actually found it a lot more positive than I thought because of exactly what Pierre's saying. So, basically, the coalition is a group of vendors, a bucolic of consultants, some pseudo thought leaders that think they are very thoughtful and maybe they're not, people like me. (Pierre laughs) And what we're doing though, is actually trying to get some clarity of terminology, get some clarity of, what are the principles? What are those key principles? How do they relate to each other? Get some, some synergy to allow, 'cause there's so much noise out there. And hopefully, this is going to say, "Okay, this is what BizOps is. "This is why it's important. "These are some simple things." And then hopefully, because of the breadth that Serge and others have managed to get in terms of membership, we're going to get all of those organizations to be consistently talking about these things, which will then create pressure on the market to actually start adopting these things in the way that we're proposing, or challenge those ideas and then make them better. So, I'm kind of excited about it, surprisingly, 'cause the last thing we need is yet another manifesto and group of people that spend their whole time talking about things and never getting anything done. But actually I think there might be some valuable stuff that comes out here and we're going to inspect and adapt to make sure it is valuable. And if it isn't, we will stop. (chuckles) (Lisa laughs) >> And Serge, strap us up with your thoughts and extending that value. >> Look, we started the BizOps manifesto really with kind of a very simple observation. Everybody's talking about the same stuff, right? But you have a value stream management church, the digital product management's church, the DevOps church, with Scrum church, the safe church. Right? But we're all saying the same thing. But we create so much confusion with our large enterprise customers, but it's just not a grain on a set of principles. And just saying like, look, fundamentally, we're all talking about the same thing. And there are process aspects, there are cultural aspects. There is what you measure. But fundamentally we agree on the same core set of principles. And so for me, the BizOps manifesto, first and foremost is to get the stakeholders from these different communities together, and recognize that, at the end of the day, we share the same values and create some clarity to the market as to how these pieces fit to one another. The second aspect, which is more from our point of view, as one of the vendors of tools, right? There's tons of tools out there. We talk a lot about kind of measuring business outcomes as a primary way to actually align to everybody in our organization. Well, today if you look at any of these organizations, on average, they use about 40 different tools on one of these value streams. None of that stuff integrates with one another. It's extremely difficult for an organization to be able to trace from an investment, all the way to stuff that delivers value and production to a customer. And so, one of my hopes for the coalition is that we start to actually provide some platform, data models, ontologies, to start to integrate those different tools to facilitate that kind of integration. So, those are kind of the two things which I think we can really help kind of develop and and improve on. >> Well, we know that there's a tremendous amount of folks out there that are wanting to embrace agility across the business, identifying areas where they need to do work. So, great advice from the three of you. Thank you so much for joining me on this power panel today and sharing what organizations can do to really embrace that agility across the organization. >> Thank you. >> Thank you. >> Thank you. >> Pierre Viljoen, Dave West and Serge Lucio. I'm Lisa Martin. Thanks for watching. (upbeat music)

(upbeat music) >> For years on the queue, we've talked about the benefits of the cloud going beyond IT cost savings. Sure. You can move your workloads into the cloud and minimize the so-called undifferentiated heavy lifting of IT equipment and deployment and management. And of course increased agility is often the number one benefit customers site from the cloud. But increasingly, the value of the cloud is being seen as applying that agility to change an organization's operating model. This drives business value that can be orders of magnitude greater than savings on tech labor costs. And one of the more interesting examples we found, is using the cloud, data and software technology to find, and flexibly source distributed energy resources so that clean energy, can be delivered efficiently. Hello, and welcome to the startup showcase on the cube brought to you by AWS. We're very excited to have two exacts on from AutoGrid. Wait until you hear about the innovations that they're driving and the problems that they're solving around, some of the world's most pressing problems. Amit Narayan is here. He's the CEO of auto-graded Rajeev Singh is the chief technology officer gentlemen, welcome to the program. >> Thank you. >> Thank you for having us. >> You're very welcome. >> Okay, so heard my summary Amit. Maybe you could add some color about AutoGrid. What's your story? >> Yeah, I mean, undoubtedly climate change is one of the defining challenges of our time, and we're already seeing extreme weather events whether these are wildfires in California, are extreme cold events in Texas, last two weeks. As we tackle the climate change through renewables, this whole volatility challenge that we are seeing is only going to become even more pressing. So we at AutoGrid provide software that creates, a virtualization layer, just like you doing in the cloud world, with hardware around all kinds of energy assets, whether these are your EVs in the homes, our batteries are distributed solar panels. And then we apply intelligence from software, to coordinate and orchestrate all of these assets. So you can think of us as a autopilot for the grid, and our technology is called virtual power plants. Which allows us to harness, the power from all these distributed energy resources. >> Yeah. I was going to say you're essentially creating, a virtual power plant. That's amazing of aggregating these distributed resources. I mean, it sounds very logical but it also sounds non-trivial, its a transformative idea. What exactly is a virtual power plant? I mean, how does that all work? >> Yeah. Well, I mean, if you think about how the grid was designed by Edison and Tesla, they really never envisioned a world where you will have a two way flow of power, not just from generator to the consumers, but potentially from the consumers back to the generators. And certainly they didn't really design the grid to incorporate this amount of renewables, which can be intermittent and volatile. So as we are now transitioning to this new energy world, we have to rethink the entire grid architecture, and reinvent how this control system works. But fortunately for us, unlike Edison and Tesla we have some really powerful tools at our disposal namely the internet and the cloud, and these tools do allow us to rethink how we connect all these different assets and we optimize them. And in a way, we are now rebuilding the grid outside in where if you have a battery in your home, not only can it power your own home when power is out, it can actually provide power back to the grid or to your neighbors. And so with this onslaught of DES, we think that we are living in the most exciting times, since Edison and Tesla in terms of how we are going to transition to a sustainable grid. And we think that our software, can play a foundational role in accelerating that transition. >> Lets stick here the bi-directional flow. It's so simple, but genius. Rajeev, maybe you could talk about the tech behind AutoGrid. I mean the secret sauce, lies I think in that whole flexibility management system but there's data involved, probably a fair amount of computer science. Maybe you could explain it more detail. >> Yeah. just as Amit mentioned now, when we started AutoGrid, we had the luxury of, cloud computing a massive scale, at that massive scale and AutoGrid, what we've been able to do is pull together a cloud native computing. They lost the city, the scale, with cutting edge AI and machine learning, as well as all of the dispatch, and command and control technologies, that are all in one platform. And all of them have to come together, to be able to manage and orchestrate, these a massively distributed energy resources. I mean, these could be small, you know batteries or solar panels, et cetera. So gone are the days of large generators that could be managed with smaller compute now because the sheer number of DER's, you need a new paradigm to be able to manage this. And this is really what is under the hood, that constitutes our virtual power plant. >> Rajeev Can you talk a little bit more about your scale model? I mean, how are you able to do this effectively without imploding, or hitting walls? >> Yeah, so obviously, we've been on AWS for about ten years now. And even prior to that, we had the previous company loaded with AWS. So that kind of gave us a glimpse off the sheer scale of compute, that is available to us on tap, if required. So that was quite comforting, because when we did back one of the calculations on the amount of data, that's coming in through IOT industrial IOT from all the distributed energy resources, the amount of processing required for real-time computing as well as, the sheer variety of the other, we have to tackle in in various geographies around the world. AWS made it happen just because having regions, across the globe, we done in, I believe six or seven different AWS regions. We cover a four continents, twelve plus countries. So just because cloud computing was there, we were able to ramp up the solution, very quickly. Now, one thing we are a big believers in is that you only learn by doing, and the only way to learn, is to run production systems. And when we started, of course we didn't do everything right. But we quickly learned we adapted, we scaled, and we kept on scaling. And this is where we are right now. >> Interesting. That's like Andy Jassy says there's no compression algorithm for experience. We know it well. One more for Rajeev, and I want to come back. With AutoGrid tapping, all these energy sources, you got a pretty major threat surface. How are you dealing with security? >> Yeah, we don't talk a lot about our security posture for obvious reasons. Some of the underlying principles are in reducing the blast radius. It should be quite familiar to people who work in security. The use of wide variety of best of the breed security tools, including, and or the past few years. In fact, past five, six years, AWS itself has rolled out a number of security managed services, which are included. But on top of that views, other solutions as well. And it's all designed in layers, with proper segregation, and we have variety of security certifications. One of the most, the one that we're proud of is we are one of the few if not the only NERC solution SAS solution in this domain on AWS. And it's just a culmination of using security by layers. And reducing the blast radius. >> Yeah. Makes sense. And let's turn to some customer use cases. What are some of the main problems, that your customers come to you to solve? How are you approaching them? Maybe you could address that and add some color. >> Yeah, absolutely. I mean, as Rajeev mentioned. There is a lot of deep tech in the platform, and the optimization complexity, grows exponentially with the number of assets. And as you go from a gigawatt scale power plant and you want to get the same power from Tesla power walls. let's say, for every generator you're replacing it with more than two hundred thousand mini generators. And if the complexity grows exponentially. it's far beyond what the current algorithms can handle. So a lot of customers come to us solve their technical challenges. But even beyond that, the whole complexity of transacting, with small generators is very high, and that our business model issues that we help our customers solve. So the whole energy industry, has been designed to have transactions, between very large generators and utilities, but very few of these transactions. And now when you are talking about DER's, you're having millions of transactions with very small entities and maybe even homeowners, back to the utilities. So neither the utilities, have the capability today, to have these transactions, nor the asset owners, and operators, have the capability to go back and have the transactions of the utilities. So we sort of act as an intermediary, and we provide a one-stop shop, for fleet owners and operators. And we say that if you work with us, we will help you monetize your assets, and get more value from these assets, by interfacing with utilities by interfacing with energy markets which can get very complex. >> I love this. I mean, everybody's winning here. Rajeev. I want to come back to the to the cloud a little bit. You talked about, you've been able to AWS for ten years and then even before that, you've got deep experience. I mean. I can't imagine, how you would do this without the cloud. I mean, maybe it could be a really heavy complicated list lift. I mean, you've seen the AWS cloud evolve over time. It's gone way beyond, of course, compute and storage brought in a lot of machine learning capabilities on and on. And I mean, how are you leveraging that evolution? Those zillion features that AWS puts out every year at reinvent. I mean, maybe you could talk about that a little bit. >> Yeah. So of course, when we started, we used it as an infrastructure provider, you know provided us compute networking, security firewalls, et cetera, just on tap. There's very good. Got us started. Then we started leveraging a lot more managed services, that AWS offered, that allowed us to run. For example, variety of databases right to data stores, in a managed fashion, with a very small startup. You're always, running lean. So that helped us with a small team, of system engineers and engineers, back from engineers to be able to put together and run these systems around the globe, just because enablers was responsible, for managing the services. We always keep an eye on. And one thing we love about AWS is the amount of innovation, that they quickly put into production. So, we're always keeping an eye on, what's coming out. And over the years, it has been quite nice to us in some ways, we directly talk to the solution architects, they tell us what's coming, what should be used, what we should not use in what's in production ready What's not. So that level of kind of deep engagement, really helps us. Kind of keep abreast of the innovations that are constantly being rolled out on AWS. And we keep kind of incorporating those into our platform and making it more and more capable. The one thing I also would like to say, is that to be able to aggregate capacity, from all these DER's, it has to be done in a cost effective fashion. So, this is where AWS helps us with running, last a city at the service level. All the microservices can scale independently. So we don't have to have this massive monolith, and across the globe, we don't need to have, fifty of those to be running. And that's going to add up to a massive cost. So we are able to scale, just the portions of the infrastructure just in time when we need it. And that also helps us greatly, in having a cost effective solution, for our customers. >> That's actually great. That's great. So that granularity is important, for you to have fine grain control of your costs. A lot of people sometimes question that granularity that AWS provides, because it does add a level of complexity, but you guys can deal with complexity. You know, one of the things that we haven't talked about I wonder if we could touch, on it is data. I mean, this is the data flow. I'm imagining the data flow, and the metadata and the decisions that you have to make are are quite complex. Can you address that a little bit? I mean, you guys got to be pretty, sharp data walks. >> Yeah. So the people that we have at the company, including myself have come from a billing lodge, high performance and high large enterprise systems, previously from airlines, Ford motor company or pharmaceuticals. In any system, where we are making a lot of decisions. The first thing you have to do is data integration. And again this is something that you just learn by doing and having done it across the globe with a variety of the DER, systems UVS, you name it. We have to pretty much done one of everything, and of course, and be very quickly abstract and learn, if you do something twice, we abstract it and make it into a library. So that the next time around it's just a simple turn-on switch. So it's no secret sauce there you just learn by doing and you kind of constantly abstract and you expand the solution. >> That's great. let's close. The other thing. We really haven't talked much about your company. Maybe you could, add some. whatever you want to to share, metrics. I mean you must be growing, head count, or whatever you're comfortable sharing. If you could just give us, a little glimpse of of the company. >> Yeah, absolutely. We have been around for close to ten years now. We are based in Silicon Valley. We have multiple locations. Our second primary location is in India. Today We are operating in over twelve countries. We have close to five thousand megawatts of distributed energy acids, that we actively control manage. This includes, everything from a thermostat in the home, to very large scale, wind and solar farms, as well as large scale batteries. EVs as a new emerging category. And, we work with a variety of partners. AWS has been one of our founding partners, on day one, you talked about data. We were the first ones to realize how much data we were going to get from all of these assets. And the current systems will not scale. So we made the decision on day one to be on cloud. And that was foundational year. I just want to say that over the last year or so, we have I think collectively as a society realized how individual actions, impact the overall society. And I think we are really at a great inflection point right now, where if we can harness this newly developed consciousness and awareness to accelerate, our transition to new energy, away from fossil fuel, we can really solve what I think is the biggest challenge that we face as a society going forward. >> Yeah. Micro actions that actually have a huge impact. And so I guess, that's kind of of where you see this heading in the future both the general market, your business. I mean presumably, you've been around a while, maybe you'd welcome competition to really solve this problem. Right? >> I think we are in the same fight. We are all working towards the same goal, of having a clean cheap reliable energy. And we would welcome as much support as we get to build momentum for this absolutely >> Its like the Pharma companies cheering each other on for the, for the vaccine. Again, guys super interesting business solving real problems really thanks gentlemen for coming on the program and we wish you well in the years ahead. >> Thank you for having us. >> It's really been our pleasure. Thank you for watching the AWS startup showcase on the cube. I'm Dave Volante.

