(bright upbeat music) >> Welcome back to theCUBE's coverage of VeeamON 2022. We're here at the Aria in Las Vegas. This is day two, Dave Vallante with David Nicholson. You know with theCUBE, we talked about the cloud a lot and the company that started the cloud, AWS. Jeanna James is here. She's the Global Alliance Manager at AWS and a data protection expert. Great to see you. Thanks for coming on theCUBE again. >> Thanks so much for having me, Dave. It's great to be here in person with everyone. >> Yes, you know, we've done a few events live more than a handful. Thanks a lot to AWS. We've done a number. We did the DC Summits. Of course, re:Invent was huge out here last year. That was right in between the sort of variant Omicron hitting. And it was a great, great show. We thought, okay, now we're back. And of course we're kind of back, but we're here and it's good to have you. So Veeam, AWS, I mean, they certainly embrace the cloud. What's your relationship there? >> Yeah, so Veeam is definitely a strong partner with AWS. And as you know, AWS is really a, you know, we have so many different services, and our customers and our partners are looking at how can I leverage those services and how do I back this up, right? Whether they're running things on premises and they want to put a copy of the data into Amazon S3, Amazon S3 Infrequent Access or Amazon S3 Glacier Deep Archive, all of these different technologies, you know Veeam supports them to get a copy from on-prem into AWS. But then the great thing is, you know, it's nice to have a copy of your data in the cloud but you might want to be able to do something with it once it gets there, right? So Veeam supports things like Amazon EC2 and Amazon EKS and EKS Anywhere. So those customers can actually recover their data directly into Amazon EC2 and EKS Anywhere. >> So we, of course, talked a lot about ransomware and that's important in that context of what you just mentioned. What are you seeing with the customers when you talk to them about ransomware? What are they asking AWS to do? Maybe we could start unpacking that a bit. >> Yeah, ransomware is definitely a huge topic today. We're constantly having that conversation. And, you know, five years ago there was a big malware attack that was called the NotPetya virus. And at that time it was based on Petya which was a ransomware virus, and it was designed to go in and, you know, lock in the data but it also went after the backup data, right? So it hold all of that data hostage so that people couldn't recover. Well, NotPetya was based on that but it was worse because it was the seek and destroy virus. So with the ransomware, you can pay a fee and get your data back. But with this NotPetya, it just went in, it propagated itself. It started installing on servers and laptops, anything it could touch and just deleting everything. And at that time, I actually happened to be in the hospital. So hospitals, all types of companies got hit by this attack. And my father had been rushed to the emergency room. I happened to be there. So I saw live what really was happening. And honestly, these network guys were running around shutting down laptops, taking them away from doctors and nurses, shutting off desktops. Putting like taping on pictures that said, do not turn on, right? And then, the nurses and staff were having to kind of take notes. And it was just, it was a mess, it was bad. >> Putting masks on the laptops essentially. >> Yeah, so just-- >> Disinfecting them or trying to. Wow, unplugging things from the network. >> Yes, because, you know, and that attack really demonstrated why you really need a copy of the data in the cloud or somewhere besides tape, right? So what happened at that time is if you lose 10 servers or something, you might be able to recover from tape, but if you lose a hundred or a thousand servers and all of your laptops, all in hours, literally a matter of hours, that is a big event, it's going to take time to recover. And so, you know, if you put a copy of the backup data in Amazon S3 and you can turn on that S3 Object Lock for immutability, you're able to recover in the cloud. >> So, can we go back to this hospital story? 'Cause that takes us inside the disaster potential. So they shut everything down, basically shut down the network so they could figure out what's going on and then fence it off, I presume. So you got, wow, so what happened? First of all, did they have to go manual, I mean? >> They had to do everything manually. It was really a different experience. >> Going back to the 1970s, I mean. >> It was, and they didn't know really how to do it, right? So they basically had kind of yellow notepads and they would take notes. Well, then let's say the doctor took notes, well, then the nurse couldn't read the notes. And even over the PA, you know, there was an announcement and it was pretty funny. Don't send down lab work request with just the last name. We need to know the first name, the last name, and the date of birth. There are multiple Joneses in this hospital so yeah (giggles). >> This is going to sound weird. But so when I was a kid, when you worked retail, if there was a charge for, you know, let's say $5.74 and, you know, they gave you, you know, amount of money, you would give them, you know, the penny back, count up in your head that's 75, give them a quarter and then give them the change. Today, of course, it works differently. The computer tells you, how much change to give. It's like they didn't know what to do. They didn't know how to do it manually 'cause they never had the manual process. >> That's exactly right. Some of the nurses and doctors had never done it manually. >> Wow, okay, so then technically they have to figure out what happened so that takes some time. However they do that. That's kind of not your job, right? I dunno if you can help with that or not. Maybe Amazon has some tooling to do that, probably does. And then you've got to recover from somewhere, not tape ideally. That's like the last resort. You put it on a Chevy Truck, Chevy Truck Access Method called CTAM, ship it in. That takes days, right? If you're lucky. So what's the ideal recovery. I presume it's a local copy somewhere. >> So the ideal-- >> It's fenced. >> In that particular situation, right? They had to really air gap so they couldn't even recover on those servers and things like that-- >> Because everything was infected on on-prem. >> Because everything was just continuing to propagate. So ideally you would have a copy of your data in AWS and you would turn on Object Lock which is the immutability, very simple check mark in Veeam to enable that. And that then you would be able to kick off your restores in Amazon EC2, and start running your business so. >> Yeah, this ties into the discussion of the ransomware survey where, you know, NotPetya was not seeking to extort money, it was seeking to just simply arrive and destroy. In the ransomware survey, some percentage of clients who paid ransom, never got their data back anyway. >> Oh my. >> So you almost have to go into this treating-- >> Huge percentage. >> Yeah, yeah, yeah. >> Like a third. >> Yeah, when you combine the ones where there was no request for ransom, you know, for any extorted funds, and then the ones where people paid but got nothing back. I know Maersk Line, the shipping company is a well studied example of what happened with NotPetya. And it's kind of chilling because what you describe, people running around shutting down laptops because they're seeing all of their peers' screens go black. >> Yes, that's exactly what's happening. >> And then you're done. So that end point is done at that point. >> So we've seen this, I always say there are these milestones in attacks. I mean, Stuxnet proved what a nation state could do and others learned from that, NotPetya, now SolarWinds. And people are freaking out about that because it's like maybe we haven't seen the last of that 'cause that was highly stealth, not a lot of, you know, Russian language in the malware. They would delete a lot of the malware. So very highly sophisticated island hopping, self forming malware. So who knows what's next? We don't know. And so you're saying the ideal is to have an air gap that's physically separate. maybe you can have one locally as well, we've heard about that too, and then you recover from that. What are you seeing in terms of your customers recovering from that? Is it taking minutes, hours, days? >> So that really de depends on the customers SLAs, right? And so with AWS, we offer multiple tiers of storage classes that provide different SLA recovery times, right? So if you're okay with data taking longer to recovery, you can use something like Amazon S3 Glacier Deep Archive. But if it's mission critical data, you probably want to put it in Amazon S3 and turn on that Object Lock for immutability sake. So nothing can be overwritten or deleted. And that way you can kick off your recoveries directly in AWS. >> One of the demos today that we saw, the recovery was exceedingly fast with a very small data loss so that's obviously a higher level SLA. You got to get what you pay for. A lot of businesses need that. I think it was like, I didn't think it was, they said four minutes data loss which is good. I'm glad they didn't say zero data loss 'cause there's really no such thing. So you've got experience, Jeanna, in the data protection business. How have you seen data protection evolve in the last decade and where do you see it going? Because let's face it, I mean when AWS started, okay, it had S3, 15 years ago, 16 years ago, whatever it was. Now, it's got all these tools as you mentioned. So you've learned, you've innovated along with your customers. You listened to your customers. That's your whole thing, customer obsession. >> That's right. >> What are they telling you? What do you see as the future? >> Definitely, we see more and more containerization. So you'll see with the Kasten by Veeam product, right? The ability to protect Amazon EKS, and Amazon EKS Anywhere, we see customers really want to take advantage of the ability to containerize and not have to do as much management, right? So much of what we call undifferentiated heavy lifting, right? So I think you'll see continued innovation in the area of containerization, you know, serverless computing. Obviously with AWS, we have a lot going on with artificial intelligence and machine learning. And, you know, the backup partners, they really have a unique capability in that they do touch a lot of data, right? So I think in the future, you know, things around artificial intelligence and machine learning and data analytics, all of those things could certainly be very applicable for folks like Veeam. >> Yeah, you know, we give a lot of, we acknowledge that backup is different from recovery but we often fall prey to making the mistake of saying, oh, well your data is available in X number of minutes. Well, that's great. What's it available to? So let's say I have backed up to S3 and it's immutable. By the way my wife keeps calling me and saying she wants mutability for me. (Jeanna laughs) I'm not sure if that's a good thing or not. But now I've got my backup in S3, begs the question, okay, well, now what do I do with it? Well, guess what you mentioned EC2. >> That's right. >> The ability exists to create a restore environment so that not only is the data available but the services are actually online and available-- >> That's right-- >> Which is what you want with EKS and Kasten. >> So if the customer is running, you know, Kubernetes, they're able to recover as well. So yes, definitely, I see more and more services like that where customers are able to recover their environment. It might be more than just a server, right? So things are changing. It's not just one, two, three, it's the whole environment. >> So speaking of the future, one of the last physical theCUBE interviews that Andy Jassy did with us. John Furrier and myself, we were asking about the edge and he had a great quote. He said, "Oh yeah, we look at the data center as just another edge node." I thought that was good classic Andy Jassy depositioning. And so it was brilliant. But nonetheless, we've talked a little bit about the edge. I was interviewing Verizon last week, and they told me they're putting outposts everywhere, like leaning in big time. And I was saying, okay, but outpost, you know, what can you do with outpost today? Oh, you can run RDS. And, you know, there's a few ecosystem partners that support it, and he's like, oh no, we're going to push Amazon. So what are you seeing at the edge in terms of data protection? Are customers giving you any feedback at this point? >> Definitely, so edge is a big deal, right? Because some workloads require that low latency, and things like outpost allow the customers to take advantage of the same API sets that they love in, you know, AWS today, like S3, right? For example. So they're able to deploy an outpost and meet some of those specific guidelines that they might have around compliance or, you know, various regulations, and then have that same consistent operational stance whether they're on-prem or in AWS. So we see that as well as the Snowball devices, you know, they're being really hardened so they can run in areas that don't have connected, you know, interfaces to the internet, right? So you've got them running in like ships or, you know, airplanes, or a field somewhere out in nowhere of this field, right? So lots of interesting things going on there. And then of course with IoT and the internet of things and so many different devices out there, we just see a lot of change in the industry and how data is being collected, how data's being created so a lot of excitement. >> Well, so the partners are key for outposts obviously 'cause you can't do it all yourself. It's almost, okay, Amazon now in a data center or an edge node. It's like me skating. It's like, hmm, I'm kind of out of my element there but I think you're learning, right? So, but partners are key to be able to support that model. >> Yes, definitely our partners are key, Veeam, of course, supports the outpost. They support the Snowball Edge devices. They do a lot. Again, they pay attention to their customers, right? Their customers are moving more and more workloads into AWS. So what do they do? They start to support those workloads, right? Because the customers also want that consistent, like we say, the consistent APIs with AWS. Well, they also want the consistent data protection strategy with Veeam. >> Well, the cloud is expanding. It's no longer just a bunch of remote services somewhere out there in the cloud. It's going to data centers. It's going out to the edge. It's going to local zones. You guys just announced a bunch of new local zones. I'm sure there are a lot of outposts in there, expanding your regions. Super cloud is forming right before our eyes. Jeanna, thanks so much for coming to theCUBE. >> Thank you. It's been great to be here. >> All right, and thank you for watching theCUBE's coverage. This is day two. We're going all day here, myself, Dave Nicholson, cohost. Check out siliconangle.com. For all the news, thecube.net, wikibon.com. We'll be right back right after this short break. (bright upbeat music)

(upbeat music) >> Lately Infinidat has been on a bit of a Super cycle of product announcements. Adding features, capabilities, and innovations to its core platform that are applied across its growing install base. CEO, Phil Bollinger has brought in new management and really emphasized a strong and consistent cadence of product releases, a hallmark of successful storage companies. And one of those new executives is a CMO with a proven product chops, who seems to bring an energy and an acceleration of product output, wherever he lands. Eric Herzog joins us on "theCUBE". Hey, man. Great to see you. Awesome to have you again. >> Dave. Thank you. And of course, for "theCUBE", of course, I had to put on a Hawaiian shirt as always. >> They're back. All right, I love it.(laughs) Watch out for those Hawaiian shirt police, Eric. (both laughing) All right. I want to have you start by. Maybe you can make some comments on the portfolio over the past year. You heard my intro, InfiniBox is the core, the InfiniBox SSA, which announced last year. InfiniGuard you made some substantial updates in February of this year. Real focus on cyber resilience, which we're going to talk about with Infinidat. Give us the overview. >> Sure. Well, what we've got is it started really 11 years ago with the InfiniBox. High end enterprise solution, hybrid oriented really incredible magic fairy dust around the software and all the software technology. So for example, the Neural Cache technology, which has multiple patents on it, allowed the original InfiniBox to outperform probably 85% of the All-Flash Arrays in the industry. And it still does that today. We also of course, had our real, incredible ease-of-use the whole point of the way it was configured and set up from the beginning, which we continued to make sure we do is if you will a set it and forget it model. For example, When you install, you don't create lungs and raid groups and volumes it automatically and autonomously configures. And when you add new solutions, AKA additional applications or additional servers and point it at the InfiniBox. It automatically, again in autonomously, adjust to those new applications learning what it needs to configure everything. So you're not setting cash size and Q depth, or Stripes size, anything you would performance to you don't have to do any of that. So that entire set of software is on the InfiniBox. The InfiniBox SSA II, which we're of course launching today and then inside of the InfiniGuard platform, there's a actually an InfiniBox. So the commonality of snapshots replication, ease of use. All of that is identical across the platform of all-flash array, hybrid array and purpose-built backup secondary storage and no other vendor has that breadth of product that has the same exact software. Some make a similar GUI, but we're talking literally the same exact software. So once you learn it, all three platforms, even if you don't have them, you could easily buy one of the other platforms that you don't have yet. And once you've got it, you already know how to use it. 'Cause you've had one platform to start as an example. So really easy to use from a customer perspective. >> So ever since I've been following the storage business, which has been a long time now, three things that customers want. They want something that is rock solid, dirt cheap and super fast. So performance is something that you guys have always emphasized. I've had some really interesting discussions over the years with Infinidat folks. How do you get performance? If you're using this kind of architecture, it's been quite amazing. But how does this launch extend or affect performance? Why the focus on performance from your standpoint? >> Well, we've done a number of different things to bolster the performance. We've already been industry-leading performance again. The regular InfiniBox outperforms 80, 85% of the All-Flash Arrays. Then, when the announcement of the InfiniBox SSA our first all-flash a year ago, we took that now to the highest demanding workloads and applications in the industry. So what did it add to the super high end Oracle app or SAP or some custom app that someone's created with Mongo or Cassandra. We can absolutely meet the performance between either the InfiniBox or the InfiniBox all-flash with the InfiniBox SSA. However, we've decided to extend the performance even farther. So we added a whole bunch of new CPU cores into our tri part configuration. So we don't have two array controllers like many companies do. We actually have three everything's in threes, which gives us the capability of having our 100% availability guarantee. So we've extended that now we've optimized. We put a additional InfiniBand interconnects between the controllers, we've added the CPU core, we've taken if you will the InfiniBox operating system, Neural Cache and everything else we've had. And what we have done is we have optimized that to take advantage of all those additional cores. This has led us to increase performance in all aspects, IOPS bandwidth and in fact in latency. In latency we now are at 35 mikes of latency. Real world, not a hero number, but real-world on an array. And when you look end to end, if I Mr. Oracle, or SAP sitting in the server and I'll look across that bridge, of course the sand and over to the other building the storage building that entire traversing can be as fast as a 100 microseconds of latency across the entire configuration, not just the storage. >> Yeah. I think that's best in class for an external array. Well, so what's the spectrum you can now hit with the performance ranges. Can you hit all the aspects of the market with the two InfiniBoxes, your original, and then the SSA? >> Yes, even with the original SSA. In fact, we've had one of our end users, who's been first InfiniBox customer, then InfiniBox SSA actually has been running for the last two months. A better version of the SSA II. So they've had a better version and this customer's running high end Oracle rack configurations. So they decided, you know what? We're not going to run storage benchmarks. We're going to run only Oracle benchmarks. And in every benchmark IOPS, latency and bandwidth oriented, we outperformed the next nearest competition. So for example, 57% faster in IOPS, 58% faster in bandwidth and on the latency side using real-world Oracle apps, we were three times better performance on the latency aspect, which of course for a high end high performance workload, that's heavily transactional. Latency is the most important, but when you look across all three of those aspects dramatically outperform. And by the way, that was a beta unit that didn't of course have final code on it yet. So incredible performance angle with the InfiniBox SSA II. >> So I mean you earlier, you were talking about the ease of use. You don't have to provision lungs and all that sort of nonsense, and you've always emphasized ease-of-use. Can you double click on that a little bit? How do you think about that capability? And I'm really interested in why you think it's different from other vendors? >> Well, we make sure that, for example, when you install you don't have to do anything, you have to rack and stack, yes and cable. And of course, point the servers at the storage, but the storage just basically comes up. In fact, we have a customer and it's a public reference that bought a couple units many years ago and they said they were up and going in about two hours. So how many high-end enterprise storage array can be up and going in two hours? Almost I mean, basically nobody about us. So we wanted to make sure that we maintain that when we have customers, one of our big plays, particularly helping with CapEx and OpEx is because we are so performant. We can consolidate, we have a large customer in Europe that took 57 arrays from one of our competitors and consolidate it to five of the original InfiniBox. 57 to 5. They saved about $25 million in capital expense and they're saving about a million and a half a year in operational expense. But the whole point was as they kept adding more and more servers that were connected to those competitive arrays and pointing them at the InfiniBox, there's no performance tuning. Again, that's all ease-of-use, not only saving on operational expense, but obviously as we know, the headcount for storage admins is way down from its peak, which was probably in 2007. Yet every admin is managing what 25 to 50 times the amount of storage between 2007 and 2022. So the reality is the easier it is to use. Not only does of course the CIO love it because both the two of us together probably been storage, doing storage now for close to 80 years would be my guess I've been doing it for 40. You're a little younger. So maybe we're at 75 to 78. Have you ever met a CIO used to be a storage admin ever? >> No. >> And I can't think of one either so guess what? The easier it is to use the CIOs know that they need storage. They don't like it. They're all these days are all software guys. There used to be some mainframe guys in the old days, but they're long gone too. It's all about software. So when you say, not only can we help reduce your CapEx at OpEx, but the operational manpower to run the storage, we can dramatically reduce that because of our ease-of-use that they get and ease-of-use has been a theme on the software side ever since the Mac came out. I mean, Windows used to be a dog. Now it's easy to use and you know, every time the Linux distribution come out, someone's got something that's easier and easier to use. So, the fact that the storage is easy to use, you can turn that directly into, we can help you save on operational manpower and OPEX and CIOs. Again, none of which ever met are storage guys. They love that message. Of course the admins do too 'cause they're managing 25 to 50 times more storage than they had to manage back in 2007. So the easier it is for them at the tactical level, the storage admin, the storage manager, it's a huge deal. And we've made sure we've maintained that as you've added the SSA, as we brought up the InfiniGuard, as we've continue to push new feature function. We always make it easy to use. >> Yeah. Kind of a follow up on that. Just focus on software. I mean, I would think every storage company today, every modern storage company is going to have more software engineers than hardware engineers. And I think Infinidat obviously is no different. You got a strong set of software, it's across the portfolio. It's all included kind of thing. I wonder if you could talk about your software approach and how that is different from your competitors? >> Sure, so we started out 11 years ago when in Infinidat first got started. That was all about commodity hardware. So while some