from the cube studios in palo alto in boston connecting with thought leaders all around the world this is a cube conversation [Music] hello everyone this is dave vellante with the cube and welcome to this conversation with me is anil singal who is the ceo of netscout anil it's a pleasure to speak with you today thanks so much for coming on the program thank you so i want to talk a little bit about uh netscout we're kind of at the cube we're sort of enamored by founder-led companies i mean you started net scout right around the same time that i entered the tech business and you remember back then it was an industry dominated by ibm monolithic systems were then with a norm in the form of mainframes you had mini computers pcs and things like pc local area networks they were in their infancy in fact most of the pcs as you remember they didn't have hard disks in them so i want to start with what was it that you saw 35 years ago to let you let that led you to start net scout and at the time did you even imagine that you'd be creating a company with a billion dollars worth of revenue and a much larger market cap well certainly i'd not imagine where we'll be right now and uh we didn't need we didn't know that this will be the outcome where i mean we just happened to be at the right place at the right time but we did have a vision some of you had the feeling we are enamored by networking and we thought that network will be the business in fact our business card in 91 said network is the business and so somehow we got that right and and we said these things will be connected and overall we found then that with the ip convergence first in the enterprise in 90s and then internet and then carriers moving from analog to digital we call talk about digital transformation in last few years but this has been going on for the last 30 years and as we add what we were doing become relevant to more and more people over time for example right now even power companies use our product okay and we have iot devices coming in so so basically what we do is we we said we're going to provide visibility through looking at the traffic through the lens and the vantage point of the network a lot of people think we're just doing network monitoring or have been doing that but actually we use the network as the vantage point which is other people are not doing most of the people have accidental data from devices at the basis of visibility and that turned out to be a very successful and but at some point different points in our life we became responsible for the market not just for netscope and that changed the shape of the company and what we did and how we drove the innovation yeah now i want to get into some of that but i i i'm still really enamored of and and fascinated by by the beginnings i worked for a founder led a chairman a guy named pat mcgovern who built the media empire he had these 10 sort of core principles we he used to test us on him we'd carry him around a little little note card things that today still serve us you know stay close to the customer uh you know keep the corporate staff lean promote from within respect for individuals the things that are drilled into your head i wonder you know what are the principles that you know sometimes they come become dogma but they're good dogma i don't mean that as a pejorative what are the things that that you built your business on the principles that you're sort of most proud of well i think there is so there are five in fact we call um uh some of the standards so five tenants we have we call we call this high ambition leadership which is more than just about making money and as just like the us is the leader of the free world we have a responsibility beyond u.s same way netscout has a responsibility beyond our own company and and revenue and our stakeholders so with that in mind we have these five things which i think i wouldn't have been able to articulate that 20 years ago like this and but they were always there so first is this guardians of the connected world which you see it on our website guardians care about their asset it's not just about money we are going to solve problems in the connected world which nobody else is able to solve or have the passion or have the resources and willpower to do it so that's that's the overall theme of the company guardians of the connected world connected world is changing broad new problems are coming our goal is there are pros and cons of every new thing our goal is to remove all the cons so you can enjoy the pros so that's guardian of the connected world then our mission is accelerate digital transformation meaning remove the road blocks people are looking at enablers but there are barriers also how do you remove the barriers for our customers so they can improve the fruits of digital transformation for example going to the cloud allows you to outsource some of the stuff especially in this time of agility and and dependency you can cut your cost but that comes with the price that you lose control so our product big bring the control back so now you can enjoy the pros and the cons and i call it sometime how do you change the wheels of your car while driving well if you change the four wheels then carve is going to fall down but how do you put one wheel in the cloud well that's what the our vision is visibility without water we'll give you the same information which is the third part so we have this uh tagline and for the company and then we have the mission accelerating digital transformation our vision is visibility without border when you run your application no matter where you run we'll give you the same piece of information that allows the people to make this transparent transparent migra that's migration transparent from a monitoring and visibility point of view then the fourth area is about a technology we call it smart data technology the whole world is talking about artificial intelligence machine learning but who are you going to learn for is your ai really authentic or is it truly artificial and that comes from smart data data is the oil of the new industry that's the oil and and people are not focusing on that they're saying i have lots of data but you don't have the data which we have in the past we said we are not going to share the data with third parties so in recently we have changed that you say yeah we'll there is the price for that we'll do that so we are branding ourselves as a smart data company where the whole industry is talking about smart analytics and i said we make smart people smarter and lastly uh the the value system of netscout is called lean but not mean okay and uh anybody can get lean if you get fat you can get your operation but how do you do lean decision making so you never have to be in me like net score never had delay in the last 35 years we have ups and down our stock has gone to three dollars and has gone to forty dollars but company continued to invest and uh and that's why we have this reputation we have with this tom here or steve here the tenure at netscout is 10 15 years minimum even in sales and people don't realize the power of that because some of our customers tell us hey your sales people are around longer than our employees and that how it builds a franchise of loyalty in the customer base we underestimate that this continuity part so there are many aspects of not what is the definition of not being mean the lean and mean is is sort of people are very proud of that and i think you can be lean without being mean and how do you become lean is don't hire when in good times unless you need them the reason people are able to do it is because they think i can fire any time so let's build up the fact so there are a lot of decision making we do around this and that's what i talk about in the book it's not about technology and this is i would say it's just one of the five diamonds but it's probably one of the most important ones and is one of the biggest differentiator of netscope well it's obviously served you well i mean no layoffs in 35 years the the retention metric is is very impressive i mean again i go back to my experience i was at idg for 15 years my passion was always to start my own company but i didn't want to leave because it was such a great culture and it seems like you've created something similar you know i talk to cios and ctos a lot too about about you know it's always people process technology and of course we want to talk about tech because we love talking about tech but they always tell me look tech comes and goes it's the processes that you put in place the culture that you have in place we could deal with the tech and it and it sounds like you've created a similar dynamic and i think back again when you started there were proprietary networks it was ibm sna dec network every mini computer had its own network then you know tcpip came in the whole world it changed and exploded but yet you said guardians of the connected world and that's kind of been your your focus from really day one you know i i loved what you said about the business the the network is the business remember the network is the computer that scott mcneely popularized so really kind of a similar dynamic there so it seems anneal that that framework that you just laid out those core principles have actually allowed you to ebb to flow to deal with stock prices and still retain people for very long periods of time maybe one more thing to add there is that on the lean but not when you talk about generalities we don't look any different like everyone cares about happy customers they care about happy employees and they care about happy stakeholders shareholders everyone including us but what's the order what's uh what's where do you start so we start with employees we say if they're happy employees they create success happy customers and then because of that they drive they buy more stuff and we create happy shareholders whereas if you start with happy shareholders you may not get happy employees and so and so all i'm saying is that everyone probably believes in what what we are saying or what i'm saying but how they implement it and then like really walking the talk is the most important part well i think you're right i mean i think you know the financials is a byproduct of happy employees which drive happy customers if you take care of employees and customers then good good things will happen uh if you start with trying to micromanage the finances of course we all attempted to to do that um i i wonder if we could talk a little bit about so just to bring it forward a little bit we're talking about how netscout has essentially from a cultural standpoint been able to withstand the ups the downs i mean you've seen since since you know over 35 years a lot of the the the downturns and the the tech softness the tech bubbles the great you know recession obviously now we're in the middle of the pandemic um i and i wonder if you could talk to that specifically so the data that we have from our survey partner etr enterprise technology research shows that before the pandemic around 16 of employees worked from home we're talking about truly remote workers not you know a couple days a week and when we talked to cios today they tell us it's you know well over 70 percent now but they fully expect that when you know the world comes back to the new abnormal i call it that it's it's that number is going to that 16 is going to double to more than double the 34 so it's it puts stress on on the the network it changes the the direction of the traffic it changes the security uh emphasis maybe you could talk a little bit about that just in terms of how you you are helping your customers respond specifically so i always talk about like is this a new problem or is the bad problem getting worse and so i put it in that bad problem getting worse so if you make the bad to zero then you can't multiply it so i think it's highlighting some of the problems which are already there are being highlighted by a lot of people are telling are you seeing more attacks no we are becoming more conscious of the attacks we always had we have more time by the way hackers have more time too because they are also sitting at home doing things so what i'm saying what i feel is that two parts one is that i think people should not in the when the new normal comes or new abnormal then i think people should not make people work from her for the wrong reason certain people are saying oh i can save money that's the wrong reason but if it's efficient we should do this so we are doing some interesting things for home users to feel how they can feel that they're really working from the office and so yeah there are some new challenges on how we monitor because when a user complains now about a performance to it because they can't get their work they don't know whether it's our network or is the isp or is their wi-fi network so we try to provide the root cause analysis as quickly as possible which we call mean time to know and one of the things i didn't mention earlier about the what is the uniqueness of our technology when we use the network vantage point to drive visibility it's almost like the blood test when you have a problem if you tell the doctor i said hey what is my problem and they start looking at all kinds of things it's going to take forever but if i take the blood test i'll be able to do the i will know what the next thing to do so in a way we are doing the blood test of the user experience security problems and when we do that we can come up with some very unique things so in the we think that we'll be moving on into other areas so the visibility is the means to an end the end could be performance management could be visibility troubleshooting uh and could be security forensics like blood tests can be used for dna evidence also and so we have all the technology so we are moving on as we move to the home user we are applying that our techniques not just for service assurance or end user experience monitoring but also for security financing and one example i give you the i always talk about and you'll see that in my book being different before being be better first be different get the earplugs out of the audience before you tell the story and you don't do that even though we are very big we are very small compared to a lot of companies in the industry compared to big players like cisco ibm and all those so the new thing which we are looking at in security is the security industry is catching the act we are going to catch the actor if i can get into the what they were doing before the act before they did the ransomware what were they doing well that required continuous monitoring of the traffic and that's what we do so when we do catch the actor catching the thief not what they're stealing then you're preventing tomorrow's attack and that's basically the innovation part of netscout which we have been pushing for but we somehow decided not to apply that to security because we had enough problems to be sold as guardians of the connected world from a monitoring point of view and so those are those are some of the things we'll be applying as as we move forward and i feel that those are equally applicable before the pandemic and after the pandemic and it's just polarized more because more people are working from home it's interesting what you're saying about the blood test uh that's a great analogy because it kind of eliminates the guesswork uh and and removes the opaqueness uh goes right to sort of the hard heart of the matter you call it mean time to know um and and it's interesting too to look at productivity i i mentioned some of the survey work when we talked to organizations they say to us that actually productivity has gone up since the the pandemic and my response to that is yeah no kidding because people are working 15-hour days you can't keep that up and and the silent killer of productivity is is the the not has having an elongated mean time to know um and having to to guess and so my premise is that this productivity gain if in fact it exists is not sustainable because we're doing it on the backs of our employees and it's going to it's going to burn them out i'm not sure whether it's real also see there are both sides it's not possible practical as you are saying because for example you're a sales person and you're working six seven hours and you're traveling six hours you can't be on the phone for 12 hours with the customer right now right how can they be productive is there both sides going some people are overworked and so definition of productivity itself is in question and how do you measure that and so that's what we'll have to look i think basically what i'm saying is we should do it whatever we do after the pandemic is over about how many people work from home should be based on your business model your expectation not just based on cost and a lot of people are looking at once again oh this is another cost saving exercise and that should not be the reason that's the wrong reason because then they're measuring the productivity in terms of reduced cost not everything else plus at least in net stock is a company which i mean every meeting i go to i use chalkboard and it's very very hard as a for our company like somebody like ibm where most of the people were there 50 offices they were remote is the easy transition it's not easy for netscout and so right now we focus on safety but we need to come up with a good hybrid model later on and different people will set up differently but what we do will be relevant in all cases yeah but i think you're making a good point that it's not some kind of mandate to drive your costs down or we saw last decade there were a couple of prominent companies that were mandating actually working in the office eliminating work from home so obviously the wrong side of history you know who they didn't know a pandemic was coming but so so how how will you make that decision uh will you is it really a discussion case by case with the employees or how what's the framework for you guys to decide that well i think so right now our focus is on safety so it's completely optional in fact we don't even allow more than 20 percent and that's only in the headquarters other places we have less than five percent people coming right and only essential workers manufacturing and all those so right now is completely optional but my personal preference when there is no risk these people should come to work like they were coming before we like to make it as close as possible to the old normal but that's not going to be the case for other companies because they're bigger in size they have other things at play but certainly we are not going to do it or because it's cheaper for net scores because we when people work from home and so we will see how it goes i think it will be a transition but i can see we going back to new normal in a year from now if the things start winding down in six months within a year or so we should be getting back to uh some normalcy and but that doesn't mean it's going to be true for our customers so from a product point of view we are doing several things so we can help the customer through this transition and by the way one other thing i wanted to mention earlier when we talk about the blood test how does it relate to guardians of the connective connected world if you believe in that what did the industry do they made sure needles were not painful that blood test was reliable you could there is no hygiene issues or no issues like that the cost has come down as a guardian of the connected world because we do that that's what we have been doing we are removing the banners to a great idea but lot of other companies gave up and then they have different strategy and some are successful some are not so as a guardian of the connected wall our goal is to continue to make this practical use imagine if blood test industry has not done that where we'll be right now and that's what what i meant by guardian of the connected world this is not easy to do and sustain that in for a period of 20 30 years but we have been able to do that and we get a lot of challenges from naysayers or this will not work at high speed when i started mad scout it was 10 megabit ethernet now we have 100 gigs 100 gig ethernet and we are still able to handle it and nobody thought in those days that you can even get 200 likes people were questioning us but what happens is other things keep working in the market intel is making improvements a lot of people are doing work to solve the problem and we leverage that and and that's how we are able to uh sort of sustain this guardian of the connected world team yeah you know the other key aspect of the guardian of the connected world again not to overdo the blood test analogy but the time to results is very important if you if you have an issue and you have to wait wait weeks for the results and your doctor you can't get a hold of her and so you're you're successfully dealing with that in real time or near real time and that that to me is is critical a very important point thanks for reminding me because i forgot today that's one of the things i say all the time hey this one of the big things we have done if blood test industry has done it how long take to get results nowadays you can get results done in in like two hours and doctors can get a report in couple of hours that's what we have done that's like mean time to know which we talked about with our technology i think we're basically