people will use custom this and custom that, yeah and I having worked at two of the biggest storage companies in the world before I came here. Yes, I know it's heavily software, but our percentage of hardware engines, softwares is even less hardware engineering than our competitors have. So we've had that model, which is why this whole what we call the set it and forget it mantra of ease-of-use is critical. We make sure that we've expanded that. For example, we're announcing today, our InfiniOps focus and Infini Ops all software allows us to do AIOps both inside of our storage system with our InfiniVerse and InfiniMetrics packages. They're easy to use. They come pre-installed and they manage capacity performance. We also now have heavy integration with AI, what I'll call data center, AIOps vendors, Vetana ServiceNow, VMware and others. And in that case, we make sure that we expose all of our information out to those AIOps data center apps so that they can report on the storage level. So we've made sure we do that. We have incredible support for the Ansible framework again, which is not only a software statement, but an ease-of-use statement as well. So for the Ansible framework, which is trying to allow an even simpler methodology for infrastructure deployment in companies. We support that extensively and we added some new features. Some more, if you will, what I'll say are more scripts, but they're not really scripts that Ansible hides all that. And we added more of that, whether that be configuration installations, that a DevOps guy, which of course just had all the storage guys listening to this video, have a heart attack, but the DevOps guy could actually configure storage. And I guess for my storage buddies, they can do it without messing up your storage. And that's what Ansible delivers. So between our AIOps focus and what we're doing with InfiniOps, that extends of course this ease-of-use model that we've had and includes that. And all this again, including we already talked about a little bit cyber resilience Dave, within InfiniSafe. All this is included when you buy it. So we don't piecemeal, which is you get this and then we try to upcharge you for that. We have the incredible pricing that delivers this CapEx and an OpEx. Not just for the array, but for the associated software that goes with it, whether that be Neural Cache, the ease-of-use, the InfiniOps, InfiniSafes. You get all of that package together in the way we deploy from a business now perspective, ease of doing business. You don't cut POS for all kinds of pieces. You cut APO and you just get all the pieces on the one PO when we deliver it. >> I was talking yesterday to a VC and we were chatting about AI And of course, everybody's chasing AI. It's a lot of investments go in there, but the reality is, AI is like containers. It's just getting absorbed into virtually every thing. And of course, last year you guys made a pretty robust splash into AIOps. And then with this launch, you're extending that pretty substantially. Tell us a little bit more about the InfiniOps announcement news. >> So the InfiniOps includes our existing in the box framework InfiniVerse and what we do there, by the way, InfiniVerse has the capability with the telemetry feed. That's how we could able to demo at our demo today and also at our demo for our channel partner pre-briefing. Again a hundred mics of latency across the entire configuration, not just to a hundred mics of latency on storage, which by the way, several of our competitors talk about a hundred mics of latency as their quote hero number. We're talking about a hundred mics of latency from the application through the server, through the SAN and out to the storage. Now that is incredible. But the monitoring for that is part of the InfiniOps packaging, okay. We support again with DevOps with all the integration that we do, make it easy for the DevOps team, such as with Ansible. Making sure for the data center people with our integration, with things like VMware and ServiceNow. The data center people who are obviously often not the storage centric person can also be managing the entire data center. And whether that is conversing with the storage admin on, we need this or that, or whether they're doing it themselves again, all that is part of our InfiniOps framework and we include things like the Ansible support as part of that. So InfiniOps is sort of an overarching theme and then overarching thing extends to AIops inside of the storage system. AIops across the data center and even integration with I'll say something that's not even considered an infrastructure play, but something like Ansible, which is clearly a red hat, software oriented framework that incorporates storage systems and servers or networks in the capability of having DevOps people manage them. And quite honestly have the DevOps people manage them without screwing them up or losing data or losing configuration, which of course the server guys, the network guys and the storage guys hate when the DevOps guys play with it. But that integration with Ansible is part of our InfiniOps strategy. >> Now our shift gears a little bit talk about cyber crime and I mean, it's a topic that we've been on for a long time. I've personally been writing about it now for the last few years. Periodically with my colleagues from ETR, we hit that pretty hard. It's top of mind, and now the house just approved what's called the Better Cybercrime Metrics Act. It was a bipartisan push. I mean, the vote was like 377 to 48 and the Senate approved this bill last year. Once president Biden signs it, it's going to be the law's going to be put into effect and you and many others have been active in this space Infinidat. You announced cyber resilience on your purpose bill backup appliance and secondary storage solution, InfiniGuard with the launch of InfiniSafe. What are you doing for primary storage from InfiniBox around cyber resilience? >> So the goal between the InfiniGuard and secondary storage and the InfiniBox and the InfiniBox SSA II, we're launching it now, but the InfiniSafe for InfiniBox will work on the original InfiniBox. It's a software only thing. So there's no extra hardware needed. So it's a software only play. So if you have an InfiniBox today, when you upgrade to the latest software, you can have the InfiniSafe reference architecture available to you. And the idea is to support the four key legs of the cybersecurity table from a storage perspective. When you look at it from a storage perspective, there's really four key things that the CISO and the CIO look for first is a mutable snapshot technology. An article can't be deleted, right? You can schedule it. You can do all kinds of different things, but the point is you can't get rid of it. Second thing of course, is an air gap. And there's two types of air gap, logical air gap, which is what we provide and physical the main physical air gaping would be either to tape or to course what's left of the optical storage market. But we've got a nice logical air gap and we can even do that logical air gaping remotely. Since most customers often buy for disaster recovery purposes, multiple arrays. We can then put that air gap, not just locally, but we can put the air gap of course remotely, which is a critical differentiator for the InfiniBox a remote logical air gap. Many other players have logical, we're logical local, but we're going remote. And then of course the third aspect is a fenced forensic environment. That fence forensic environment needs to be easily set up. So you can determine a known good copy to a restoration after you've had a cyber incident. And then lastly is rapid recovery. And we really pride ourself on this. When you go to our most recent launch in February of the InfiniGuard within InfiniSafe, we were able to demo live a recovery taking 12 minutes and 12 seconds of 1.5 petabytes of backup data from Veeam. Now that could have been any backup data. Convolt IBM spectrum tech Veritas. We happen to show with Veeam, but in 12 minutes and 12 seconds. Now on the primary storage side, depending on whether you're going to try to recover locally or do it from a remote, but if it's local, we're looking at something that's going to be 1 to 2 minutes recovery, because the way we do our snapshot technology, how we just need to rebuild the metadata tree and boom, you can recover. So that's a real differentiator, but those are four things that a CISO and a CIO look for from a storage vendor is this imutable snapshot capability, the air gaping capability, the fenced environment capability. And of course this near instantaneous recovery, which we have proven out well with the InfiniGuard. And now with the InfiniBox SSA II and our InfiniBox platform, we can make that recovery on primary storage, even faster than what we have been able to show customers with the InfiniGuard on the secondary data sets and backup data sets. >> Yeah. I love the four layer cake. I just want to clarify something on the air gap if I could so you got. You got a local air gap. You can do a remote air gap with your physical storage. And then you're saying there's I think, I'm not sure I directly heard that, but then the next layer is going to be tape with the CTA, the Chevy truck access method, right? >> Well, so while we don't actively support tape and go to that there's basically two air gap solutions out there that people talk about either physical, which goes to tape or optical or logical. We do logical air gaping. We don't do air gaping to tape 'cause we don't sell tape. So we make sure that it's a remote logical air gap going to a secondary DR Site. Now, obviously in today's world, no one has a true DR data center anymore, right. All data centers are both active and DR for another site. And because we're so heavily concentrated in the global Fortune 2000, almost all the InfiniBoxes in the field already are set up as in a disaster recovery configuration. So using a remote logical air gap would be is easy for us to do with our InfiniBox SSA II and the whole InfiniBox family. >> And, I get, you guys don't do tape, but when you say remote, so you've got a local air gap, right? But then you also you call a remote logical, but you've got a physical air gap, right? >> Yeah, they would be physically separated, but when you're not going to tape because it's fully removable or optical, then the security analysts consider that type of air gap, a logical air gap, even though it's physically at a remote. >> I understand, you spent a lot of time with the channel as well. I know, and they must be all over this. They must really be climbing on to the whole cyber resiliency. What do you say, do they set up? Like a lot of the guys, doing managed services as well? I'm just curious. Are there separate processes for the air gap piece than there are for the mainstream production environment or is it sort of blended together? How are they approaching that? >> So on the InfiniGuard product line, it's blended together, okay. On the InfiniBox with our InfiniSafe reference architecture, you do need to have an extra server where you create an scuzzy private VLAN and with that private VLAN, you set up your fenced forensic environment. So it's a slightly more complicated. The InfiniGuard is a 100% automated. On the InfiniBox we will be pushing that in the future and we will continue to have releases on InfiniSafe and making more and more automated. But the air gaping and the fence reference now are as a reference architecture configuration. Not with click on a gooey in the InfiniGuard case are original InfiniSafe. All you do is click on some windows and it just goes does. And we're not there yet, but we will be there in the future. But it's such a top of mind topic, as you probably see. Last year, Fortune did a survey of the Fortune 500 CEOs and the number one cited threat at 66% by the way was cybersecurity. So one of the key things store storage vendors do not just us, but all storage vendors is need to convince the CISO that storage is a critical component of a comprehensive cybersecurity strategy. And by having these four things, the rapid recovery, the fenced forensic environment, the air gaping technology and the immutable snapshots. You've got all of the checkbox items that a CISO needs to see to make sure. That said many CISOs still even today stood on real to a comprehensive cybersecurity strategy and that's something that the storage industry in general needs to work on with the security community from a partner perspective. The value is they can sell a full package, so they can go to their end user and say, look, here's what we have for edge protection. Here's what we've got to track the bad guide down once something's happened or to alert you that something's happened by having tools like IBM's, Q Radar and competitive tools to that product line. That can traverse the servers and the software infrastructure, and try to locate malware, ransomware akin to the way all of us have Norton or something like Norton on our laptop that is trolling constantly for viruses. So that's sort of software and then of course storage. And those are the elements that you really need to have an overall cybersecurity strategy. Right now many companies have not realized that storage is critical. When you think about it. When you talk to people in security industry, and I know you do from original insertion intrusion to solution is 287 days. Well guess what if the data sets thereafter, whether it be secondary InfiniGuard or primary within InfiniBox, if they're going to trap those things and they're going to take it. They might have trapped those few data sets at day 50, even though you don't even launch the attack until day 200. So it's a big deal of why storage is so critical and why CISOs and CIOs need to make sure they include it day one. >> It's where the data lives, okay. Eric. Wow.. A lot of topics we discovered. I love the agile sort of cadence. I presume you're not done for the year. Look forward to having you back and thanks so much for coming on today. >> Great. Thanks you, Dave. We of course love being on "theCUBE". Thanks again. And thanks for all the nice things about Infinidat. You've been saying thank you. >> Okay. Yeah, thank you for watching this cube conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

>>Hello, everyone. Welcome to this cube conversation. I'm here in Palo Alto, California. I'm John furrier, host of the cube, and we have a great guest here. Barack Shuster. Who's in Tel-Aviv senior director of chief architect at bridge crew, a part of Palo Alto networks. He was formerly the co-founder of the company, then sold to Palo Alto networks Brock. Thanks for coming on this cube conversation. >>Thanks John. Great to be here. >>So one of the things I love about open source, and you're seeing a lot more of the trend now that talking about, you know, people doing incubators all over the world, having open source and having a builder, people who are starting companies, it's coming more and more, you you're one of them. And you've been part of this security open source cloud infrastructure infrastructure as code going back a while, and you guys had a lot of success. Now, open source infrastructure as code has moved up to the stack, certainly lot going down at the network layer, but developers just want to build security from day one, right? They don't want to have to get into the, the, the waiting game of slowing down their pipelining of code in the CIC D they want to move faster. And this has been one of the core conversations this year is how to make developers more productive and not just a cliche, but actually more productive and not have to wait to implement cloud native. Right. So you're in the middle of it. And you've got you're in, tell us, tell us what you guys are dealing with that, >>Right? Yeah. So I hear these needles working fast, having a large velocity of releases from many of my friends, the SRAs, the DevOps, and the security practitioners in different companies. And the thing that we asked ourselves three years ago was how can we simplify the process and make the security teams an enabler instead of a gatekeeper that blocks the releases? And the thing that we've done, then we understood that we should do is not only doing runtime scanning of the cloud infrastructure and the cloud native clusters, but also shift left the findings and fixings the remediation of security issues to the level of the code. So we started doing infrastructure is good. We Terraform Kubernetes manifests cloud formation, server less, and the list goes on and we created an open source product around it, named checkup, which has an amazing community of hundreds of contributors. Not all of them are Palo Alto employees. Most of them are community users from various companies. And we tried to and succeeded to the democratic side is the creation of policy as code the ability to inspect your infrastructure as code and tell you, Hey, this is the best practice that you should use consider using it before applying a misconfigured S3 bucket into production, or before applying a misconfigured Kubernetes cluster into your production or dev environment. And the goal, >>The goal, >>The goal is to do that from the ID from the moment that you write code and also to inspect your configuration in CGI and CD and in runtime. And also understand that if there is any drift out there and the ability to fix that in the source code, in the blueprint itself. >>So what I hear you saying is really two problems you're solving. One is the organizational policies around how things were done in a environment before all the old way. You know, the security teams do a review, you send a ticket, things are waiting, stop, wait, hurry up and wait kind of thing. And then there's the technical piece of it, right? Is that there's two pieces to that. >>Yeah, I think that one thing is the change of the methodologies. We understood that we should just work differently than what we used to do. Tickets are slow. They have priorities. You have a bottleneck, which is a small team of security practitioners. And honestly, a lot of the work is repetitive and can be democratized into the engineering teams. They should be able to understand, Hey, I wrote the code piece that provision this instance, I am the most suitable person as a developer to fix that piece of code and reapply it to the runtime environment. >>And then it also sets the table for our automation. It sets the table for policies, things that make things more efficient scaling. Cause you mentioned SRS are a big part of this to dev ops and SRE. Those, those folks are, are trying to move as fast as possible at scale, huge scale challenge. How does that impact the scale piece become into here? >>So both themes Esri's and security teams are about a link to deploying application, but new application releases into the production environment. And the thing that you can do is you can inspect all kinds of best practices, not only security, best practices, but also make sure that you have provision concurrencies on your serverless functions or the amount of auto-scaling groups is what you expect it to be. And you can scan all of those things in the level of your code before applying it to production. >>That's awesome. So good, good benefits scales a security team. It sounds like too as well. You could get that policy out there. So great stuff. I want to really quickly ask you about the event. You're hosting code two cloud summit. What are we going to see there? I'm going to host a panel. Of course, I'm looking forward to that as well. You get a lot of experts coming in there. Why are you having this event and what topics will be covered? >>So we wanted to talk on all of the shifts, left movement and all of the changes that have happened in the cloud security market since inception till today. And we brought in great people and great practitioners from both the dev ops side, the chaos engineering and the security practitioners, and everybody are having their opinion on what's the current status state, how things should be implemented in a mature environment and what the future might hold for the code and cloud security markets. The thing that we're going to focus on is all of the supply chain from securing the CCD itself, making sure your actions are not vulnerable to a shut injection or making sure your version control system are configured correctly with single sign-on MFA and having branch protection rules, but also open source security like SCA software composition analysis infrastructure as code security. Obviously Ron thinks security drifts and Kubernetes security. So we're going to talk on all of those different aspects and how each and every team is mitigating. The different risks that come with. >>You know, one of the things that you bring up when you hear you talking is that's the range of, of infrastructure as code. How has infrastructure as code changed? Cause you're, you know, there's dev ops and SRS now application developers, you still have to have programmable infrastructure. I mean, if infrastructure code is real realize up and down the stack, all aspects need to be programmable, which means you got to have the data, you got to have the ability to automate. How would you summarize kind of the state of infrastructure as code? >>So a few years ago, we started with physical servers where you carried the infrastructure on our back. I, I mounted them on the rack myself a few years ago and connected all of the different cables then came the revolution of BMS. We didn't do that anymore. We had one beefy appliance and we had 60 virtual servers running on one appliance. So we didn't have to carry new servers every time into the data center then came the cloud and made everything API first. And they bill and enabled us to write the best scripts to provision those resources. But it was not enough because he wanted to have a reproducible environment. The is written either in declarative language like Terraform or CloudFormation or imperative like CDK or polluted, but having a consistent way to deploy your application to multiple environments. And the stage after that is having some kind of a service catalog that will allow application developer to get the new releases up and running. >>And the way that it has evolved mass adoption of infrastructure as code is already happening. But that introduces the ability for velocity in deployment, but also new kinds of risks that we haven't thought about before as security practitioners, for example, you should vet all of the open source Terraform modules that you're using because you might have a leakage. Our form has a lot of access to secrets in your environment. And the state really contains sensitive objects like passwords. The other thing that has changed is we today we rely a lot on cloud infrastructure and on the past year we've seen the law for shell attack, for example, and also cloud providers have disclosed that they were vulnerable to log for shell attack. So we understand today that when we talk about cloud security, it's not only about the infrastructure itself, but it's also about is the infrastructure that we're using is using an open source package that is vulnerable. Are we using an open source package that is vulnerable, is our development pipeline is configured and the list goes on. So it's really a new approach of analyzing the entire software bill of material also called Asbell and understanding the different risks there. >>You know, I think this is a really great point and great insight because new opera, new solutions for new problems are new opportunities, right? So open source growth has been phenomenal. And you mentioned some of those Terraform and one of the projects and you started one checkoff, they're all good, but there's some holes in there and it's open source, it's free, everyone's building on it. So, you know, you have, and that's what it's for. And I think now is open source goes to the next level again, another generational inflection point it's it's, there's more contributors there's companies are involved. People are using it more. It becomes a really strong integration opportunity. So, so it's all free and it's how you use it. So this is a new kind of extension of how open source is used. And if you can factor in some of the things like, like threat vectors, you have to know the code. >>So there's no way to know it all. So you guys are scanning it doing things, but it's also huge system. It's not just one piece of code. You talking about cloud is becoming an operating system. It's a distributed computing environment, so whole new area of problem space to solve. So I love that. Love that piece. Where are you guys at on this now? How do you feel in terms of where you are in the progress bar of the solution? Because the supply chain is usually a hardware concept. People can relate to, but when you bring in software, how you source software is like sourcing a chip or, or a piece of hardware, you got to watch where it came from and you gotta track track that. So, or scan it and validate it, right? So these are new, new things. Where are we with? >>So you're, you're you're right. We have a lot of moving parts. And really the supply chain terms of came from the automobile industry. You have a car, you have an engine engine might be created by a different vendor. You have the wheels, they might be created by a different vendor. So when you buy your next Chevy or Ford, you might have a wheels from continental or other than the first. And actually software is very similar. When we build software, we host it on a cloud provider like AWS, GCP, Azure, not on our own infrastructure anymore. And when we're building software, we're using open-source packages that are maintained in the other half of the war. And we don't always know in person, the people who've created that piece. And we do not have a vetting process, even a human vetting process on these, everything that we've created was really made by