the all the issues that you can't even breathe without doing something on the network so if you're listening to the traffic or hearing that uh what the conversation you can form an independent view of what is happening and that could be the that's the smart data which then becomes the basis of analytics whether analytics in the security space or not and so that's uh and that one thing we have not changed this technique now the outcomes are different what are we doing with the visibility is different is keep changing the number of customers and the type of customers are different but ultimately that part has interestingly has not changed i wonder if i could ask you i'd like to ask ceos especially those that are technologists and business leaders you know their thoughts on on the cloud i mean our data shows that the public cloud is growing in the 30 plus range annually the big three cloud public cloud players now account this year probably for close to 75 billion dollars in revenue maybe even a little bit more you know what what do you see driving this growth what does it mean for your customers well i think so forth we have a big announcement coming out called smart cloud monitoring to address this but what's the meaning of that i think what our customers are looking for is that it's it's not all or nothing it's not that everything is in the cloud or everything is in the program it could be private cloud public cloud colos the way vpns are laid out so they want to make sure that they can use our technology to do this react and analytics regardless of what decision they make and even five years from now there'll be enough non-cloud stuff okay so that's what we are trying to do we want to that's what is visibility without water and when they do that they say that helps them decide what's the best mode of operation for them for what application moving blindly to the cloud is a problem not going into that area is is also a problem but i think this the two new things have happened recently i would say one is sort of because of this crisis people don't want to own uh like hospitality industry okay this would i mean they're obviously having a big big issues with them but if they want a lot of the infrastructure they could have turned off some of that and so that's driving more movement to the cloud but i think there is a lot of choices available about a year or two ago i think affordable pricing model multiple choices not just aws and technology maturing where you can you can really implement and have a good experience i think those have become big enablers and so i think now it is possible to get to massive movement to the cloud but then they want to make sure that i'm now i'm outsourcing my problems but i'm not also outsourcing my vision to the cloud vendors because previously the way in the iit industry a lot of problems were solved is it was called the war rule let's get everyone who reports to me and everyone who reported to you but now that everyone doesn't report to you so how do you maintain the control when i complain to my ci hey my webex is slow or office three seriously and how does it resolve that problem because they cannot tell me oh we outsource them so i can't tell you that well we should not have outsourced them to the cloud so how do you drive this collaboration between the providers and the consumers is going to be key to accelerating this transformation because otherwise the cost of capex cost of reduction of moving to the cloud will be offseted by the increase in operax and customer satisfaction for the customer and so if we can help deal with one of the parts industry is already doing the other big part of making cloud work i think then we'll have the best chance of success yeah and of course the security has implications on the security model you were talking earlier about that as an opportunity people sometimes think oh yeah i put put my data in the cloud i'm good on security but there's there's a shared responsibility uh again we talked about different traffic patterns uh you've got work from home going on uh so and it's interesting when you juxtapose a sort of industry narrative on security which is it's it gets harder and harder and harder and you hear some of the cloud players say hey the state of security is really good uh but when you talk to csos you know they'll talk about the lack of talent uh the challenges they have the tools tools creep the fact that they spend more but the adversaries just keep getting stronger and stronger and stronger it's a really serious problem i mean maybe we close there i mean kind of how do you see it from your your vantage point let's look at the blood test so i look at if you don't the technique which we are talking about at least in the dimension of security monitoring then you are going to a lot of little things because you are doing little things you are going to be do a tool creep and because of that you have a like a talent issue and i think if you can make the right stuff work then you will not have this this talent issue and i feel that we are always looking solving yesterday's problem okay because we are not watching what led to the attack we are just dealing with the attack as an incident a security issue so i think continuous monitoring of deviation traffic allows you look at the deviation of the north so signature based security is a big portion but how do you know the signature of tomorrow and well you know that because you know the normal but only way you know normal is if you have been monitoring what was going on not for a specific event but deviation from normal that's what our approach is going to be anomalous behavior detection through our smart data and then you apply machine learning and ai algorithms to that i think that could be nirvana and but we don't have all the smart people for analytics but we can feed our data to those smart people and that's something we are going to bring up and the reason i feel it will be successful because this idea has been widely successful for netscout in the non-security space yeah i think you're bringing up another point that i've talked about a lot which is we've the industry has gone from sort of an industry of products to platforms and now ecosystems is really driving a lot of the innovation it's exactly what you're talking about feeding data to other partners data partners and now you start thinking about iot and the edge and machines talking to machines i mean i put you know video cameras up in my house to to make my environment more secure but of course i'm scared to death that those things can get hacked um it's a very complicated situation and the the power of many is going to trump the the the resources of one and so i'm glad you you brought that out um maybe give us your final thoughts anil it really has been a pleasure talking to you well i think the vr one of the things people have asked me is uh is why did you start another company especially in silicon valley i said with this spot many companies but they all happened to be called netstar netscout 1.0 2.0 3.0 actually we we are into the 4.0 i sometimes say you know george foreman's four sons they're all called george foreman so it's like one and so every time we do something different and now we are in the process of launching netscore 5.0 it was partly because maybe accelerated because of what's what's going on with the pandemic because there are some new challenges which we then here for and we are entering the security space so i'm very excited about repeating what we did in the traditional monitoring space service assurance space both for enterprise and carriers to the security space and people will question us how come it took so long while we were solving other problems which were more interesting than this for netscout and now we're going to bring that technology and all the tenants guardian of the connected world smart data to the security space and also i mean people are around for a long time we are also building the next generation of leaders at netstar and and so we have our hands full over the next two three years in uh building the next generation of net scout solving some of the problems which industry is facing without abandoning our tenants and the culture and if we can do that i think uh there'll be uh we'll be going to uh to the next level in terms of netscore branding and leadership well given given the guiding principles that you shared with us earlier the the the fundamental technology that you have around visibility uh i think that's served you very well and i think there's no shortage of of opportunity uh for netscout so neil thanks so much for sharing your story and coming on thecube good thank you all right and thank you for watching everybody this is dave vellante for the cube we'll see you next time [Music] you

>>from around the globe. It's the Cube with digital coverage of VM World 2020 brought to you by VM Ware and its ecosystem partners. Welcome back. I'm stew Minuteman, and this is we've actually reached the end of the cubes coverage of VM World 2020. Hard to believe. 11 years we've done lots of interviews here has been great to be able to engage with the audience talk, talk to the executives, talk some customers, but saving one more for you. So happy to welcome to the program is the first time on the Cube. But we've been talking to him since they came out of stealth. So I have the co founder and CEO of Minhai. Oh, and that is a non Babu Harry Asami A B. So nice to see you. Thanks so much for joining us. Thank >>you too. Thank you for having me on the show. >>Alright. So we love when we get to talk to the founders of companies were gonna dig into your company. But before we do just frame for us, you're not really high performance. I Oh, I oh, is in the name of your company. Um, men might make me think that there's some miniaturization, but give us the VM Ware connection. Obviously, VM Ware talked a lot about Cloud this week. They've talked about going deep into a I and computing. So we know this ecosystem has changed a lot in the 11 years that we've been covering it. Tell us how you and your company high end >>sounds good. Yeah. So men in many of those stands for minimalism right somehow in the enterprise like it has always been like shiny, heavy, complex things, find complex solutions to simple problems and charge them a lot. That has been the trend in the past, right? That's what Cloud has recent in the Enterprise and men on mini Iot is actually about solving that data storage problem. A very large scale. And the solution is like find simple solutions to complex problems. And we grew in the cloud in the both in the public and Private Cloud, and we are now the fastest growing object storage for the private cloud. And now we, um, we're coming into the government, the territory we actually CVM where is set to lead the kubernetes race. And in the Cooper Natives, if you look for an object storage pretty much, many ways standard. And this is where we bring our ecosystem toe. Be aware. And we, um where brings the enterprise market of cloud And this is the start off the private cloud. In the long run, I think public and private cloud will look alike. >>Yeah, absolutely. We've We've been writing about this for for for for many years a b We saw the enterprises taking on more of the characteristics of the hyper scholars, the hyper scholars. Of course, they're coming more to the enterprise. Ah, lot of discussion about hybrid and multi cloud these days. But what I want you to explain a little bit when? When When when your company was formed. You talk about, you know, doing these kubernetes environment. You do partner with AWS and azure, but ah, lot of what you do is on premises and that strikes people as a little bit unconventional in the thing. Or definitely 2017 and even for 2020. So help us understand. You know what it is exactly that you know the technology bring and why you think it's the fit for if you extend making private cloud on par with public. >>Yeah, it's not surprising to us at all, but it made no sense when we started with the rest of the world, right? Even the investors like not our other investors but the typical venture community toe the rest of the world. They thought that an object storage if it is not useful inside AWS, there is no use but an object storage at all. And we our question was very simple that the amount of data the world will produce in the next 10 years bulk off the data. Where is it going to be? Right? And it's not going to be in the public cloud. And it didn't sound obvious back then, right? And we saw that in the long run, public and private cloud will look alike but bulk of the data if it's going to be generated outside AWS while AWS s three sets the standard, the rest of the world what are they going to do? So many who was raised to be the S three for the rest of the world and the rest of the world is the biggest market. And back then there was no private cloud. There was public cloud and public cloud. What meant only AWS, right? And this was not so long ago. We're talking like 56 years, right? And then soon multi cloud came from multi cloud private cloud came what really accelerated. This is basically kubernetes and containers, right? In fact, containers started the trend and then Coburn It has accelerated it further nowadays. If you if you see why it's no longer a dream, are a faith based model, right, it's actually we're we're talking about, like a $540,000. Actually, 540,000 doctors pulled a day, right? And 400 like 400 well million or so Dr Pools in aggregate. That shows that the entire industry has changed, and it's already the Coburn. It is even public or private cloud. It is the one hybrid infrastructure layer, and now it has now it's no longer private Cloud is that question right? And customers are now able to move between public and private cloud. The trend is hybrid hybrid cloud. I think it's irreversible. >>Alright, you talked about Dr Poles and the code there, so let's make sure our audience understand exactly what you are. Sounds like your software sounds like open source is a piece of it. Help us understand. You know how you fit with Because if we're talking about object storage, there's gotta be some infrastructure underneath that. What does mean I owe provide and where do you turn to the partners? >>Yeah, so just like server less, it means that it's not like there is no server, right? It's about a software problem. Similarly, storage right When store when object storage is containerized, we still need drives, right? That is where VM ware V Sand comes. Descends Job is to virtualized the physical layer toe the basically container layer. But end of the day if you see the it is a software problem and what may I would just like a database would solve the metadata data store problem. I mean, I will solve the blob data problem. And in the public, cloud object storage is the foundational piece. It is the primary storage, but we saw this as a software problem, and when customers started building these applications, they actually containerized their application and use Cooper notice to roll out their application infrastructure. And when they do that, they cannot possibly by a hardware appliance on the public cloud. And even on the on the private cloud, they when they when they completely orchestrate two containers, they cannot roll out hardware appliances. This is where the the industry the cloud native community always saw this as a software problem. It was obvious to them for the enterprise I t it was not so clear. And the storage industry giants, if you see everyone off them is a hardware appliance play, and they are in for a total shock. And we were basically as a as reset with their seven or to update one, if there is a lot of interesting things to come. >>All right, So if if I understand Here you sit from a VM Ware environment, I've got V sand underneath. I've got Tangguh above, and you're you're providing that object service in between. So for our for our friends in the in the channel market on when thinking about gear, anything that V san can sit on, you just can come along for the ride. Do I have that right? >>Yeah. So underneath the sand is basically bunch of J boards, right? These are like Dell and HP servers with the drives in them on This is not a hardware appliance anymore, right? You look at the storage market, it is. Stand our NASA plans. That is how the enterprise I t operated not in the club world. And as we and we're moves into the cloud world, everything looks cloud native and in this case, the sand. NASA plans have no role to play. Even the object storage hardware appliance has no role to play because we and we're becomes the end where Visa becomes the new block storage layer. And then they have positioned object storage database. Everything as a data data store are a data persist since layer. So only this software only the software that is contained race gets to play on top of, um, where in the new World, including the storage itself. And it's No, there is no appliance here. >>All right, so and your your solution is is listed as kubernetes kubernetes native. So now you mentioned VCR seven, VCR seven, update one Now house full kubernetes support. I'm assuming Then you can plug into tansy you you can plug into, uh, Amazon Azure. Other kubernetes options out there. Is that the case? >>Yeah, So from a customer point of view, right? If you are on the enterprise, I d. Environment Now from I t administrator point off you. Nothing changes much other than from the V Center console itself. You now get to see me, and I will in in the first suspend data services. You click and deploy entirely as a software without even learning to spell Cooper notice. You can build a private cloud storage multi tenant exactly like how public cloud storage outrage. And that is from the private cloud point a few right, and it's purely software. You're not waiting for six months, but the hardware to arrive and long procurement cycles and provisioning all that is now provisioned as a software container. In just five minutes, you can actually set up a private cloud in Prospector. That's for the private cloud, right? But why? The reason why customers want this to be a software problem is they roll out their software on the on the private cloud on the public cloud for burst, wear clothes and sustained work clothes on private cloud burst workloads on public cloud. Noncritical jobs are anything that is fast moving on, convenience based. They push it to public cloud. Customers do want tohave one leg here and one like there. And nowadays even the edge on decentralized on the from the telco space toe video on other other areas even the edges now growing toe. They want a your software solution. The entire data center software is now containerized. They can roll out Public cloud Our private cloud are on the edge On with me No, we solve the data side the compute side Then we're already has done a wonderful job on the networking side. They have done it on on the beast on the storage site dated the physical toe container layer movies. And now the data storage part is what we solved. Now what does this do to the end user? Now they can build software and truly deploy on public private our age without any modification on entirely it is a software problem. This >>great. What do you find? Or some of the more prevalent use cases, you know, sitting on top, What applications or the key ones that people are deploying your solution for >>Yeah, So in the public cloud, if you see, that's that. That's actually a good place to start if you see in the public cloud, right, starting from even simple static website hosting toe aml, big data, workloads toe. Even the modern databases like Snowflake, for example is built on object storage in the public cloud. It has become a truly horizontal play. And that is how it started right there. W started with history and then came everything else. And now that trend is beginning to percolate into the enterprise. And surprisingly, we found that the enterprise was the explosion of data. Growth is actually not about like cat videos, right? What? What are these touring? Mostly We found that bulk of the data that is drowning that crisis messing generated data. And these are basically like some kind of log data event data data streams that are continuously produced on that actually can grow from 10 terabytes to 10 petabytes in a very short time. This is where clearly object storage has become the right choice, just like in the public cloud. But customers are now adopting object storage as the primary storage and now multiple applications. Whether it is the cloud native applications in like the Hangzhou Application Service like spring boot and like all the clothes on re stack from their toe. So all the m l big data workloads pretty much everybody has been verging to object storage as there foundation. >>Yeah, absolutely. You seen some of those use cases very prevalent here in the VM Ware community. I heard you talking about it. I was expecting to hear you talk about Splunk data protection, something that's been a big topic of conversation in the last few years. Obviously, VM Ware has a number of key partners. So I'm assuming many of those air who you are also working with. >>Look, it felt good broad Splunk Splunk itself is actually is an important move that what we did recently with VM where finally we can run Splunk natively on BM where at large scale and without any performance penalty and at a price point that it becomes really attractive Now comparing Splunk Cloud, where's the Splunk on Prem? We can actually show like at least like one third off what it would cost to run on Splunk load. So I don't know Splunk themselves would like it, But I think Splunk as a company would like what customers like, right? And this is where Splunk actually now can sit on many, many us, all the all their data stores. They call it smart store underneath underneath me. I will now, when the previous original Visa incarnation, we couldn't actually your huge amounts of data. But now, with the visa and direct, we actually have access to the local drives and you can attach as many drives as you want. Then if you want more capacity, more more number of servers so you can pack thousands and thousands off drives at a price point that even public cloud cannot be anywhere closer. And this is actually important. Yeah, environment for the Splunk customers. Because for them, not only the cost right, even the data is sensitive for them. They cannot really, really push it to the public cloud data generated outside of the public cloud. If data generated inside Public Cloud, probably Amazon has their own solution, and Splunk cloud makes sense. But when data is produced outside, these are sensitive data and it's huge volume, and they produce on an average, like the kind of users VCs center about. It's a day on on, then it's only growing at an accelerated pace. And this is where the Visa and Direct and Mini Oh, you can now bring that workload onto the number. Finally, the ICTY can control the control, the Splunk deployments. This is something important for I t right in the past, if you see big data workloads always ran on bare metal and silos, something I d hated right This time it is flexible that it's not just flexible, exactly gets better. >>Well, it sure sounds like the technology maturation has finally caught up on the VM ware standpoint with the vision that you and the team had. So give us a little bit. Look forward now that you've got kubernetes really being embraced by VM where on and starting to see maturation in this space. Where do we go from here? >>So we were actually, If you see what they brought to the table this time, they didn't actually catch up with others, right? Typically, the innovation in the recent times happened in the open source space and then the large vendors will come and innovate. Startups and open source started the innovation large, large. When the large winters come in later. But this time around, remember, actually did the innovation part and these and direct. It's actually a big step forward in the Covenant of CSE space. And the reason why it's a big step is C s A. Traditionally is designed for the sand gnats vendors and using the same C s. A model, remember, was able to bring in large work clothes and that allowed entirely to use the local drive possibility. Right now it moving forward. What What we will see. What were said to see is the cloud native workload. Actually a ran as a silo in the Enterprise, right? There was big data workloads. There was the applications team that ran Cooper knitters and containers on their own. There are on their on their own develop shop on enterprise. I'd ran the idea introspect These three were not connected on finally this time around. By bringing cover natives native into the I T infrastructure, there is going to be a convergence. You will not. The silos will get eliminated. Big data, big data workloads, ml wear clothes on bare metal will now come toe come toe. Then I will be aware that the Governor disk combination and you will see the the coordinative applications space. They will hand over the physical layer infrastructure onto the VM Ware e and everybody coming together. I think it's the best. Big step forward. >>Well, maybe. I sure hope you're right. We love to see the breaking down of silos. Things coming together. We've been a little bit concerned over the last few years that we're rebuilding the silos in the cloud. We've got different skill sets different there, but we always love some good tech optimism here, uh, to say that we're gonna move these sorts of Thank you so much. Great to catch up with you and definitely look forward to hearing more from you and your customers in the future. >>Thank you to this. Wonderful to be on your show. >>All right. We want to thank everybody for joining VM World 2020 for day. Volonte John, for your big thanks to the whole production team and of course, VM Ware and our sponsors for helping us to bring this content to you. As always, I'm stew Minuteman and thank you for joining us on the Cube

(upbeat music) >> Live from Barcelona Spain, it's theCUBE. Covering Cisco Live! Europe. Brought to you by Cisco and its ecosystem partners. >> Hi everyone welcome back to the theCUBE's live coverage here in Barcelona, Spain for Cisco Live! Europe 2019. I'm John Furrier and my co-host Stu Miniman, Dave Vellante is out there as well co-hosting this week. Our next guest is John Apostolopoulos who's the VP and CTO for the Enterprise Networking Business, Unit Lab Director for the Innovation Labs. Here to talk with us about AI and some great innovations. John thanks for coming on theCUBE, great to see you. >> Thank you for inviting me, pleasure to be here. >> So, Cisco has some big announcements, the messages coming together certainly the bridge for the future, bridge for tomorrow, whatever the phrase is. You know, kind of looking at that new world connecting on premise, cloud, ACI anywhere, hyper-flex anywhere, lot of complexity, being mis-tracked the way with software, separate from the V-Comp from the hardware, lot of scale in the cloud and IoT and all around the edge. So software is a big part of this. >> Oh yes. >> So can't help but think, okay complexity, scale, you see Facebook using machine learning. Machine learning and AI operations now, a real conversation for Cisco. >> Yeah. >> Talk about what that is, how are you guys looking at AI, and machine learning in particular, it's been around for a while. What's your thoughts on Cisco's position and opportunity? >> Sure, yeah. Cisco's been investing or using AI for many, many years. What happens to Cisco, like most companies, we haven't really talked about the machine learning as a term because machine learning is a tool used to solve different problems. So you talk about, what are the customer problems we have? And then we saw, no matter how good our solution is, but we haven't really talked about the details about the how but, we've using at Cisco, like myself from past careers and so forth for many many years some machine learning. Security has been using it for multiple decades for example. >> And where's the use case for machine learnering, because it's one of things where there's different versions and flavors of machine learning. Machine learning we know powers AI and data feeds machine learning, so do you have all these dependencies and all these things going on, how do you...how should someone think about sorting through machine learning? >> Well machine learner itself that term is a very broad term, it's almost as big as computer science, right? So that's where a lot of the confusion comes in. But what happens is you can look up what types of problems we want to solve, and when you try to look at what types of problems we want to solve, some of them...for example some problems you can exploit the fact that the laws of physics that apply and if the laws of physics apply, you should use those laws. We can either figure out that if we drop this, this will fall at some speed by measuring it and using a machine learning or we have gravitational force and friction with the air and re-account for that and figure it out. So the many ways to solve these problems and we want to choose the best method for solving each one of them. >> And when the people think about Cisco, the first reaction isn't "Oh machine learning... innovator." What are you guys using machine learning for? Where has it been successful? What are you investing in? Where's the innovation? >> Sure sure, so there's a lot of problems here that come into play. If you look at...if you look at a customer problems, one example is all the digital disruption. We have on the order of a million devices, new devices coming on to the network every hour throughout the world. Now, what are those devices? How should you treat them? With machine learning we're able to identify what the devices are and then figure out what the network caches should be. For instance when IoT device you want to protect it, protect it from others. Another big topic is operations. As you know people spend, I think it was The Gardner identified that people spend about sixty-billion dollars per year on operations costs, why is it so much? Because most of the operations are manual, about 95% manual, which also means that these changes are slow and error-prone. What we do there is we basically use machine learning to do intelligent automation and we get a whole bunch of insights about what's happening and use that to drive intelligent automation. You may have heard about Assurance, which was announced at Cisco Live, one year ago at Barcelona and both in the campus with DNA Center we announced Cisco DNA Center Assurance and the data center went out, network and network analytic engine. And what both of these do is they look at what's happened to the network, they apply machine learning to identify patterns and from those patterns, identify, is there a problem, where's the problem? How can we...what's the root cause and then how can we solve that problem quickly? >> John, can you help us connect where this fits in a multi-cloud environment? Because what we've seen the past couple of years is when we talk about managing the network, a lot of what I might be in charge of managing, is really outside of my purview and therefore I could imagine something like ML is going to be critically important because I'm not going to be touching it but therefore I still need to have data about it and a lot of that needs to happen. >> Yeah, well one of the places ML helps with multi-cloud is the fact you need to figure out which...where to send your packets, and this comes with SD-WAN. So with SD-WAN we often have multiple paths available to us and let's say with the move to Office365, people are using the SaaS service and they want to have very good interactivity. One of the things we realized is that by carefully selecting which path we can use, at the branch and the campus too, we could get a 40% reduction in the latency. So that's a way we choose which colo or which region or which side of Office365 to send the packets to, to dramatically reduce latency. >> What's the role of data? Because when you think about it, you know, moving a packet from point A to point B, that's networking. Storage acts differently 'cause you store data data's got to come back out and be discovered. Now if you have this horizontal scalability for cloud, edge, core coming into the middle, get of the data 'cause machine learning needs the data, good data, not dirty data you need clean data. How do you see that evolving, how should customers then be thinking about preparing for either low-hanging use-cases. Just what's your thoughts and reaction to that? >> Yeah well the example you gave is a very interesting example. You described how you need to get data from one point to another, for instance, for my device to a data center with applications over the cloud. And you also mentioned how the many things between. What we care about, not necessarily the application data, we care about... You know we want to have the best network performance so your applications are working as well as possible. In that case we want to have an understanding of what's happening across a path so we want to pull to telemetry in all kinds of contexts to be able to understand, is there problem, where's the problem, what is it, and how to solve it. And that's what Assurance does. We pull this data from the access points the switches, from the routers, we pour, pull in all kinds of contextual information to get a rich understanding of the situation, and try to identify if there's a problem or not, and then how to solve it. >> Its the classic behavioral, contextual, paradigm of data but now you guys are looking at it from a network perspective and as the patterns changed the applications centric, programmability of the network, the traffic patterns are changing. Hence the announcements here but intent-based networking and hyper-flexed anywhere. This is now a new dynamic. Talk about the impact of that from an AI perspective. How are you guys getting out front on that? It's not just North, South, East, West, it's pretty much everywhere. The patterns are, could be application specific at any given point, on a certain segment of a network, I mean it's complex. >> Yeah, its complex. One of the really nice things about intent-based network and those, it fits in really nicely and that was by design, 'cause what happens with intent-based networking, as you know, a user expresses some intent if it's something they want to do. I want to securely onboard the SIoT device, and then it gets activated in the network, and then we use Assurance to see if it's doing the right thing. But what happens is that Assurance part, that's basically gathering visibility and insight in terms of what's happening. That's using machine learning to understand what's happening in the network across all these different parts that you mentioned. And then, what happens is we take those insights and then we make intelligent actions and that's part of the activation. So this...with intent based network in this feedback loop that we have directly ties with using the data for getting insights and then for activation, for intelligent actions. >> John, always want to get the update on the innovation lab, is there anything particular here at the show or, what's new that you can share? >> So we're looking at extending IBN to the cloud, to multi-cloud, to multiple devices so there's a lot of really fascinating work happening there. I believe you're going to be talking to one of my colleagues later, too, T.K. He's, I think, hopefully going to talk about some of the machine learning that's been done and that's already prioritized as you know in encrypted thread analytics. That's an example of where we use machine learning to identify if there's malware in encrypted traffic. Which is really a fascinating problem. >> That's a hard problem to solve. I'm looking forward to that conversation. >> So some members of Cisco, Dave McGrew, in particular, Cisco Fellow, started working on that problem four and a half years ago. Because of his work with other colleagues, he was able and they were able to come up with a solution. So it was a very complicated problem as you saw but through the use of machine learning and many years of investment, plus the fact that Cisco's access to Talos which has, they know the threats throughout the world. They're a list of data in terms of all kinds of threats that's massive. That's pretty powerful. >> The volume, that's where machine learning shines. I mean you see the amount of volume of data coming in, that's where it could do some heavy lifting. >> Exactly, that's one of Cisco's strengths. The fact that we have this massive view on all the threats throughout the world and we can bring it to bear. >> Network security foundation only just creates so much value for apps. Final question for you, for the folks watching, what's in you opinion the most important story here at Cicso Live Barcelona, that people should be paying attention to? >> I think how we are trying to extend across all these different domains and make it like one network for our customers. This is still a journey and it's going to take time but with intent based networking we can do that. We're going across campus, WAN, data center to multi-cloud. >> How hard is cross domain, just put it in perspective. Cross domains reversal and having visibility into these, from a latency, from a physics standpoint, how hard is it? >> It's quite hard, there's all kinds of technical challenges but there's even other sorts of challenges. This is WiFi, right? IEEE 802.11 defines the QoS standard for wireless and that's completely different than how the internet group ITEF defined it for wired. So even between wireless and wired, there's a lot of work that has to be done and Cisco's leading that effort. >> And having all that data. Great to have you on John, thanks for spending the time and demystifying machine learning and looking forward to this encrypted understanding with machine learning, that's a hard problem, looking forward to digging into that. Again, truly, the breakthroughs are happening with machine learning and adding values with application centric world. It's all about the data, it's theCUBE bringing you the data from Barcelona, I'm John with Stu Mini, stay with us for more coverage after this short break. (upbeat music)

>> Announcer: Live from the Bill Graham Auditorium in San Francisco, it's theCUBE! Covering Pure Storage Accelerate 2018 brought to you by Pure Storage. >> Welcome back to theCUBE's continuing coverage of Pure Storage Accelerate 2018, I'm Lisa Martin, sporting the clong and apparently this symbol actually has a name, the clong, I learned that in the last half an hour. I know, who knew? >> Really? >> Yes! Is that a C or a K? >> Is that a Prince orientation or, what is that? >> Yes, I'm formerly known as. >> Nice. >> Who of course played at this venue, as did Roger Daltry, and The Who. >> And I might have been staff for one of those shows. >> You could have been, yeah, could I show you to your seat? >> Maybe you're performing later. You might not even know this. We have a couple of guests joining us. We've got Matt Burr, the GM of FlashBlade, and Rob Ober, the Chief Platform Architect at NVIDIA. Guys, welcome to theCUBE. >> Hi. >> Thank you. >> Dave: Thanks for coming on. >> So, lots of excitement going on this morning. You guys announced Pure and NVIDIA just a couple of months ago, a partnership with AIRI. Talk to us about AIRI, what is it? How is it going to help organizations in any industry really democratize AI? >> Well, AIRI, so AIRI is something that we announced, the AIRI Mini today here at Accelerate 2018. AIRI was originally announced at the GTC, Global Technology Conference, for NVIDIA back in March, and what it is is, it essentially brings NVIDIA's DGX servers, connected with either Arista or Cisco switches down to the Pure Storage FlashBlade, so this is something that sits in less than half a rack in the data center, that replaces something that was probably 25 or 50 racks of compute and store, so, I think Rob and I like to talk about it as kind of a great leap forward in terms of compute potential. >> Absolutely, yeah. It's an AI supercomputer in a half rack. >> So one of the things that this morning, that we saw during the general session that Charlie talked about, and I think Matt (mumbles) kind of a really brief history of the last 10 to 20 years in storage, why is modern external storage essential for AI? >> Well, Rob, you want that one, or you want me to take it? Coming from the non storage guy, maybe? (both laugh) >> Go ahead. >> So, when you look at the structure of GPUs, and servers in general, we're talking about massively parallel compute, right? These are, we're now taking not just tens of thousands of cores but even more cores, and we're actually finding a path for them to communicate with storage that is also massively parallel. Storage has traditionally been something that's been kind of serial in nature. Legacy storage has always waited for the next operation to happen. You actually want to get things that are parallel so that you can have parallel processing, both at the compute tier, and parallel processing at the storage tier. But you need to have big network bandwidth, which was what Charlie was eluding to, when Charlie said-- >> Lisa: You like his stool? >> When Charlie was, one of his stools, or one of the legs of his stool, was talking about, 20 years ago we were still, or 10 years ago, we were at 10 gig networks, in merges of 100 gig networks has really made the data flow possible. >> So I wonder if we can unpack that. We talked a little bit to Rob Lee about this, the infrastructure for AI, and wonder if we can go deeper. So take the three legs of the stool, and you can imagine this massively parallel compute-storage-networking grid, if you will, one of our guys calls it uni-grid, not crazy about the name, but this idea of alternative processing, which is your business, really spanning this scaled out architecture, not trying to stuff as much function on a die as possible, really is taking hold, but what is the, how does that infrastructure for AI evolve from an architect's perspective? >> The overall infrastructure? I mean, it is incredibly data intensive. I mean a typical training set is terabytes, in the extreme it's petabytes, for a single run, and you will typically go through that data set again and again and again, in a training run, (mumbles) and so you have one massive set that needs to go to multiple compute engines, and the reason it's multiple compute engines is people are discovering that as they scale up the infrastructure, you actually, you get pretty much linear improvements, and you get a time to solution benefit. Some of the large data centers will run a training run for literally a month and if you start scaling it out, even in