us or by a trusted source. >>And this is where we come in. We help you empower you, the engineer, we tools to analyze all of the dependency tree of your software, bill of materials. We will scan your infrastructure code, your application packages that you're using from package managers like NPM or PI. And we scan those open source dependencies. We would verify that your CIC is secure. Your version control system is secure. And the thing that we will always focus on is making a fixed accessible to you. So let's say that you're using a misconfigured backup. We have a bot that will fix the code for you. And let's say that you have a, a vulnerable open-source package and it was fixed in a later version. We will bump the version for you to make your code secure. And we will also have the same process on your run time environment. So we will understand that your environment is secure from code to cloud, or if there are any three out there that your engineering team should look at, >>That's a great service. And I think this is cutting edge from a technology perspective. What's what are some of the new cloud native technologies that you see in emerging fast, that's getting traction and ultimately having a product market fit in, in this area because I've seen Cooper. And you mentioned Kubernetes, that's one of the areas that have a lot more work to do or being worked on now that customers are paying attention to. >>Yeah, so definitely Kubernetes is, has started in growth companies and now it's existing every fortune 100 companies. So you can find anything, every large growler scale organization and also serverless functions are, are getting into a higher adoption rate. I think that the thing that we seeing the most massive adoption off is actually infrastructure as code during COVID. A lot of organization went through a digital transformation and in that process, they have started to work remotely and have agreed on migrating to a new infrastructure, not the data center, but the cloud provider. So at other teams that were not experienced with those clouds are now getting familiar with it and getting exposed to new capabilities. And with that also new risks. >>Well, great stuff. Great to chat with you. I want to ask you while you're here, you mentioned depth infrastructure as code for the folks that get it right. There's some significant benefits. We don't get it. Right. We know what that looks like. What are some of the benefits that can you share personally, or for the folks watching out there, if you get it for sure. Cause code, right? What does the future look like? What does success look like? What's that path look like when you get it right versus not doing it or getting it wrong? >>I think that every engineer dream is wanting to be impactful, to work fast and learn new things and not to get a PagerDuty on a Friday night. So if you get infrastructure ride, you have a process where everything is declarative and is peer reviewed both by you and automated frameworks like bridge and checkoff. And also you have the ability to understand that, Hey, once I re I read it once, and from that point forward, it's reproducible and it also have a status. So only changes will be applied and it will enable myself and my team to work faster and collaborate in a better way on the cloud infrastructure. Let's say that you'd done doing infrastructure as code. You have one resource change by one team member and another resource change by another team member. And the different dependencies between those resources are getting fragmented and broken. You cannot change your database without your application being aware of that. You cannot change your load Bonser without the obligation being aware of that. So infrastructure skullduggery enables you to do those changes in a, in a mature fashion that will foes Le less outages. >>Yeah. A lot of people getting PagerDuty's on Friday, Saturday, and Sunday, and on the old way, new way, new, you don't want to break up your Friday night after a nice dinner, either rock, do you know? Well, thanks for coming in all the way from Tel-Aviv really appreciate it. I wish you guys, everything the best over there in Delhi, we will see you at the event that's coming up. We're looking forward to the code to cloud summit and all the great insight you guys will have. Thanks for coming on and sharing the story. Looking forward to talking more with you Brock thanks for all the insight on security infrastructures code and all the cool things you're doing at bridge crew. >>Thank you, John. >>Okay. This is the cube conversation here at Palo Alto, California. I'm John furrier hosted the cube. Thanks for watching.

>>We'll go back to the queue. It's continuous coverage of AWS storage day. We're here in Seattle home with the Mariners home, with the Seahawks home of the Seattle storm. If you're a w NBA fan your cloud migration, according to our surveys and the ETR data that we use last year was number two initiative for it. Practitioners behind security. Welcome to this power panel on migration and transfer services. And I'm joined now by Matt Matthews. Who's the general manager of AWS transfer a family of services sitting. Roy is the GM of the snow family. And Randy boudin is the general manager of AWS data sync, gents. Welcome to good to see you. Thank you. So, Matt, you heard my narrative upfront, obviously it's top of mind for it. Pros, what are you seeing in the marketplace? >>Yeah, uh, certainly, um, many customers are currently executing on data migration strategies, uh, to the cloud. And AWS has been a primary choice for cloud storage for 15 years. Right. Um, but we still see many customers are evaluating, um, how to do their cloud migration strategies. And they're looking for, you know, um, uh, understanding what services can help them with those migrations. >>So said, well, why now? I mean, a lot of people might be feeling, you know, you got, you've got a hesitancy of taking a vaccine. What about hesitancy making a move? Maybe the best move is no movable. W why now? Why does it make sense? >>So AWS offers compelling, uh, cost savings to customers. I think with our global footprint that our 11 nines of durability are fully managed services. You're really getting the centralization benefits for the cloud, like all the resiliency and durability. And then besides that you are unlocking the on-prem data center and data store costs as well. So it's like a dual prong cost saving on both ends >>Follow up on that. If I may, I mean, again, the data was very clear cloud migration, top priority F for a lot of reasons, but at the same time migration, as you know, it's almost like a dirty word sometimes in it. So, so where do people even start? I mean, they've got so much data to migrate. How can they even handle >>That? Yeah. I'd recommend, uh, customers look at their cool and cold data. Like if they look at their backups and archives and they have not been used for long, I mean, it doesn't make sense to kind of keep them on prem, look at how you can move those and migrate those first and then slowly work your way up into like warm data and then hot data. >>Okay, great. Uh, so Randy, we know about the snow family of products. Of course, everybody's familiar with that, but what about online data migration? What can you tell us there? What's the, what are customers thinking >>About? Sure. So as you know, for many their journey to the cloud starts with data migration, right? That's right. So if you're, if you're starting that journey with, uh, an offline movement, you look to the snow family of products. If you, if you're looking for online, that's when you turn to data, sync data thinks that online data, movement, service data is it makes it fast and easy to move your data into AWS. The customers >>Figure out which services to use. Do you, how do you advise them on that? Or is it sort of word of mouth, peer to peer? How do they figure it out that that's squint through that? Yeah, >>So it comes down to a combination of things. So first is the amount of available bandwidth that you have, the amount of data that you're looking to move and the timeframe you have in which to do that. Right. So if you have a, high-speed say gigabit, uh, uh, network, uh, you can move data very quickly using data sync. If, if you have a slower network or perhaps you don't want to utilize your existing network for this purpose, then the snow family of products makes a lot of sense. Call said, that's it? Call center. That's >>My answer. Yeah, there you go. Oh, you'll >>Joke. Right. See Tam that's Chevy truck access method. You put it right on there and break it over. How about, you know, Matt, I wonder if we could talk maybe about some, some customer examples, any, any favorites that you see are ones that stand out in various industries? >>Yeah. So one of the things we're seeing is certainly getting your data to the cloud is, is important, but also customers want to migrate their applications to the cloud. And when they, when they do that, they, uh, the many applications still need ongoing data transfers from third parties, from ex partners and customers and, and whatnot. So, great example of this is, uh, FINRA and their partnership with AWS. So a FINRA is the single largest, um, uh, regulatory body for securities in the U S and they take in 335 billion market events per day, over 600,000 of their member brokers, registered brokers. So, uh, they use, um, AWS transfer family, uh, secure file transfers, uh, to get that data in an aggregated in, in S3, so they can, um, analyze it and, and, uh, really kind of, uh, understand that data so they can protect investors. So that's, that's a great example. >>So it's not just seeding the cloud, right? It's the ongoing population of it. How about, I mean, how do you guys see this shaping up the future? We all talk about storage silos. I see this as, you know, the cloud is in some ways a silo Buster. Okay. We've got all this data in the cloud now, but you know, you can not apply machine learning. There are other tooling, so what's the north star here. >>Yeah. It's really the north star of getting, you know, we want to unlock, uh, not only get the data in the cloud, but actually use it to unlock the benefits of the cloud has to offer. Right. That's really what you're getting at, aggregating all that data, uh, and using the power of the cloud to really, um, you know, harness that power to analyze the data. It's >>A big, big challenge that customers have. I mean, you guys are obsessed listening to customers, you know, w what kinds of things do you see in the future? Sid and Randy, maybe, maybe see if you can start, >>Uh, I'll start with the I'll kind of dovetail, on example, a Matthews, uh, I'll talk about a customer join, who moved 3.4 petabytes of data to the cloud joined was a streaming service provider out of Germany. They had prohibitive on-prem costs. They saved 500 K per year by moving to the cloud. And by moving to the cloud, they get much more of the data by being able to fine tune their content to local audiences and be more reactive and quicker, a reaction to business changes. So centralizing in the cloud had its benefits of access, flexibility, agility, and faster innovation, and faster time to market. Anything you'd add, right. >>Yeah, sure. So we have a customer Takara bio they're a biotech company. Uh, they're working with genome sequencing, right? So data rich information coming out of those sequencers, they're collecting and analyzing this data daily and sending it up into AWS for analysis, um, and, uh, by using data sync in order to do that, they've improved their data transfer rate by three times. And they've reduced their, uh, overhead six by 66% in terms of their process. >>Guys get, must be blown away by this. I mean, we've all sort of lived in this, so I'm prem world and you sort of lay it out infrastructure, and then you go onto the next one, but the use cases are so diverse. The industry, examples. Matt will give you the last >>Word here. Yeah, no, w w what are we looking to do? You know, we, we always want to listen to our customers, uh, but you know, collectively our, our services and working across other services, AWS, we really, uh, want to help customers not only move their data in the crowd, but also unlock the power of that data. And really, um, you know, uh, we think there's a big opportunity across their migration and transfer services to help customers choose, choose the right service, uh, based on their, where they are in their cloud migration, uh, and, and all the different things they're dealing with. >>I've said a number of times the next 10 years is not going to be like the last 10 years. It's like the cloud is growing up. You know, it's out of the infancy stage. Maybe it's an adolescent. So I don't really know exactly, but guys, thanks so much for coming to the cube and sharing your insights and information. Appreciate it. And thank you for watching everybody keep it right there. More great content from AWS storage day in Seattle.