these incredibly powerful things, you can bring time to solution down, you can have meaningful results much more quickly. >> And you be a sensitive, sort of a practical application of that. Yeah there's a large hedge fund based in the U.K. called Man AHL. They're a system-based quantitative training firm, and what that means is, humans really aren't doing a lot of the training, machines are doing the vast majority if not all of the training. What the humans are doing is they're essentially quantitative analysts. The number of simulations that they can run is directly correlative to the number of trades that their machines can make. And so the more simulations you can make, the more trades you can make. The shorter your simulation time is, the more simulations that you can run. So we're talking about in a sort of a meta context, that concept applies to everything from retail and understanding, if you're a grocery store, what products are not on my shelves at a given time. In healthcare, discovering new forms of pathologies for cancer treatments. Financial services we touched on, but even broader, right down into manufacturing, right? Looking at, what are my defect rates on my lines, and if it used to take me a week to understand the efficiency of my assembly line, if I can get that down to four hours, and make adjustments in real time, that's more than just productivity, it's progress. >> Okay so, I wonder if we can talk about how you guys see AI emerging in the marketplace. You just gave an example. We were talking earlier again to Rob Lee about, it seems today to be applied and, in narrow use cases, and maybe that's going to be the norm, whether it's autonomous vehicles or facial recognition, natural language processing, how do you guys see that playing out? Whatever be, this kind of ubiquitous horizontal layer or do you think the adoption is going to remain along those sort of individual lines, if you will. >> At the extreme, like when you really look out at the future, let me start by saying that my background is processor architecture. I've worked in computer science, the whole thing is to understand problems, and create the platforms for those things. What really excited me and motivated me about AI deep learning is that it is changing computer science. It's just turning it on its head. And instead of explicitly programming, it's now implicitly programming, based on the data you feed it. And this changes everything and it can be applied to almost any use case. So I think that eventually it's going to be applied in almost any area that we use computing today. >> Dave: So another way of asking that question is how far can we take machine intelligence and your answer is pretty far, pretty far. So as processor architect, obviously this is very memory intensive, you're seeing, I was at the Micron financial analyst meeting earlier this week and listening to what they were saying about these emerging, you got T-RAM, and obviously you have Flash, people are excited about 3D cross-point, I heard it, somebody mentioned 3D cross-point on the stage today, what do you see there in terms of memory architectures and how they're evolving and what do you need as a systems architect? >> I need it all. (all talking at once) No, if I could build a GPU with more than a terabyte per second of bandwidth and more than a terabyte of capacity I could use it today. I can't build that, I can't build that yet. But I need, it's a different stool, I need teraflops, I need memory bandwidth, and I need memory capacity. And really we just push to the limit. Different types of neural nets, different types of problems, will stress different things. They'll stress the capacity, the bandwidth, or the actual compute. >> This makes the data warehousing problem seem trivial, but do you see, you know what I mean? Data warehousing, it was like always a chase, chasing the chips and snake swallowing a basketball I called it, but do you see a day that these problems are going to be solved, architecturally, it talks about, More's laws, moderating, or is this going to be this perpetual race that we're never going to get to the end of? >> So let me put things in perspective first. It's easy to forget that the big bang moment for AI and deep learning was the summer of 2012, so slightly less than six years ago. That's when Alex Ned get the seed and people went wow, this is a whole new approach, this is amazing. So a little less than six years in. I mean it is a very young, it's a young area, it is in incredible growth, the change in state of art is literally month by month right now. So it's going to continue on for a while, and we're just going to keep growing and evolving. Maybe five years, maybe 10 years, things will stabilize, but it's an exciting time right now. >> Very hard to predict, isn't it? >> It is. >> I mean who would've thought that Alexa would be such a dominant factor in voice recognition, or that a bunch of cats on the internet would lead to facial recognition. I wonder if you guys can comment, right? I mean. >> Strange beginnings. (all laughing) >> But very and, I wonder if I can ask you guys ask about the black box challenge. I've heard some companies talk about how we're going to white box everything, make it open and, but the black box problem meaning if I have to describe, and we may have talked about this, how I know that it's a dog. I struggle to do that, but a machine can do that. I don't know how it does it, probably can't tell me how it does it, but it knows, with a high degree of accuracy. Is that black box phenomenon a problem, or do we just have to get over it? >> Up to you. >> I think it's certain, I don't think it's a problem. I know that mathematicians, who are friends, it drives them crazy, because they can't tell you why it's working. So it's a intellectual problem that people just need to get over. But it's the way our brains work, right? And our brains work pretty well. There are certain areas I think where for a while there will be certain laws in place where you can't prove the exact algorithm, you can't use it, but by and large, I think the industry's going to get over it pretty fast. >> I would totally agree, yeah. >> You guys are optimists about the future. I mean you're not up there talking about how jobs are going to go away and, that's not something that you guys are worried about, and generally, we're not either. However, machine intelligence, AI, whatever you want to call it, it is very disruptive. There's no question about it. So I got to ask you guys a few fun questions. Do you think large retail stores are going to, I mean nothing's in the extreme, but do you think they'll generally go away? >> Do I think large retail stores will generally go away? When I think about retail, I think about grocery stores, and the things that are going to go away, I'd like to see standing in line go away. I would like my customer experience to get better. I don't believe that 10 years from now we're all going to live inside our houses and communicate over the internet and text and half of that be with chat mods, I just don't believe that's going to happen. I think the Amazon effect has a long way to go. I just ordered a pool thermometer from Amazon the other day, right? I'm getting old, I ordered readers from Amazon the other day, right? So I kind of think it's that spur of the moment item that you're going to buy. Because even in my own personal habits like I'm not buying shoes and returning them, and waiting five to ten times, cycle, to get there. You still want that experience of going to the store. Where I think retail will improve is understanding that I'm on my way to their store, and improving the experience once I get there. So, I think you'll see, they need to see the Amazon effect that's going to happen, but what you'll see is technology being employed to reach a place where my end user experience improves such that I want to continue to go there. >> Do you think owning your own vehicle, and driving your own vehicle, will be the exception, rather than the norm? >> It pains me to say this, 'cause I love driving, but I think you're right. I think it's a long, I mean it's going to take a while, it's going to take a long time, but I think inevitably it's just too convenient, things are too congested, by freeing up autonomous cars, things that'll go park themselves, whatever, I think it's inevitable. >> Will machines make better diagnoses than doctors? >> Matt: Oh I mean, that's not even a question. Absolutely. >> They already do. >> Do you think banks, traditional banks, will control of the payment systems? >> That's a good one, I haven't thought about-- >> Yeah, I'm not sure that's an AI related thing, maybe more of a block chain thing, but, it's possible. >> Block chain and AI, kind of cousins. >> Yeah, they are, they are actually. >> I fear a world though where we actually end up like WALLE in the movie and everybody's on these like floating chez lounges. >> Yeah lets not go there. >> Eating and drinking. No but I'm just wondering, you talked about, Matt, in terms of the number of, the different types of industries that really can verge in here. Do you see maybe the consumer world with our expectation that we can order anything on Amazon from a thermometer to a pair of glasses to shoes, as driving other industries to kind of follow what we as consumers have come to expect? >> Absolutely no question. I mean that is, consumer drives everything, right? All flash arrays were driven by you have your phone there, right? The consumerization of that device was what drove Toshiba and all the other fad manufacturers to build more NAM flash, which is what commoditized NAM flash, which what brought us faster systems, these things all build on each other, and from a consumer perspective, there are so many things that are inefficient in our world today, right? Like lets just think about your last call center experience. If you're the normal human being-- >> I prefer not to, but okay. >> Yeah you said it, you prefer not to, right? My next comment was going to be, most people's call center experiences aren't that good. But what if the call center technology had the ability to analyze your voice and understand your intonation, and your inflection, and that call center employee was being given information to react to what you were saying on the call, such that they either immediately escalated that call without you asking, or they were sent down a decision path, which brought you to a resolution that said that we know that 62% of the time if we offer this person a free month of this, that person is going to view, is going to go away a happy customer, and rate this call 10 out of 10. That is the type of things that's going to improve with voice recognition, and all of the voice analysis, and all this. >> And that really get into how far we can take machine intelligence, the things that machines, or the humans can do, that machines can't, and that list changes every year. The gap gets narrower and narrower, and that's a great example. >> And I think one of the things, going back to your, whether stores'll continue being there or not but, one of the biggest benefits of AI is recommendation, right? So you can consider it userous maybe, or on the other hand it's great service, where a lot of, something like an Amazon is able to say, I've learned about you, I've learned about what people are looking for, and you're asking for this, but I would suggest something else, and you look at that and you go, "Yeah, that's exactly what I'm looking for". I think that's really where, in the sales cycle, that's really where it gets up there. >> Can machines stop fake news? That's what I want to know. >> Probably. >> Lisa: To be continued. >> People are working on that. >> They are. There's a lot, I mean-- >> That's a big use case. >> It is not a solved problem, but there's a lot of energy going into that. >> I'd take that before I take the floating WALLE chez lounges, right? Deal. >> What if it was just for you? What if it was just a floating chez lounge, it wasn't everybody, then it would be alright, right? >> Not for me. (both laughing) >> Matt and Rob, thanks so much for stopping by and sharing some of your insights and we should have a great rest of the day at the conference. >> Great, thank you very much. Thanks for having us. >> For Dave Vellante, I'm Lisa Martin, we're live at Pure Storage Accelerate 2018 at the Bill Graham Civic Auditorium. Stick around, we'll be right back after a break with our next guest. (electronic music)

>> Announcer: Live from the Bill Graham Auditorium in San Francisco, it's theCUBE covering Pure Storage Accelerate 2018, brought to you by Pure Storage. (bright music) >> Welcome to theCUBE. We are live at Pure Storage Accelerate 2018. I'm Lisa Martin also known as Prince for today with Dave Vellante. We're at the Bill Graham Civic Auditorium, really cool, unique venue. Dave, you've been following Pure for a long time. Today's May 23rd, they just announced FY19 Q1 earnings a couple days ago. Revenue up 40% year over year, added 300 new customers this last quarter including the Department of Energy, Paige.ai, bringing their customer tally now up to about 4800. We just came from the keynote. What are some of the things that you've observed over the last few years of following Pure that excite you about today? >> Well Lisa, Pure's always been a company that is trying to differentiate itself from the pack, the pack largely being EMC at the time. And what Pure talked about today, Matt Kixmoeller talked about, that in 2009, if you go back there, Fusion-io was all the rage, and they were going after the tip of the pyramid, and everybody saw flash, as he said, his words, as the tip of the pyramid. Now of course back then David Floyer in 2008 called that flash was going to change the world, that is was going to dominate. He'd forecast that flash was going to be cheaper than disk over the long term, and that is playing out in many market segments. So he was one of the few that didn't fall into that trap. But the point is that Pure has always said, "We're going to make flash cheaper than "or as cheap as spinning disk, "and we're going to drive performance, "and we're going to differentiate from the market, "and we're going to be first." And you heard that today with this company. This company is accelerated to a billion dollars, the first company to hit a billion dollars since NetApp. Eight years ago I questioned if any company would do that. If you look at the companies that exited the storage market, that entered and exited the storage market that supposedly hit escape velocity, 10 years ago it was 3PAR hit $250 million. Isilon, Data Domain, Compellent, these companies sold for between $1 and $2.5 billion. None of them hit a billion dollars. Pure is the first to do that. Nutanix, which is really not a storage company, they're hyper-converged infrastructure, they got networking and compute, sort of, hit a billion, but Pure is the the first pure play, no pun intended, storage company to do that. They've got a $5 billion evaluation. They're growing, as you said, at 40% a year. They just announced their earnings they beat. But the street reacted poorly because it interpreted their guidance as lower. Now Pure will say that we know we raised (laughs) our guidance, but they're lowering the guidance in terms of growth rates. So that freaks the street out. I personally think it's pure conservativism and I think that they'll continue to beat those expectations so the stock's going to take a hit. They say, "Okay, if you want to guide lower growth, "you're going to take the hit," and I think that's smart play by Pure because if and when they beat they'll get that updraft. But so that's what you saw today. They're finally free cash flow positive. They've got about a billion dollars in cash on the balance sheet. Now half a billion of that was from a convertible note that they just did, so it's really not coming from a ton of free cash flow, but they've hit that milestone. Now the last point I want to make, Lisa, and we talked about this, is Pure Storage at growing at 40% a year, it's like Amazon can grow even though they make small profit. The stock price keeps going up. Pure has experienced that. You're certainly seeing that with companies like Workday, certainly Salesforce and its ascendancy, ServiceNow and its ascendancy. These companies are all about growth. The street is rewarding growth. Very hard for a company like IBM or HPE or EMC when it was public, when they're not growing to actually have the stock price continue to rise even though they're throwing off way more cash than a company like Pure. >> Also today we saw for the first time the new CEO's been Charlie Giancarlo, been the CEO since August of 2017, sort of did a little introduction to himself, and they talked about going all in on shared accelerated storage, this category that Gartner's created. Big, big focus there. >> Yeah, so it's interesting. When I look at so-called shared accelerated storage it's 2018, Gartner finally came up with a new category. Again, I got to give credit to the Wikibon guys. I think David Floyer in 2009 created the category. He called it Server SAN. You don't know if that's David, but I think maybe shared accelerated storage's a better name. Maybe Gartner has a better V.P. of Naming than they do at Wikibon, but he forecast this notion of Server SAN which really it's not DAS, it's not SAN, it's this new class of accelerated storage that's flash-based, that's NVMe-based, eliminates the horrible storage stack. It's exactly what Pure was talking about. Again, Floyer forecast that in 2009, and if you look at the charts that he produced back then it looks like you see the market like this going shoom, the existing market and the new market just exploding. So Pure, I think, is right on. They're targeting that wide market. Now what they announced today is this notion of their flash array for all workloads, bringing NVMe to virtually their entire portfolio. So they're aiming their platform at the big market. Remember, Pure's ascendancy to a billion really came at the expense of EMC's VMAX and VNX business. They aimed at that and they hit it hard. They positioned flash relative to EMC's either spinning disk or flash-based systems as better, easier, cheaper, et cetera, et cetera, and they won that battle even though they were small. Pure's a billion, EMC at the time was $23, $24 billion, but they gained share very rapidly when you see the numbers. So what they're doing is basically staking a claim, Lisa, saying, "We can point our platform "at the entire $30, $40, $50 billion storage TAM," and their intention, we're going to ask Charlie Giancarlo and company, their aspiration is to really continue to gain share in that marketplace and grow significantly faster than the overall market. >> So they also talked about the data-centric architecture today and gave some great examples of customers. I loved the Domino's Pizza example that they talked about, I think he was here last year, and how they're actually using AI at Domino's to analyze the phone calls using this AI engine to identify accurate order information and get you your pizza as quickly as you want. So not only do we have pizza but we were showered with confetti. Lot of momentum there. What is your opinion of Pure, what they're doing to enable companies to utilize and maximize AI-based applications with this data-centric architecture? >> So Pure started in the what's called block storage, really going after the high-volume, the transaction OLTP business. In the early days of Pure you'd see them at Oracle OpenWorld. That's where the high-volume transactions are taking place. They were the first really, by my recollection, to do file-based flash storage. Back in the day it was you would buy EMC for a block, you'd buy NetApp for file. What Pure did is said, "Okay, let's go after "the biggest market player, EMC, "which we'll gain share there in block, "and then now let's go after NetApp space and file." They were again the first to do that. And now they're extending that to AI. Now AI is a small but growing market, so they want to be the infrastructure for artificial intelligence and machine intelligence. They've struck a partnership with Nvidia, they're using the example of Domino's. It's clearly not a majority of their business today, but they're doing some clever things in marketing, getting ahead of the game. This is Pure's game. Be first, get out in the lead, market it hard, and then let everybody else look like they're following which essentially they are and then claim leadership position. So they are able to punch above their weight class by doing that, and that's what you're seeing with the Domino's example. >> You think they're setting the bar? >> Do I think they're setting the bar? Yeah, in many respects they are because they are forcing these larger incumbents to respond and react because they're in virtually all accounts now. The IT practitioners, they look at the Gartner Magic Quadrant, who's in the upper right, I got to call them in for the RFP. They get a seat at that table. I would say it was interesting hearing Charlie speak today and the rest of the executives. These guys are hardcore storage geeks, and I mean that with all due respect. They love storage. It kind of reminds me of the early days of EMC. They are into this stuff. Their messaging is really toward that storage practitioner, that administrator. They're below the line but those are the guys that are actually making the decisions and affecting transactions. They're touching above the line with AI messages and data growth and things like that, but it's really not a hardcore CIO, CFO, CEO message yet. I think that will come later. They see a big enough market selling to those IT practitioners. So I think they are setting the bar in that IT space, I do. >> One of the things I thought that they did well is kind of position the power of data where, you know people talk about data as fuel. Data's really a business catalyst that needs to be analyzed across multiple areas of a business simultaneously to really be able to extract value. They talked about the gold rush, oh gee, of 1849 and now kind of in this new gold rush enabling IT with the tools. And interestingly they also talked about a survey that they did with the SEE Suite who really believe that analyzing data is going to be key to driving businesses forward, identifying new business models, new products, new services. Conversely, IT concern do we have the right tools to actually be able to evaluate all of these data to extract the value from it? Because if you can't extract the value from the data, is it, it's not useful. >> Yeah, and I think again, I mean to, we give Pure great marketing, and a lot of what they're doing, (laughs) it's technology, it's off-the-shelf technology, it's open source components. So what's their differentiation? Their differentiation is clearly their software. Pure has done a great job of simplifying the experience for the customer, no question, much in the same way that 3PAR did 10 or 15 years ago. They've clearly set the bar on simplicity, so check. The other piece that they've done really well is marketing, and marketing is how companies differentiate (laughs) today. There's no question about it that they've done a great job of that. Now having said that I don't think, Lisa, that storage, I think storage is going to be table stakes for AI. Storage infrastructure for AI is going to have to be there, and they talked about the gold rush of 1849. The guys who made all the money were the guys with the picks and the axes and the shovels supplying them, and that's really what Pure Storage is. They're a infrastructure company. They're providing the pickaxes and the shovels and the basic tools to build on top of that AI infrastructure. But the real challenges of AI are where do I apply and how do I infuse it into applications, how do I get ROI, and then how do I actually have a data model where I can apply machine intelligence and how do I get the skillsets applied to that data? So is Pure playing a fundamental catalyst to that? Yes, in the sense that I need good, fast, reliable, simple-to-use storage so that I don't have to waste a bunch of time provisioning LUNs and doing all kinds of heavy lifting that's nondifferentiated. But I do see that as table stakes in the AI game, but that's the game that Pure has to play. They are an infrastructure company. They're not shy about it, and it's a great business for them because it's a huge market where they're gaining share. >> Partners are also key for them. There's a global partner summit going on. We're going to be speaking, you mentioned Nvidia. We're going to be talking with them. They also announced the AIRI Mini today. I got to get a look at that box. It looks pretty blinged out. (laughing) So we're going to be having conversations with partners from Nvidia, from Cisco as well, and they have a really diverse customer base. We've got Mercedes-AMG Petronas Motorsport Formula One, we've got UCLA on the CIO of UCLA Medicine. So that diversity is really interesting to see how data is being, value, rather, from data is being extracted and applied to solve so many different challenges whether it's hitting a race car around a track at 200 kilometers an hour to being able to extract value out of data to advance health care. They talked about Paige.ai, a new customer that they added in Q1 of FY19 who was able to take analog cancer pathology looking at slides and digitize that to advance cancer research. So a really cool kind of variety of use cases we're going to see on this show today. >> Yeah, I think, so a couple thoughts there. One is this, again I keep coming back to Pure's marketing. When you talk to customers, they cite, as I said before, the simplicity. Pure's also done a really clever thing and not a trivial thing with regard to their Evergreen model. So what that means is you can add capacity and upgrade your software and move to the next generation nondisruptively. Why is this a big deal? For decades you would have to actually shut down the storage array, have planned downtime to do an upgrade. It was a disaster for the business. Oftentimes it turned into a disaster because you couldn't really test or if you didn't test properly and then you tried to go live you would actually lose application availability or worse, you'd lose data. So Pure solved that problem with its Evergreen model and its software capability. So its simplicity, the Evergreen model. Now the reality is typically you don't have to bring in new controllers but you probably should to upgrade the power, so there are some nuances there. If you're mixing and matching different types of devices in terms of protocols there's not really tiering, so there's some nuances there. But again it's both great marketing and it simplifies the customer experience to know that I can go back to serial number 00001 and actually have an Evergreen upgrade is very compelling for customers. And again Pure was one of the first if not the first to put that stake in the ground. Here's how I know it's working, because their competitors all complain about it. When the competitors are complaining, "Wow, Pure Storage, they're just doing X, Y, and Z, "and we can do that too," and it's like, "Hey, look at me, look at me! "I do that too!" And Pure tends to get out in front so that they can point and say, "That's everybody following us, we're the leader." And that resonates with customers. >> It does, in fact. And before we wrap things up here a lot of the customer use cases that I read in prepping for this show all talked about this simplicity, how it simplified the portability, the Evergreen model, to make things much easier to eliminate downtime so that the business can keep running as expected. So we have a variety of use cases, a variety of Puritans on the program today as well as partners who are going to be probably articulating that value. >> You know what, I really didn't address the partner issue. Again, having a platform that's API-friendly, that's simple makes it easier to bring in partners, to integrate into new environments. We heard today about integration with Red Hat. I think they took AIRI. I think Cisco's a part of that partnership. Obviously the Nvidia stuff which was kind of rushed together at the last minute and had got it in before the big Nvidia customer show, but they, again, they were the first. Really made competitors mad. "Oh, we can do that too, it's no big deal." Well, it is a big deal from the standpoint of Pure was first, right? There's value in being first and from a standpoint of brand and mindshare. And if it's easier for you to integrate with partners like Cisco and other go-to-market partners like the backup guys you see, Cohesity and Veeam and guys like Catalogic are here. If it's easier to integrate you're going to have more integration partners and the go-to-market is going to be more facile, and that's where a lot of the friction is today, especially in the channel. >> The last thing I'll end with is we got a rain of confetti on us during the main general session today. The culture of Pure is one that is pervasive. You feel it when you walk into a Pure event. The Puritans are very proud of what they've done, of how they're enabling so many, 4800+ customers globally, to really transform their businesses. And that's one of the things that I think is cool about this event, is not just the plethora of orange everywhere but the value and the pride in the value of what they're delivering to their customers. >> Yeah, I think you're right. It is orange everywhere, they're fun. It's a fun company, and as I say they're alpha geeks when it comes to storage. And they love to be first. They're in your face. The confetti came down and the big firecracker boom when they announced that NVMe was going to be available across the board for zero incremental cost. Normally you would expect it to be a 15 to 20% premium. Again, a first that Pure Storage is laying down the gauntlet. They're setting the bar and saying hey guys, we're going to "give" this value away. You're going to have to respond. Everybody will respond. Again, this is great marketing by Pure because they're >> Shock and awe. going to do it and everybody's going to follow suit and they're going to say, "See, we were first. "Everybody's following, we're the leader. "Buy from us," very smart. >> There's that buy. Another first, this is the first time I have actually been given an outfit to wear by a vendor. I'm the symbol of Prince today. I won't reveal who you are underneath that Superman... >> Okay. >> Exterior. Stick around, you won't want to miss the reveal of the concert tee that Dave is wearing. >> Dave: Very apropos of course for Bill Graham auditorium. >> Exactly, we both said it was very hard to choose which we got a list of to pick from and it was very hard to choose, but I'm happy to represent Prince today. So stick around, Dave and I are going to be here all day talking with Puritans from Charlie Giancarlo, David Hatfield. We've also got partners from Cisco, from Nvidia, and a whole bunch of great customer stories. We're going to be right back with our first guest from the Mercedes-AMG Petronas Motorsport F1 team. I'm Lisa "Prince" Martin, Dave Vellante. We'll be here all day, Pure Storage Accelerate. (bright music)

>> Announcer: Coverage DataWorks Summit Europe 2017 brought to you by Hortonworks. >> Welcome back everyone. Live here in Munich, Germany for theCUBE'S special presentation of Hortonworks Hadoop Summit now called DataWorks 2017. I'm John Furrier, my co-host Dave Vellante, our next guest is Shaun Connolly, Vice President of Corporate Strategy, Chief Strategy Officer. Shaun great to see you again. >> Thanks for having me guys. Always a pleasure. >> Super exciting. Obviously we always pontificating on the status of Hadoop and Hadoop is dead, long live Hadoop, but runs in demise is greatly over-exaggerated, but reality is is that no major shifts in the trends other than the fact that the amplification with AI and machine learning has upleveled the narrative to mainstream around data, big data has been written on on gen one on Hadoop, DevOps, culture, open-source. Starting with Hadoop you guys certainly have been way out in front of all the trends. How you guys have been rolling out the products. But it's now with IoT and AI as that sizzle, the future self driving cars, smart cities, you're starting to really see demand for comprehensive solutions that involve data-centric thinking. Okay, said one. Two, open-source continues to dominate MuleSoft went public, you guys went public years ago, Cloudera filed their S-1. A crop of public companies that are open-source, haven't seen that since Red Hat. >> Exactly. 99 is when Red Hat went public. >> Data-centric, big megatrend with open-source powering it, you couldn't be happier for the stars lining up. >> Yeah, well we definitely placed our bets on that. We went public in 2014 and it's nice to see that graduating class of Taal and MuleSoft, Cloudera coming out. That just I think helps socializes movement that enterprise open-source, whether it's for on-prem or powering cloud solutions pushed out to the edge, and technologies that are relevant in IoT. That's the wave. We had a panel earlier today where Dahl Jeppe from Centric of British Gas, was talking about his ... The digitization of energy and virtual power plant notions. He can't achieve that without open-source powering and fueling that. >> And the thing about it is is just kind of ... For me personally being my age in this generation of computer industry since I was 19, to see the open-source go mainstream the way it is, is even gets better every time, but it really is the thousandth flower bloom strategy. Throwing the seeds out there of innovation. I want to ask you as a strategy question, you guys from a performance standpoint, I would say kind of got hammered in the public market. Cloudera's valuation privately is 4.1 billion, you guys are close to 700 million. Certainly Cloudera's going to get a haircut looks like. The public market is based on the multiples from Dave and I's intro, but there's so much value being created. Where's the value for you guys as you look at the horizon? You're talking about white spaces that are really developing with use cases that are creating value. The practitioners in the field creating value, real value for customers. >> So you covered some of the trends, but I'll translate em into how the customers are deploying. Cloud computing and IoT are somewhat related. One is a centralization, the other is decentralization, so it actually calls for a connected data architecture as we refer to it. We're working with a variety of IoT-related use cases. Coca-Cola, East Japan spoke at Tokyo Summit about beverage replenishment analytics. Getting vending machine analytics from vending machines even on Mount Fuji. And optimizing their flow-through of inventory in just-in-time delivery. That's an IoT-related to run on Azure. It's a cloud-related story and it's a big data analytics story that's actually driving better margins for the business and actually better revenues cuz they're getting the inventory where it needs to be so people can buy it. Those are really interesting use cases that we're seeing being deployed and it's at this convergence of IoT cloud and big data. Ultimately that leads to AI, but I think that's what we're seeing the rise of. >> Can you help us understand that sort of value chain. You've got the edge, you got the cloud, you need something in-between, you're calling it connected data platform. How do you guys participate in that value chain? >> When we went public our primary workhorse platform was Hortonworks Data Platform. We had first class cloud services with Azure HDInsight and Hortonworks Data Cloud for AWS, curated cloud services pay-as-you-go, and Hortonworks DataFlow, I call as our connective tissue, it manages all of your data motion, it's a data logistics platform, it's like FedEx for data delivery. It goes all the way out to the edge. There's a little component called Minify, mini and ify, which does secure intelligent analytics at the edge and transmission. These smart manufacturing lines, you're gathering the data, you're doing analytics on the manufacturing lines, and then you're bringing the historical stuff into the data center where you can do historical analytics across manufacturing lines. Those are the use cases that are connect the data archives-- >> Dave: A subset of that data comes back, right? >> A subset of the data, yep. The key events of that data it may not be full of-- >> 10%, half, 90%? >> It depends if you have operational events that you want to store, sometimes you may want to bring full fidelity of that data so you can do ... As you manufacture stuff and when it got deployed and you're seeing issues in the field, like Western Digital Hard Drives, that failure's in the field, they want that data full fidelity to connect the data architecture and analytics around that data. You need to ... One of the terms I use is in the new world, you need to play it where it lies. If it's out at the edge, you need to play it there. If it makes a stop in the cloud, you need to play it there. If it comes into the data center, you also need to play it there. >> So a couple years ago, you and I were doing a panel at our Big Data NYC event and I used the term "profitless prosperity," I got the hairy eyeball from you, but nonetheless, we talked about you guys as a steward of the industry, you have to invest in open-source projects. And it's expensive. I mean HDFS itself, YARN, Tez, you guys lead a lot of those initiatives. >> Shaun: With the community, yeah, but we-- >> With the community yeah, but you provided contributions and co-leadership let's say. You're there at the front of the pack. How do we project it forward without making forward-looking statements, but how does this industry become a cashflow positive industry? >> Public companies since end of 2014, the markets turned beginning at 2016 towards, prior to that high growth with some losses was palatable, losses were not palatable. That his us, Splunk, Tableau most of the IT sector. That's just the nature of the public markets. As more public open-source, data-driven companies will come in I think it will better educate the market of the value. There's only so much I can do to control the stock price. What I can from a business perspective is hit key measures from a path to profitability. The end of Q4 2016, we hit what we call the just-to-even or breakeven, which is a stepping stone. On our earnings call at the end of 2016 we ended with 185 million in revenue for the year. Only five years into this journey, so that's a hard revenue growth pace and we basically stated in Q3 or Q4 of 17, we will hit operating cashflow neutrality. So we are operating business-- >> John: But you guys also hit a 100 million at record pace too, I believe. >> Yeah, in four years. So revenue is one thing, but operating margins, like if you look at our margins on our subscription business for instance, we've got 84% margin on that. It's a really nice margin business. We can make that better margins, but that's a software margin. >> You know what's ironic, we were talking about Red Hat off camera. Here's Red Hat kicking butt, really hitting all cylinders, three billion dollars in bookings, one would think, okay hey I can maybe project forth some of these open-source companies. Maybe the flip side of this, oh wow we want it now. To your point, the market kind of flipped, but you would think that Red Hat is an indicator of how an open-source model can work. >> By the way Red Hat went public in 99, so it was a different trajectory, like you know I charted their trajectory out. Oracle's trajectory was different. They didn't even in inflation adjusted dollars they didn't hit a 100 million in four years, I think it was seven or eight years or what have you. Salesforce did it in five. So these SaaS models and these subscription models and the cloud services, which is an area that's near and dear to my heart. >> John: Goes faster. >> You get multiple revenue streams across different products. We're a multi-products cloud service company. Not just a single platform. >> So we were actually teasing this out on our-- >> And that's how you grow the business, and that's how Red Hat did it. >> Well I want to get your thoughts on this while we're just kind of ripping live here because Dave and I were talking on our intro segment about the business model and how there's some camouflage out there, at least from my standpoint. One of the main areas that I was kind of pointing at and trying to poke at and want to get your reaction to is in the classic enterprise go-to-market, you have sales force expansive, you guys pay handsomely for that today. Incubating that market, getting the profitability for it is a good thing, but there's also channels, VARs, ISVs, and so on. You guys have an open-source channel that kind of not as a VAR or an ISV, these are entrepreneurs and or businesses themselves. There's got to be a monetization shift there for you guys in the subscription business certainly. When you look at these partners, they're co-developing, they're in open-source, you can almost see the dots connecting. Is this new ecosystem, there's always been an ecosystem, but now that you have kind of a monetization inherently in a pure open distribution model. >> It forces you to collaborate. IBM was on stage talking about our system certified on the Power Systems. Many may look at IBM as competitive, we view them as a partner. Amazon, some may view them as a competitor with us, they've been a great partner in our for AWS. So it forces you to think about how do you collaborate around deeply engineered systems and value and we get great revenue streams that are pulled through that they can sell into the market to their ecosystems. >> How do you vision monetizing the partners? Let's just say Dave and I start this epic idea and we create some connective tissue with your orchestrator called the Data Platform you have and we start making some serious bang. We make a billion dollars. Do you get paid on that if it's open-source? I mean would we be more subscriptions? I'm trying to see how the tide comes in, whose boats float on the rising tide of the innovation in these white spaces. >> Platform thinking is you provide the platform. You provide the platform for 10x value that rides atop that platform. That's how the model works. So if you're riding atop the platform, I expect you and that ecosystem to drive at least 10x above and beyond what I would make as a platform provider in that space. >> So you expect some contributions? >> That's how it works. You need a thousand flowers to be running on the platform. >> You saw that with VMware. They hit 10x and ultimately got to 15 or 16, 17x. >> Shaun: Exactly. >> I think they don't talk about it anymore. I think