>>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.

>>Hello, and welcome. Very excited to see everybody here. DockerCon is going fantastic. Everybody's uh, engaging in the chat. It's awesome to see. My name is Peter McKee. I'm the head of developer relations here at Docker and Taber. Today. We're going to be talking about container first development now and in the future. But before we do that, a couple little housekeeping items, first of all, yes, we are live. So if you're in our session, you can go ahead and chat, ask us questions. We'd love to get all your questions and answer them. Um, if you come to the main page on the website and you do not see the chat, go ahead and click on the blue button and that'll die. Uh, deep dive you into our session and you can interact with the chat there. Okay. Without further ado, let's just jump right into it. Katie, how are you? Welcome. Do you mind telling everybody who you are and a little bit about yourself? >>Absolutely. Hello everyone. My name is Katie and currently I am the eco-system advocate at cloud native computing foundation or CNCF. My responsibility is to lead and represent the end-user community. So these are all the practitioners within the cloud native space that are vendor neutral. So they use cloud native technologies to build their services, but they don't sell it. So this is quite an important characteristic as well. My responsibility is to make sure to close the gap between these practitioners and the project maintainers, to make sure that there is a feedback loop around. Um, I have many roles within the community. I am on the advisory board for KIPP finishes, a sandbox project. I'm working with open UK to make sure that Elton standards are used fairly across data, hardware, and software. And I have been, uh, affiliated way if you'd asked me to make sure that, um, I'm distributing a cloud native fundamental scores to make cloud and they do a few bigger despite everyone. So looking forward to this panel and checking with everyone. >>Awesome. Yeah. Welcome. Glad to have you here. Johanna's how are you? Can you, uh, tell everybody a little bit about yourself and who you are? Yeah, sure. >>So hi everybody. My name is Johannes I'm one of the co-founders at get pot, which in case you don't know is an open-source and container based development platform, which is probably also the reason why you Peter reached out and invited me here. So pleasure to be here, looking forward to the discussion. Um, yeah, though it is already a bit later in Munich. Um, and actually my girlfriend had a remote cocktail class with her colleagues tonight and it took me some stamina to really say no to all the Moscow mules that were prepared just over there in my living room. Oh wow. >>You're way better than me. Yeah. Well welcome. Thanks for joining us. Jerome. How are you? Good to see you. Can you tell everybody who you are and a little bit about yourself? Hi, >>Sure. Yeah, so I'm, I, I used to work at Docker and some, for me would say I'm a container hipster because I was running containers in production before it for hype. Um, I worked at Docker before it was even called Docker. And then since 2018, I'm now a freelancer and doing training and consulting around Docker containers, Kubernetes, all these things. So I used to help folks do stuff with Docker when I was there and now I still have them with containers more generally speaking. So kind of, uh, how do we say same, same team, different company or something like that? Yeah. >>Yeah. Perfect. Yeah. Good to see you. I'm glad you're on. Uh, Jacob, how are you? Good to see you. Thanks for joining us. Good. Yeah. Thanks for having me tell, tell everybody a little bit about yourself who you are. >>Yeah. So, uh, I'm the creator of a tool called mutagen, which is an open source, uh, development tool for doing high performance file synchronization and, uh, network forwarding, uh, to enable remote development. And so I come from like a physics background where I was sort of always doing, uh, remote developments, you know, whether that was on a big central clusters or just like some sort of local machine that was a bit more powerful. And so I, after I graduated, I built this tool called mutagen, uh, for doing remote development. And then to my surprise, people just started using it to use, uh, with Docker containers. And, uh, that's kind of grown into its primary use case now. So I'm, yeah, I've gotten really involved with the Docker community and, uh, talked with a lot of great people and now I'm one of the Docker captains. So I get to talk with even more and, and join these events and yeah, but I'm, I'm kind of focused on doing remote development. Uh, cause I, you know, I like, I like having all my tools available on my local machine, but I also like being able to pull in a little bit more powerful hardware or uh, you know, maybe a software that I can't run locally. And so, uh, that's sort of my interest in, in Docker container. Yeah. Awesome. >>Awesome. We're going to come back to that for sure. But yeah. Thank you again. I really appreciate you all joining me and yeah. So, um, I've been thinking about container first development for a while and you know, what does that actually mean? So maybe, maybe we can define it in our own little way. So I, I just throw it out to the panel. When you think about container first development, what comes to mind? What w what, what are you kind of thinking about? Don't be shy. Go ahead. Jerome. You're never a loss of words >>To me. Like if I go back to the, kind of the first, uh, you know, training engagements we did back at Docker and kind of helping folks, uh, writing Dockerfiles to stop developing in containers. Um, often we were replacing, um, uh, set up with a bunch of Vagrant boxes and another, like the VMs and combinations of local things. And very often they liked it a lot and they were very soon, they wanted to really like develop in containers, like run this microservice. This piece of code is whatever, like run that in containers because that means they didn't have to maintain that thing on their own machine. So that's like five years ago. That's what it meant to me back then. However, today, if you, if you say, okay, you know, developing in containers, um, I'm thinking of course about things like get bought and, uh, I think it's called PR or something like that. >>Like this theme, maybe that thing with the ESCO, that's going to run in a container. And you, you have this vs code thing running in your browser. Well, obviously not in your browser, but in a container that you control from your browser and, and many other things like that, that I, I think that's what we, where we want to go today. Uh, and that's really interesting, um, from all kinds of perspectives, like Chevy pair pairing when we will not next to each other, but actually thousands of miles away, um, or having this little environment that they can put aside and come back to it later, without it having using resource in my machine. Um, I don't know, having this dev service running somewhere in the cloud without needing something like, it's at the rights that are like the, the possibilities are really endless. >>Yeah. Yeah. Perfect. Yeah. I'm, you know, a little while ago I was, I was torn, right. W do I spin up containers? Do I develop inside of my containers? Right. There's foul sinking issues. Um, you know, that we've been working on at Docker for a while, and Jacob is very, very familiar with those, right? Sometimes it, it becomes hard, but, and I, and I love developing in the cloud, but I also have this screaming, you know, fast machine sitting on my desktop that I think I should take advantage of. So I guess another question is, you know, should we be developing inside of containers? Is that a smart thing to do? Uh, I'd love to hear you guys' thoughts around that. >>You know, I think it's one of those things where it's, you know, for me container first development is really about, um, considering containers as sort of a first class citizen in, in terms of your development toolkit, right. I mean, there's not always that silver bullet, that's like the one thing you should use for everything. You know, you shouldn't, you shouldn't use containers if they're not fitting in or adding value to your workflow, but I think there's a lot of scenarios that are like, you know, super on super early on in the development process. Like as soon as you get the server kind of running and working and, you know, you're able to access it, you know, running on your local system. Uh that's I think that's when the value comes in to it to add containers to, you know, what you're doing or to your project. Right. I mean, for me, they're, um, they're more of a orchestrational tool, right? So if I don't have to have six different browser tabs open with like, you know, an API server running at one tab and a web server running in another tab and a database running in another tab, I can just kind of encapsulate those and, and use them as an automation thing. So I think, you know, even if you have a super powerful computer, I think there's still value in, um, using containers as, as a orchestrational mechanism. Yeah. Yeah, >>For sure. I think, I think one of the, one of my original aha moments with Docker was, oh, I can spin up different versions of a database locally and not have to install it and not have to configure it and everything, but, you know, it just ran inside of a container. And that, that was it. Although it's might seem simple to some people that's very, very powerful. Right. So I think being able to spin things up and containers very quickly is one of the super benefits. But yeah, I think, uh, developing in containers is, is hard right now, right. With, um, you know, and how do you do that? Right. Does anybody have any thoughts around, how do you go about that? Right. Should you use a container as just a development environment, so, you know, creating an image and then running it just with your dev tools in it, or do you just, uh, and maybe with an editor all inside of it, and it's just this process, that's almost like a VM. Um, yeah. So I'll just kick it back to the panel. I'd love to hear your thoughts on, you know, how do you set up and configure, uh, containers to develop in any thoughts around that? >>Maybe one step back again, to answer your question, what kind of container first development mean? I think it doesn't mean, um, by default that it has to be in the cloud, right? As you said, um, there are obvious benefits when it comes to the developer experience of containers, such as, I dunno, consistency, we have standardized tools dependencies for the dev side of things, but it also makes their dev environment more similar to all the pipeline that is somehow happening to the right, right. So CIC D all the way to production, it is security, right? Which also somehow comes with standardization. Um, but vulnerability scanning tools like sneak are doing a great job there. And, um, for us, it gets pod. One of the key reasons why we created get pod was literally creating this peace of mind for deaths. So from a developer's point of view, you do not need to take care anymore about all the hassle around setups and things that you will need to install. >>And locally, based on some outdated, REIT me on three operating systems in your company, everybody has something different and leading to these verbs in my machine situations, um, that really slow professional software developers down. Right. Um, back to your point, I mean, with good pod, we obviously have to package everything together in one container because otherwise, exactly the situation happens that you need to have five browser tabs open. So we try and leverage that. And I think a dev environment is not just the editor, right? So a dev environment includes your source code. It includes like a powerful shell. It includes file systems. It includes essentially all the tools you need in order to be productive databases and so on. And, um, yeah, we believe that should be encapsulated, um, um, in