it's probably trading the other way. >> You know my days at JBoss Red Hat it was somewhere between 15 to 20x. That was the value that was created on top of the platforms. >> What about the ... I want to ask you about the forking of the Hadoop distros. I mean there was a time when everybody was announcing Hadoop distros. John Furrier announced SiliconANGLE was announcing Hadoop distro. So we saw consolidation, and then you guys announced the ODP, then the ODPI initiative, but there seems to be a bit of a forking in Hadoop distros. Is that a fair statement? Unfair? >> I think if you look at how the Linux market played out. You have clearly Red Hat, you had Conicho Ubuntu, you had SUSE. You're always going to have curated platforms for different purposes. We have a strong opinion and a strong focus in the area of IoT, fast analytic data from the edge, and a centralized platform with HDP in the cloud and on-prem. Others in the market Cloudera is running sort of a different play where they're curating different elements and investing in different elements. Doesn't make either one bad or good, we are just going after the markets slightly differently. The other point I'll make there is in 2014 if you looked at the then chart diagrams, there was a lot of overlap. Now if you draw the areas of focus, there's a lot of white space that we're going after that they aren't going after, and they're going after other places and other new vendors are going after others. With the market dynamics of IoT, cloud and AI, you're going to see folks chase the market opportunities. >> Is that dispersity not a problem for customers now or is it challenging? >> There has to be a core level of interoperability and that's one of the reasons why we're collaborating with folks in the ODPI, as an example. There's still when it comes to some of the core components, there has to be a level of predictability, because if you're an ISV riding atop, you're slowed down by death by infinite certification and choices. So ultimately it has to come down to just a much more sane approach to what you can rely on. >> When you guys announced ODP, then ODPI, the extension, Mike Olson wrote a blog saying it's not necessary, people came out against it. Now we're three years in looking back. Was he right or not? >> I think ODPI take away this year, there's more than we can do above and beyond the Hadoop platform. It's expanded to include SQL and other things recently, so there's been some movement on this spec, but frankly you talk to John Mertic at ODPI, you talk to SAS and others, I think we want to be a bit more aggressive in the areas that we go after and try and drive there from a standardization perspective. >> We had Wei Wang on earlier-- >> Shaun: There's more we can do and there's more we should do. >> We had Wei on with Microsoft at our Big Data SV event a couple weeks ago. Talk about the Microsoft relationship with you guys. It seems to be doing very well. Comments on that. >> Microsoft was one of the two companies we chose to partner with early on, so and 2011, 2012 Microsoft and Teradata were the two. Microsoft was how do I democratize and make this technology easy for people. That's manifest itself as Azure Cloud Service, Azure HDInsight-- >> Which is growing like crazy. >> Which is globally deployed and we just had another update. It's fundamentally changed our engineering and delivering model. This latest release was a cloud first delivery model, so one of the things that we're proud of is the interactive SQL and the LLAP technology that's in HDP, that went out through Azure HDInsight what works data cloud first. Then it certified in HDP 2.6 and it went power at the same time. It's that cadence of delivery and cloud first delivery model. We couldn't do it without a partnership with Microsoft. I think we've really learned what it takes-- >> If you look at Microsoft at that time. I remember interviewing you on theCUBE. Microsoft was trading something like $26 a share at that time, around their low point. Now the stock is performing really well. Stockinnetel very cloud oriented-- >> Shaun: They're very open-source. >> They're very open-source and friendly they've been donating a lot to the OCP, to the data center piece. Extremely different Microsoft, so you slipped into that beautiful spot, reacted on that growth. >> I think as one of the stalwarts of enterprise software providers, I think they've done a really great job of bending the curve towards cloud and still having a mixed portfolio, but in sending a field, and sending a channel, and selling cloud and growing that revenue stream, that's nontrivial, that's hard. >> They know the enterprise sales motions too. I want to ask you how that's going over all within Hortonworks. What are some of the conversations that you're involved in with customers today? Again we were saying in our opening segment, it's on YouTube if you're not watching, but the customers is the forcing function right now. They're really putting the pressure one the suppliers, you're one of them, to get tight, reduce friction, lower costs of ownership, get into the cloud, flywheel. And so you see a lot-- >> I'll throw in another aspect some of the more late majority adopters traditionally, over and over right here by 2025 they want to power down the data center and have more things running in the public cloud, if not most everything. That's another eight years or what have you, so it's still a journey, but this journey to making that an imperative because of the operational, because of the agility, because of better predictability, ease of use. That's fundamental. >> As you get into the connected tissue, I love that example, with Kubernetes containers, you've got developers, a big open-source participant and you got all the stuff you have, you just start to see some coalescing around the cloud native. How do you guys look at that conversation? >> I view container platforms, whether they're container services that are running one on cloud or what have you, as the new lightweight rail that everything will ride atop. The cloud currently plays a key role in that, I think that's going to be the defacto way. In particularly if you go cloud first models, particularly for delivery. You need that packaging notion and you need the agility of updates that that's going to provide. I think Red Hat as a partner has been doing great things on hardening that, making it secure. There's others in the ecosystem as well as the cloud providers. All three cloud providers actually are investing in it. >> John: So it's good for your business? >> It removes friction of deployment ... And I ride atop that new rail. It can't get here soon enough from my perspective. >> So I want to ask about clouds. You were talking about the Microsoft shift, personally I think Microsoft realized holy cow, we could actaully make a lot of money if we're selling hardware services. We can make more money if we're selling the full stack. It was sort of an epiphany and so Amazon seems to be doing the same thing. You mentioned earlier you know Amazon is a great partner, even though a lot of people look at them as a competitor, it seems like Amazon, Azure etc., they're building out their own big data stack and offering it as a service. People say that's a threat to you guys, is it a threat or is it a tailwind, is it it is what it is? >> This is why I bring up industry-wide we always have waves of centralization, decentralization. They're playing out simultaneously right now with cloud and IoT. The fact of the matter is that you're going to have multiple clouds on-prem data and data at the edge. That's the problem I am looking to facilitate and solve. I don't view them as competitors, I view them as partners because we need to collaborate because there's a value chain of the flow of the data and some of it's going to be running through and on those platforms. >> The cloud's not going to solve the edge problem. Too expensive. It's just physics. >> So I think that's where things need to go. I think that's why we talk about this notion of connected data. I don't talk hybrid cloud computing, that's for compute. I talk about how do you connect to your data, how do you know where your data is and are you getting the right value out of the data by playing it where it lies. >> I think IoT has been a great sweet trend for the big data industry. It really accelerates the value proposition of the cloud too because now you have a connected network, you can have your cake and eat it too. Central and distributed. >> There's different dynamics in the US versus Europe, as an example. US definitely we're seeing a cloud adoption that's independent of IoT. Here in Europe, I would argue the smart mobility initiatives, the smart manufacturing initiatives, and the connected grid initiatives are bringing cloud in, so it's IoT and cloud and that's opening up the cloud opportunity here. >> Interesting. So on a prospects for Hortonworks cashflow positive Q4 you guys have made a public statement, any other thoughts you want to share. >> Just continue to grow the business, focus on these customer use cases, get them to talk about them at things like DataWorks Summit, and then the more the merrier, the more data-oriented open-source driven companies that can graduate in the public markets, I think is awesome. I think it will just help the industry. >> Operating in the open, with full transparency-- >> Shaun: On the business and the code. (laughter) >> Welcome to the party baby. This is theCUBE here at DataWorks 2017 in Munich, Germany. Live coverage, I'm John Furrier with Dave Vellante. Stay with us. More great coverage coming after this short break. (upbeat music)

>> Announcer: Live from Silicon Valley, it's theCube, covering Mobile World Congress 2017, brought to you by Intel. >> Okay, welcome back, everyone. Here, live, in Palo Alto, California, the Silicon Angle Studio for the Silicon Valley coverage of Mobile World Congress 2017. I'm John Furrier. We're in theCube. We're here with Cube alumni and one of our favorite guests, Alan Cohen, the Chief Commercial Officer of Illumio, hot security startup, coming in to share his commentary on Mobile World Congress. Alan's a veteran in the industry. Great to have you. Been in the Silicon Valley Friday Show a few weeks ago. Great to see you. >> Thrilled to be back. Beautiful environment. You know, party. >> It was great to see you on the Silicon Valley Friday Show because after our segment the New York Times ran that story Friedman had that the cross where they took our content. >> We're going to Freeport next. >> Exactly. (laughing) And great content, we're serving it up. So I want to say thank you, it was great coverage. Thanks to the New York Times for picking up our content, taking it to the next level. Always great to have a conversation. You've got a good way to put the finger on the pulse. Mobile World Congress, two days of coverage for us. I'll just give you a quick Reader's Digest summary of what we're seeing. It's a bipolar show. It's a device show and a telco trying-to-figure-things-out show. Then in the middle is a lot of money to be had by whoever can help sort out the counseling of the telco business. Intel certainly is a big player in that with 5G. And there's a lot of under the covers stuff. SDN, NFV, new networks and new paradigms of how to configure these architectures. Not much mention of security, but that's essentially what's going on. You've got everyone's working out the devices, the new LG, the Yahweh, all this stuff's going on. Then you get the telcos well speeds and feeds and build out and business models. So what's your assessment? >> I've been to the Mobile World Congress 10 times. We never talked about this, but I actually worked the cellular carrier in the 90s. To me the show is the same every year. It's drones, clones, and phones. That's what people really focus on, right? So the 11,000 versions of the Android phone, even though Apple's still taking 89% of the profit at the industry so it actually only one phone you have to pay attention to on one side. Then more bits, less money side of being on the carrier, because what is being an ISP, wireless ISP or a wired ISP. Every year I give you more bits and I make less money. I'm going to make it up in volume. And I keep pouring all this capital into this. So to me, they haven't really yet completely broken out of that paradigm. The key thing is that the mobile network is the primary network. So all the profitability in telco is in the mobile network. Nobody says hey, I'm going to get up and build a wired network and pull some more copper to your house, right? So that is the principle way that people are using it and we have now an entire generation that don't know you can actually plug a phone into a wall or an ethernet connection. I think that's the kind of competitive dynamics that people go with. >> And that's under pressure though, because now the carrier's always in the operating, always controlled the relationship to the user via the contract. Did you buy an iPhone lately? There's no more relationship. You just buy whatever device you want. The subsidy ended ... I'm not talking about subsidy. I'm talking about like I have a contract with AT and T, I can certainly change it to Verizon, so I can certainly swap. But for the most part the carrier views me as a subscriber. Pretty much that's it. They bill me, I'm not really getting anything extra from AT and T. Maybe I'll get some hotspots. But I mean come on, what value? >> You are just our poo. >> Where does it go from here? We had the guys from Datatron on who had an interesting proposition. They had a ton of data. So there really has been this struggle institutionally, as you know, I mean core competency has been provisioning, truck roll, and billing. So what else can they do? What's your thoughts, okay let's change the mental, here's the exercise. We get elected to be the CEO of the biggest telco. >> You're Verizon, I'm A T and T. >> We own the telcos, and what do we do? Do we fire everybody? Do we do what Donald Trump does and just fire everyone and run it the way we want to run it? Or do we build it? What would we do seriously, what would we do if we were telcos and we want to put our business hat on? >> I think you have to kind of deconstruct the value chain of that. So what telcos do is they offer up content, for the most part. These devices, I've had to teach my kids that you can make a call with it. But aside from a call mostly what people do is use some form of internet application. They don't get any other money for the internet application. They don't get any money for hosting it, they don't get any money for managing it. They don't get very much money for making it perform. So to me, the biggest challenge of the telcos is actually Amazon because if you think about it, Amazon is now becoming the supply chain for so much internet delivery content. If the telco wants to be something other than the last mile and the wires connecting that last mile, it takes a lot of wires to build a wireless network, people forget that. They're going to have to start to figure out can I, whether it's cash and data center, can I turn profitable services to the people who are all competing at the edge of that universe and applications. I don't think they really have done that. I mean they are some of the largest data center operators in the world, but they haven't really thought it through. I was in a studio in L.A. a couple weeks ago and it's one of the large national studios. It's an Illumio customer and they've now moved all their content distribution into Amazon. So they don't send the content from their network to the affiliates. They put it in Amazon, and Amazon delivers it. How much longer is it going to before there's actually studio that works out of Amazon? >> Yeah, I mean the head end's dead. This cable is kind of changing. That's the media piece, but also you have all these new use cases, the fantasy autonomous driving cars which you can say it's a data center on wheels, yes I could buy that. Is it going to be uploading data every half mile? Where's the wire? So you have this new construction. Smart cities is another one, smart homes is an echo in there. >> I made my living out of making data centers more secure. But the data center is going to completely evolve. The share perfusion of data that's going to come out of these devices, and a lot of people have talked about the edge architecture, is going to blow up the idea of back hauling it to a centralized server. Process it in a bunch of ways and spit it back out. For me, if I wanted to write a smart or autonomous car management system, let's say I was the city of Palo Alto and I'm responsible for now instead of just the traffic lights, I'm also responsible for how autonomous cars go through Palo Alto, I'm not sending something back to some data center in Virginia for Amazon. I'm going to have to figure out how to process all that data closest to where those cars are. Make intelligent decisions about them while at local, and then send back out instructions. What I think you're going to do is you're going to see a shift from this central model to a much more distributed model and I'm going to have to have mini data centers. So instead of having 10 mega data centers I might have 1,000 mini mega data centers that's going to make all of these things happen. I don't think a lot of people have paid attention to that architectural shift. If you're in the process of, business of selling server networks you're still thinking client-server back haul it into the giant data center next to the nuclear power plant. But it's all going to have to move a lot closer to where something, because I only care about that decision right now with the 50 cars coming down middle field and the streets that feed into it. >> But there's a bigger architecture thing that the Mobile World Congress is trying to point at, which is an ecosystem. Let me take a step back. Is Mobile Congress a relevant show, or is it becoming a CES sideshow, Biz Dev show? I mean Cy Gerli was on yesterday saying look, it's where everyone goes, who's who goes there. It's essentially a Biz Dev show that happens to have a trade show running with it. >> It's the agora, right? The Greek term for marketplace. You go there to do business with people. It's like RSA two weeks ago, right? You guys were up at RSA. It's like is it really fun to walk through 14,000 vendor booths, or is it like everybody who make decisions on buying and selling security stuff happened to be in the same two-square miles of San Francisco. I don't think that part goes away, but I do think ... >> It's a super important part. >> Yeah, but I think the architecture of who plays is going to change. The the question you've got to ask is who's going to be the Amazon of the mobile world and disrupt the network model? The network is now just something glued together with software. I mean years ago they had the same thing, it didn't really work out, that they called the cloud where I would rent my access point in London to people and I'd use their wifi. The stuff that glues it together is always much more important than the infrastructure itself. So if Mobile World Congress can be important there's going to be a track on the people actually glue all of that stuff all together. >> All right, so I've got to get your take on the business conversation, the marketplace that runs there. What are some of the conversations that you could imagine that was happening at Mobile World Congress? I know we're not there, I mean we've been seeing and hearing some of the hallway conversations. Obviously 5G's the big story. What are some of the marketplace hallway conversations or business meetings that are going on in your mind's eye if you had to make a guess on what's happening? >> What are the most important content that people like to use today? Pop quiz, do you know this? >> Yeah, video. >> Video, right? So to me, one of the conversation Netflix was having and Amazon Prime was having because they're not just waiting for you to be in your TV, to consume, right? People are consuming increasing amounts of video content on mobile devices. So I think there's the Hollywood influence or the studio or what is it? The National Association of Programming Executives, NAPE right? What you're doing, if you're a content producer you're looking for eyeballs and people to pay for it. There's nothing more ubiquitous than that piece of glass we're all carrying in front of our nose 17 hours a day. I think that's a big set of business discussions. Your partner was talking about this, is okay, is there just a dramatically different way to build this network? 5G is going to give you the promise, more is a lot of work. The physics are I'm getting a lot more bandwidth. What am I going to do with it? Well people are going to fill it up. >> There's different use cases. There's the mobility and then with dense areas. Then things that are moving at a hundred miles an hour, 50 miles and hour, planes, trains. >> I think there's an element of that. I think there's the internet of things discussion. I still think five years will take the internet whatever things, right? I call the IOWT, right, because it's like nobody's, it's not really about connecting your lightbulb to the network, but there are a lot of things in motion that people want to better manage. >> We just introduced a research agenda this morning with Peter Burroughs, IOT, IOT people. Things and people. >> Have you gone back to the Furrier family and counted up how many IP addresses you have as a family? The Cohen family has 111 IP addresses. >> John: IPV6 for you. (laughing) >> Yeah, we need a gateway man for the network router that comes into the house. But that is actually ... >> We just bought the new Google access points, the ones that have that little mesh instrument. >> But yes, I'm just kidding you. So there are a lot of things. The other thing is that there is the interaction of the mobile, actually I think Google is a great example. If you think about Google produces the wifi at Starbucks and a lot of retail. They're interested in what's going on. Today we think about the mobile network as a mobile network and we think about the broadband fixed network as a different network. And like the interplay between those two, it's like there's a lot more than Foursquare and Facebook. >> Sure fibers of the home is very capital intensive. We knew it would cost us to do a truck roll, the trench, and connect to the home which we did. Overlay wireless, fixed wireless would be fantastic there. >> So you have the overlay and then when I know that you're coming by, right, because the fixed network is now actually a wifi network, I mean it has wires. So you have the mobile network, you have the wifi network, and you have people moving in and out of those environments. I think I'm seeing a lot of companies getting funded. People actually trying to say how do we monetize that experience? This is obviously was Foursquare and those other location guys started years ago. I mean, look at something like Wayce. Wayce went from a GPS app with social interaction to a car sharing, ride sharing going after Uber, this Google company. >> Well we had an NTD Delcomo VC, Chris McCoo, talk about mapping as a huge app for these telcos. >> Mapping is the killer app. Almost everything on your phone local works off a map which, by the way, is paid for by us as taxpayers. The GPS comes from the United States government. It's free. The most powerful utility in mobility is location, and GPS is free. >> All right, final question. Bumper sticker from Mobile World Congress from your perspective this year. Yawner, golf clap, or standing ovation? >> I say golf clap because more bandwidth is good and I think there's an insatiable demand. We're a long way from ending the bandwidth drought, and there is a bandwidth drought. I think the other thing is there aren't camps anymore. I think people will coalesce very quickly on 5G. So good time to be in that business. One hand clap maybe. >> Yeah, not a hole in one. Certainly more golf analogies coming on theCube. Alan Cohen here, Chief Commercial Officer, Illumio. We didn't get to security, but we'll do that next time. I'm John Furrier, I'll be right back with more Mobile World Congress coverage after this short break. (upbeat instrumental music)