a container. >>Yeah. Awesome. Katie, you talked to a lot of end users, right. And you're talking to a lot of developers. What, what's your thoughts around container first development, right? Or, or what's the community out there screaming or screaming. It might be too to, uh, har you know, to, to two grand of the word. Right. But yeah, I love it. I love to hear what your, your thoughts. >>Absolutely. So I think when you're talking about continuing driven development, uh, the first thing that crosses my mind is the awareness of the infrastructure or the platform you're going to run your application on top of, because usually when you develop your application, you'd like to replicate as much as possible the production or even the staging environment to make sure that when you deploy your application, you have us little inconsistencies as possible, but at the same time, you minimize the risk for something to go wrong as well. So when it talking about the, the community, um, again, when you deploy applications and containers and Kubernetes, you have to use, you have awareness about, and probably apply some of the best practices, like introducing liveliness and readiness probes, to make sure that your application can restart in, in case it actually goes down or there's like a you're starving going CPU or something like that. >>So, uh, I think when it comes to deployment and development of an application, the main thing is to actually improve the end developer experience. I think there has been a lot of focus in the community to develop the tool, to actually give you the right tool to run application and production, but that doesn't necessarily, um, go back to how the end developer is actually enabling that application to run into that production system. So I think there has been, uh, this focus for the community identified now, and it's more, more, um, or trying to build momentum on enhancing the developer experience. And we've seen this going through many, uh, where we think production of many tools did what has been one of them, which actually we can have this portable, um, development environment if you choose so, and you can actually replicate them across different teams in different machines, which is actually quite handy. >>But at the same time, we had tools such as local composts has been a great tool to run locally. We have tool such as carefully, which is absolutely great to automatically dynamically upload any changes to how within your code. So I think all of these kinds of tools, they getting more matured. And again, this is going back to again, we need to enhance our developer experience coming back to what is the right way to do so. Um, I think it really depends on the environment you have in production, because there's going to define some of the structures with the tool and you're going to have internally, but at the same time, um, I'd like to say that, uh, it really depends on, on what trucks are developing. Uh, so it's, it's, I would like to personally, I would like to see a bit more diversification in this area because we might have this competitive solutions that is going to push us to towards a new edge. So this is like, what definitely developer experience. If we're talking about development, that's what we need to enhance. And that's what I see the momentum building at the moment. >>Yeah. Yeah. Awesome. Jerome, I saw you shaking your head there in agreement, or maybe not, but what's your thoughts? >>I was, uh, I was just reacting until 82. Uh, it depends thinking that when I, when I do training, that's probably the answer that I gave the most, uh, each time somebody asks, oh, should we do diesel? And I was also looking at some of the questions in the chat about, Hey, the, should we like have a negatory in the, in the container or something like that. And folks can have pretty strong opinions one way or the other, but as a ways, it kind of depends what we do. It also depends of the team that we're working with. Um, you, you could have teams, you know, with like small teams with folks with lots of experience and they all come with their own Feb tools and editorials and plugins. So you know that like you're gonna have PRI iMacs out of my cold dead hands or something like that. >>So of course, if you give them something else, they're going to be extremely unhappy or sad. On the other hand, you can have team with folks who, um, will be less opinionated on that. And even, I don't know, let's say suddenly you start working on some project with maybe a new programming language, or maybe you're targeting some embedded system or whatever, like something really new and different. And you come up with all the tools, even the ADE, the extensions, et cetera, folks will often be extremely happy in that case that you're kind of giving them a Dettol and an ADE, even if that's not what they usually would, uh, would use, um, because it will come with all of the, the, the nice stage, you know, the compression, the, um, the, the, the bigger, the, whatever, all these things. And I think there is also something interesting to do here with development in containers. >>Like, Hey, you're going to start working on this extremely complex target based on whatever. And this is a container that has everything to get started. Okay. Maybe it's not your favorites editor, but it has all the customization and the conserver and whatever. Um, so you can start working right away. And then maybe later you, we want to, you know, do that from the container in a way, and have your own Emacs, atom, sublime, vs code, et cetera, et cetera. Um, but I think it's great for containers here, as well as they reserve or particularly the opportunity. And I think like the, that, that's one thing where I see stuff like get blood being potentially super interesting. Um, it's hard for me to gauge because I confess I was never a huge ID kind of person had some time that gives me this weird feeling, like when I help someone to book some, some code and you know, that like with their super nice IDE and everything is set up, but they feel kind of lost. >>And then at some point I'm like, okay, let's, let's get VI and grep and let's navigate this code base. And that makes me feel a little bit, you know, as this kind of old code for movies where you have the old, like colorful guy who knows going food, but at the end ends up still being obsolete because, um, it's only a going for movies that whole good for masters and the winning right. In real life, we don't have conformance there's anymore mentioned. So, um, but part of me is like, yeah, I like having my old style of editor, but when, when the modern editorial modern ID comes with everything set up and configured, that's just awesome. That's I, um, it's one thing that I'm not very good at sitting up all these little things, but when somebody does it and I can use it, it's, it's just amazing. >>Yeah. Yeah. I agree. I'm I feel the same way too. Right. I like, I like the way I've I have my environment. I like the tools that I use. I like the way they're set up. And, but it's a big issue, right? If you're switching machines, like you said, if you're helping someone else out there, they're not there, your key bindings aren't there, you can't, you can't navigate their system. Right? Yeah. So I think, you know, talking about, uh, dev environments that, that Docker's coming out with, and we're, you know, there's a lot, there, there's a, it's super complex, all these things we're talking about. And I think we're taking the approach of let's do something, uh, well, first, right. And then we can add on to that. Right. Because I think, you know, setting up full, full developed environments is hard, right. Especially in the, the, um, cloud native world nowadays with microservices, do you run them on a repo? >>Do you not have a monitor repo? Maybe that would be interesting to talk about. I think, um, you know, I always start out with the mono repos, right. And you have all your services in there and maybe you're using one Docker file. And then, because that works fine. Cause everything is JavaScript and node. And then you throw a little Python in there and then you throw a little go and now you start breaking things out and then things get too complex there, you know, and you start pulling everything out into different, get repos and now, right. Not everything just fits into these little buckets. Right. So how do you guys think maybe moving forward, how do we attack that night? How do we attack these? Does separate programming languages and environments and kind of bring them all together. You know, we, we, I hesitate, we solve that with compose around about running, right about executing, uh, running your, your containers. But, uh, developing with containers is different than running containers. Right. It's a, it's a different way to think about it. So anyway, sorry, I'm rattling on a little bit, but yeah. Be interesting to look at a more complex, uh, setup right. Of, uh, of, you know, even just 10 microservices that are in different get repos and different languages. Right. Just some thoughts. And, um, I'm not sure we all have this flushed out yet, but I'd love to hear your, your, you guys' thoughts around that. >>Jacob, you, you, you, you look like you're getting ready to jump there. >>I didn't wanna interrupt, but, uh, I mean, I think for me the issue isn't even really like the language boundary or, or, um, you know, a sub repo boundary. I think it's really about, you know, the infrastructure, right? Because you have, you're moving to an era where you have these cloud services, which, you know, some of them like S3, you can, you can mock up locally, uh, or run something locally in a container. But at some point you're going to have like, you know, cloud specific hardware, right? Like you got TPS or something that maybe are forming some critical function in your, in your application. And you just can't really replicate that locally, but you still want to be able to develop against that in some capacity. So, you know, my, my feeling about where it's going to go is you'll end up having parts of your application running locally, but then you also have, uh, you know, containers or some other, uh, element that's sort of cohabitating with, uh, you know, either staging or, or testing or production services that you're, uh, that you're working with. >>So you can actually, um, you know, test against a really or realistic simulation or the actual, uh, surface that you're running against in production. Because I think it's just going to become untenable to keep emulating all of that stuff locally, or to have to like duplicate these, you know, and, you know, I guess you can argue about whether or not it's a good thing that, that everything's moving to these kind of more closed off cloud services, but, you know, the reality of situation is that's where it's going to go. And there's certain hardware that you're going to want in the cloud, especially if you're doing, you know, machine learning oriented stuff that there's just no way you're going to be able to run locally. Right. I mean, if you're, even if you're in a dev team where you have, um, maybe like a central machine where you've got like 10 or 20 GPU's in it, that's not something that you're going to be able to, to, to replicate locally. And so that's how I kind of see that, um, you know, containers easing that boundary between different application components is actually maybe more about co-location, um, or having different parts of your application run in different locations, on different hardware, you know, maybe someone on your laptop, maybe it's someone, you know, AWS or Azure or somewhere. Yeah. It'd be interesting >>To start seeing those boundaries blur right. Working local and working in the cloud. Um, and you might even, you might not even know where something is exactly is running right until you need to, you know, that's when you really care, but yeah. Uh, Johanas, what's your thoughts around that? I mean, I think we've, we've talked previously of, of, um, you know, hybrid