from the noise it's the cube covering vmworld 2015 brought to you by vmware and it's ecosystem sponsors and now your host dave vellante we're back at Moscone everybody this is the cube SiliconANGLE Wikibon it's continuous production of vmworld 2015 we're riding the data wave Eric Harris dog is here he's a vice president marketing IBM storage in the Hawaiian shirt great to see you again my friend well Dave thank you very much as I keep telling people it's not about data lakes people have oceans a day to these days yes I oceans a day to dos today that oceans a data now so what's the story get the Hawaiian shirt on what do you got going on across the straw our big thing really is oceans of data so between all the solutions we have from a storage solution set a platform computing environment our joint deal that we do with Cisco with what we call the versus stack and our spectrum family of software now our customers are saying everything's going digital and it doesn't matter whether you're a global enterprise a midsize company or even an SMB with everything going digital it isn't about lakes of data it's about oceans of data so let's start maybe at the versus stack as a hyper converge is sort of taken the world by storm you're seeing vmware's obviously talking about it you got a bunch of startups talking about it when you guys made the move to to sell the the server business the x86 server business to lenovo BNT the acquisition of B&C went with it opened up whole new opportunities for IBM from a partnership standpoint and one of the first guys you went to a cisco so talk about that well we've had a great partnership with Cisco we deliver the versus tak through our mutual channel partners so globally so we have channel partners in all of the gos that are selling the versus stack solution we started originally with our v7000 product which allows us to not only provide a strong mid to your offering but because of our integration of our spectrum virtualized actually will virtualize heterogeneous torso over 300 arrays from our competitors can be virtualized giving any data center or cloud deployment single way to replicate single way to snapshot and of course a single way actually my great dinner which is a huge issue obviously in big deployment well and the same volume controller was really the first platform to do that that was the right gold standard and the whole the original you know tier 1 tier two storage sort of was defined by the sand volume controller kept really now you've built those capabilities into an end to the array so we started with our v7000 storwize was the first with a versus tack we announced last week two new versions one hour v nine thousand which incorporates that same value of the sand volume controller but an all-flash array okay that product is been incredibly successful for us we have thousands of customers we have deployed more petabytes than anyone in the industry and more units than anyone in the issue for you know some of those analysts that track the number side of the business we've done more than any pricing it right is what you're telling me we are definitely pricing it right we do north petabytes more minutes and more units than anybody by far but not the most revenue second most revenue so you well we're a fair price for a fair job as opposed to a high price for okay job that's what we believe in delivering more value for the money so we've got that so that opens up heavy virtualized environments heavy cloud environments big data analytics all those applications were all flash high-end Oracle deployments SP Hana configs all those sort of things are ideal same time you brought in the v5000 at the lower entry place of the mid-tier and it's with the UCS mini from Cisco so it gives you a lower entry price and allows a couple things one you can go in department until deployments a big enterprise to you can go into remote office deployments and also of large enterprise but three it allows you to take the value of a converged infrastructure down into smaller customers because it's a lower entry price point it's got all the value of the virtualization engine we have in all of our V family of products that v5 to be seven in the v9 all flash but it's at a much lower price point with a lower cost UCS mini and a lower cost switch infrastructure from from Cisco so it's a great solution for those big offices but again remote and department level and ideal though to move converged infrastructure down into smaller companies so so cisco has been incredibly successful with that space when Cisco first came out I a misunderstood I said how they going to fall flat in their face and servers and I was totally wrong about that because I didn't understand that they were trying to change the game what's it like partnering with those guys and how is it added value to your business well it's been very strong for us one they've got an excellent channel two they have a great direct sales model as does IBM three we've been partnering them for ages and ages and ages in fact in the 90s we sold a bunch of our networking technology to Cisco and is now deployed by Cisco so some of the networking technology at Cisco puts out there to the to their end users to their channel partners into you know their big telcos that actually came from IBM when we sold our networking division to Cisco in the mid-90s so strong partnership ever since then so let's talk more about the portfolio particularly i'm sickly interested in the whole TSM vs TSM came over to the storage group which thrilled me i think there was a great move by IBM to do that whoever made that decision smart move how has that affected having that storage software capability embedded into the storage business how has that affected your ability to go to market well it's been great so that's our spectrum family there are six elements to that spectrum protect which used to be TSM spectrum control which used to be the tsc product spectrum virtualized which is a software version of the sand volume controller so you can get as a software-only solution spectrum archive spectrum accelerate which is a scale-out block solution think of it as a software version of our XIV platform but software only and spectrum scale which gives incredible scale-out nas capability in fact spectrum scale has a number of customers in the enterprise side not in the HPC market but in global enterprises over 100 petabytes and we even have one customer that has one exabyte in production under spectrum scale exabyte one exabyte in production and not an hpc customer or not not one of the big universities not one of the think tanks but a commercial large global fortune 500 company we an exabyte with spectrum scale so so talk a little bit more about the strategy I think people all times misunderstand IBM's approach they say okay IBM getting out of the hardware business which they think Inferno must get another storage business you're not get out of the storage business obviously they hired hogging store oh so talk more about the strategy and how you're you know pursuing that yeah well I'd say a couple things so first of all our commitment to storage is very strong we're investing a billion in all flash technology and a billion in spectrum software in addition to our normal engineering development for our store wise family and our other members of our products that we've already had so a billion extra in flash and a billion extra in our software family in addition to that we've got a method of consumption that we're looking at so some end users want a full storage solution our ds8000 our flash systems are storwize some customers want to move to the software-defined storage and in several cases such as XIV software only spectrum virtualize okay we've got a number of different ways that you can consume the product and then lastly in several of the products such as spectrum scale spectrum accelerate and a lite version of spectrum control that we call spectrum control storage insights available through a cloud consumption model so if the customer wants a comprehensive solution we have it if the customer wants software-defined storage we have it if the customer wants integrated infrastructure with our vs stack we have it and if the customer wants a cloud storage model of consumption we have that too and quite honestly we think in bigger accounts they may have multiple consumption models for example core data center might go for a full storage solution but guess what the cloud solutions would be ideal for a remote or branch office so talk to me more about the cloud you're talking about the SoftLayer we here we go to the IBM shows you a soft layer of bluemix you know so a lot of money or the devops crowd what's going on bactrim accelerate spectrum scale and spectrum control are all available as a soft layer offering they are not targeting test and Dev they are not targeting you know just the bluemix out these are targeting core data center they could be testing dev or they could be remote office branch office opportunities for large enterprises that want to spend a full storage solution and spend that money on the core data center but for the remote office have spectrum scale delivered over softlayer an ideal solution and various consumption models which ever fits their need so David flora just wrote a piece on Wikibon calm of talking about latency and capacity storage at a very high level sort of segmenting the market those ways it's sort of sizing it up and projecting some of the trends and obviously latency storage he's thinking you know more flash oriented capacity storage more more disk spinning disk and tape is that a reasonable way to look at the business and how does it apply to your portfolio so we do think that's a reasonable way to look at it you have if you will a performance segment and a capacity segment depending the number of things that people need to really look at when they buy storage first of all I'm a storage guy for 30 years no one cares about storage it's all about the data it's all about the data that your storage optimizes it's about the workload the activation the use case for me I do too but unfortunately almost every time you know see how it's going to say almost every CIO is a software guy so it's how does the storage optimize my software environment and that's what's critical to them so we see certain applications that are very performance exit certain SLA s they need to meet we have some that are medium sensitive and we have some that of course are very capacity oriented which is our spectrum scale one exabyte with a single customer now that's capacity that's an ocean of data but we also have solutions we're able to put it together so for example in a lot of data analytics workloads that would run in spectrum scale we actually sell a lot of our all flash flash systems use the flash to ingest the data use flash to manage the metadata use the flash to run the search engine in a big giant config such as that and when you're running an analytics workload you run the analytics workload on that flash yet you're really doing a very large deployment hundreds of petabytes to an exabyte with our spectrum scale so we see if you will a continuum and the key thing as IBM offers all of the various piece parts to any level of the continuum and in that example I just gave combining high performance and deep high capacity software in a single solution to meet a business I mean IBM is an unbelievable company think about Watson cloud bluemix the analytics business deep deep heavy rd z mainframe so you got all the pieces how is the storage business how can it better leverage those other pieces and and is it or is it is it relevant or is it just just take the storage hill so we see our storage products as integrating with our other so for example we do a lot of deals where they buy a mainframe in our ds8000 sure we offer integrated infrastructure not only with cisco but actually with the power family as well it's called pure power and that has an integrated v7000 with a power server and we're looking at deepening that relationship as well a lot of analytics were lot alex workloads going scale so whether they buy the big insights whether they use in Watson we've got several customers use Watson but by flash systems because it's obviously very compute intensive so they use flash systems to do that so you know we fit in at the same time we have plenty of customers that don't buy anything else from IBM and just buy storage so we are appealing to a very broad audience those that are traditional IBM shops that by a lot of different products from IBM and those that go in fact one of our public references general mills they had not bought anything from any division of IBM for 50 years and one of our channel partners in Minnesota we are able to get in there with our XIV product and now not only do they buy XIV and some spectrum protect for backup but they've actually started to buy some other technology from IBM and for 50 years they bought nothing from IBM from any division so in that case storage led the way so again in certain accounts we're in there with the ds8000 and Z or were in there with Watson and flash systems and other accounts were pioneering and in some cases we're the only product they buy they don't buy from IBM we will meet whichever need they have now in periods in the last I mean it's been Evan flow in the storage business for IBM periods the last decade IBM deep rd but the products couldn't seem to go to market now you shared with me under under NDA so we can't talk about it in detail but shared with me the roadmap and and the product roadmap is accelerating from release maybe it's just my impression from what I'm used to should we expect to see a much more you know steady cadence of product delivery from IBM going forward absolutely so keeping in our spirit of oceans we ride the wave we don't fight the way and in today's era in any era of high-tech not just in store it doesn't matter whether storage whether its servers whether it's web to know whatever it is it's all about innovation and doing it quickly so we're going to ride that wave of innovation we're going to have a regular cadence of releases we released four different members of spectrum plus two verses stocks and next quarter you'll see five really five major product releases in one quarter and then in q1 you're going to see another three so we're making sure that as this trajectory of innovation hits all of high tech in all segments that IBM storage is not going to be left behind and we're going to continue to innovate on an accelerated pace that pace is is really important you know IBM again spends a lot of money on R&D it's key to get that product into the pipeline let's talk about vmware and vmworld obviously we're here at vmworld so on vmware very important constituency a lot of customers you got a you got to talk to vmware if you want to be in the data center today what is your strategy around vmware specifically but also generally as it relates to multi cloud environments whether it's your own cloud or other clouds OpenStack or what if you could talk about those so let's take virtualization first so we support a number of different hypervisors we support VMware extensively we support hyper-v we support kvm we support ovm we support open initiatives like OpenStack cinder we support Hadoop we have Hadoop connectors in many of our products so whether it's a cloud deployment or a virtual deployment we want to make sure we support everybody for example spectrum protect was announced last week with support for softlayer as a target device basically a tier well guess what in 1h we're going to support amazon and as you're not just softlayer so again we want to make sure we support everything with VMware specifically for the first time ever VMware has invited IBM storage on stave at three questions iBM has done things in the server world in the past but we have never ever ever been invited by VMware to their technical sessions in fact when is it five o'clock today it's called Project capstone which they publicly announced last week and it's about deploying Oracle environments in VMware virtualization it's a partnership with VMware with IBM flash systems all flash and with HP superdome servers and that's going to be on stage at five o'clock today here at moscone center awesome so we're starting to see a tighter relationship with with VMware building out the portfolio what do you say to the customer says yeah I hear you but vmware's doing all this sort of interesting stuff around things like v san what do you what do you tell a customer you know what about that so we see the San as it you know in this era of behemoths everyone is your partner everyone is your competitor but we work with Intel all the time other divisions of IBM think Intel's a major competitor some of our server division work with some of our storage competitors so we think you know we will work with everyone and while we work with VMware a number of angles so if he sounds a little bit of a competitor that's fine and we see an open space for all of the solutions in the market today we got to leave it there the last question so take us through sort of your objectives for IBM storage over the you know near and midterm what do you what should we be well so our big thing is to make sure we keep the cadence up there's so much development going on whether that be in software defined and integrated infrastructure in all flash in all the areas that we are going to make sure that we continue to develop in every area we've got the billion dollars in all flash in the billion dollars in software to find we are going to spend it and we're going to bring those products to market that fit the need so that the oceans of data that everyone is dealing with can be handled appropriately cost-effectively and quite honestly that oceans of data it's about the business value of the data not the storage underneath so we're going to make sure that for all those oceans a data we will allow them to drive real business value and make sure that those data oceans are protected meet their SLA s and are always available to their end user base I love it yet the Steve Mills billion-dollar playbook obviously worked in Linux it was well over a billion in analytics business IBM's a leader they're applying it to flash great acquisition of Texas memory systems you become a leader they're now going after the software to find Eric Herzog thanks very much for coming to the cubes great very much we love to have all right everybody will be back with our next guest right after this World we're live from vmworld and Moscone keep right there you