kind of environments. Uh, but yeah. What, what's your thoughts around that? >>Um, so essentially, yeah, I think, I mean, we believe that the lines between cloud and local will also potentially blur, and it's actually not really about that distinction. It's just packaging your dev environment in a way and provisioning your dev environment in a way that you are what we call always ready to coat. So that literally, um, you, you have that for the, you described as, um, peace of mind that you can just start to be creative and start to be productive. And if that is a container potentially running locally and containers are at the moment. I think, you know, the vehicle that we use, um, two weeks ago, or one week ago actually stack blitz announced the web containers. So potentially some things, well, it's run in the browser at some point, but currently, you know, Docker, um, is the standard that enables you to do that. And what we think will happen is that these cloud-based or local, um, dev environments will be what we call a femoral. So it will be similar to CIS, um, that we are using right now. And it doesn't literally matter, um, where they are running at the end. It's just, um, to reduce friction as much as possible and decrease and yeah, yeah. Essentially, um, avoid or the hustle that is currently involved in setting up and also managing dev environments, um, going forward, which really slows down specifically larger teams. >>Yeah. Yeah. Um, I'm going to shift gears a little bit here. We have a question from the audience in chat, uh, and it's, I think it's a little bit two parts, but so far as I can see container first, uh, development, have the challenges of where to get safe images. Um, and I was going to answer it, but let me keep it, let me keep going, where to get safe images and instrumentation, um, and knowing where exactly the problem is happening, how do we provide instrument instrumentation to see exactly where a problem might be happening and why? So I think the gist of it is kind of, of everything is in a container and I'm sitting outside, you know, the general thought around containers is isolation, right. Um, so how do I get views into that? Um, whether debugging or, or, or just general problems going on. I think that's maybe a broader question around the, how you, you know, you have your local hosts and then you're running everything containers, and what's the interplay there. W what's your thoughts there? >>I tend to think that containers are underused interactively. I mean, I think in production, you have this mindset that there's sort of this isolated environment, but it's very, actually simple to drop into a shell inside of a container and use it like you would, you know, your terminal. Um, so if you want to install software that way, you know, through, through an image rather than through like Homebrew or something, uh, you can kind of treat containers in that way and you can get a very, um, you know, direct access to the, to the space in which those are running in. So I think, I think that's maybe the step one is just like getting rid of that mindset, that, that these are all, um, you know, these completely encapsulated environments that you can't interact with because it's actually quite easy to just Docker exec into a container and then use it interactively >>Yeah. A hundred percent. And maybe I'll pass, I'm going to pass this question. You drone, but maybe demystify containers a little bit when I talked about this on the last, uh, panel, um, because we have a question in the, in the chat around, what's the, you know, why, why containers now I have VMs, right? And I think there's a misunderstanding in the industry, uh, about what, what containers are, we think they're fair, packaged stuff. And I think Jacob was hitting on that of what's underneath the hood. So maybe drown, sorry, for a long way to set up a question of what, what, what makes up a container, what is a container >>Is a container? Well, I, I think, um, the sharpest and most accurate and most articulate definition, I was from Alice gold first, and I will probably misquote her, but she said something like containers are a bunch of capsulated processes, maybe running on a cookie on welfare system. I'm not sure about the exact definition, but I'm going to try and, uh, reconstitute that like containers are just processes that run on a Unix machine. And we just happen to put a bunch of, um, red tape or whatever around them so that they are kind of contained. Um, but then the beauty of it is that we can contend them as much, or as little as we want. We can go kind of only in and put some actual VM or something like firecracker around that to give some pretty strong angulation, uh, all we can also kind of decontam theorize some aspects, you know, you can have a container that's actually using the, um, the, um, the network namespace of the host. >>So that gives it an entire, you know, wire speed access to the, to the network of the host. Um, and so to me, that's what really interesting, of course there is all the thing about, oh, containers are lightweight and I can pack more of them and they start fast and the images can be small, yada yada, yada. But to me, um, with my background in infrastructure and building resilient, things like that, but I find really exciting is the ability to, you know, put the slider wherever I need it. Um, the, the, the ability to have these very light containers, all very heavily, very secure, very anything, and even the ability to have containers in containers. Uh, even if that sounds a little bit, a little bit gimmicky at first, like, oh, you know, like you, you did the Mimi, like, oh, I heard you like container. >>So I put Docker when you're on Docker. So you can run container for you, run containers. Um, but that's actually extremely convenient because, um, as soon as you stop building, especially something infrastructure related. So you challenge is how do you test that? Like, when we were doing.cloud, we're like, okay, uh, how do we provision? Um, you know, we've been, if you're Amazon, how do you provision the staging for us installed? How do you provision the whole region, Jen, which is actually staging? It kind of makes things complicated. And the fact that we have that we can have containers within containers. Uh, that's actually pretty powerful. Um, we're also moving to things where we have secure containers in containers now. So that's super interesting, like stuff like a SIS box, for instance. Um, when I saw that, that was really excited because, uh, one of the horrible things I did back in the days as Docker was privileged containers, precisely because we wanted to have Docker in Docker. >>And that was kind of opening Pandora's box. That's the right, uh, with the four, because privileged containers can do literally anything. They can completely wreck up the machine. Um, and so, but at the same time, they give you the ability to run VPNs and run Docker in Docker and all these cool things. You can run VM in containers, and then you can list things. So, um, but so when I saw that you could actually have kind of secure containers within containers, like, okay, there is something really powerful and interesting there. And I think for folks, well, precisely when you want to do development in containers, especially when you move that to the cloud, that kind of stuff becomes a really important and interesting because it's one thing to have my little dev thing on my local machine. It's another thing when I want to move that to a swarm or Kubernetes cluster, and then suddenly even like very quickly, I hit the wall, which is, oh, I need to have containers in my containers. Um, and then having a runtime, like that gets really intense. >>Interesting. Yeah, yeah, yeah. And I, and jumping back a bit, um, yeah, uh, like you said, drum at the, at the base of it, it containers just a, a process with, with some, uh, Abra, pardon me, operating constructs wrapped around it and see groups, namespaces those types of things. But I think it's very important to, for our discussion right. Of, uh, developers really understanding that, that this is just the process, just like a normal process when I spin up my local bash in my term. Uh, and I'm just interacting with that. And a lot of the things we talk about are more for production runtimes for securing containers for isolating them locally. I don't, I don't know. I'll throw the question out to the panel. Is that really relevant to us locally? Right. Do we want to pull out all of those restrictions? What are the benefits of containers for development, right. And maybe that's a soft question, but I'd still love to hear your thoughts. Maybe I'll kick it over to you, Katie, would you, would you kick us off a little bit with that? >>I'll try. Um, so I think when, again, I was actually thinking of the previous answers because maybe, maybe I could do a transition here. So, interesting, interesting about containers, a piece of trivia, um, the secrets and namespaces have been within the Linux kernel since 2008, I think, which just like more than 10 years ago, hover containers become popular in the last years. So I think it's, it's the technology, but it's about the organization adopting this technology. So I think why it got more popular now is because it became the business differentiator organizations started to think, how can I deliver value to my customers as quickly as possible? So I think that there should be this kind of two lane, um, kind of progress is the technology, but it's at the same time organization and cultural now are actually essential for us to develop, uh, our applications locally. >>Again, I think when it's a single application, if you have just one component, maybe it's easier for you to kind of run it locally, have a very simple testing environment. Sufficient is a container necessary, probably not. However, I think it's more important when you're thinking to the bigger picture. When we have an architecture that has myriads of microservices at the basis, when it's something that you have to expose, for example, an API, or you have to consume an API, these are kind of things where you might need to think about a lightweight set up within the containers, only local environment to make sure that you have at least a similar, um, environment or a configuration to make sure that you test some of the expected behavior. Um, I think the, the real kind of test you start from the, the dev cluster will like the dev environment. >>And then like for, for you to go to staging and production, you will get more clear into what exactly that, um, um, configuration should be in the end. However, at the same time, again, it's, it's more about, um, kind of understanding why you continue to see this, the thing, like, I don't say that you definitely need containers at all times, but there are situations when you have like, again, multiple services and you need to replicate them. It's just the place to, to, to work with these kind of, um, setups. So, um, yeah, really depends on what you're trying to develop here. Nothing very specific, unfortunately, but get your product and your requirements are going to define what you're going to work with. >>Yeah, no, I think that's a great answer, right. I think one of the best answers in, in software engineering and engineering in general as well, it depends. Right. It's things are very specific when we start getting down to the details, but yeah, generally speaking, you know, um, I think containers are good for development, but yeah, it depends, right. It really depends. Is it helping you then? Great. If it's hindering you then, okay. Maybe think what's, what's the hindrance, right. And are containers the right solution. I agree. 110% and, >>And everything. I would like absurd this too as well. When we, again, we're talking about the development team and now we have this culture where we have the platform and infrastructure team, and then you have your engineering team separately, especially when the regulations are going to be segregated. So, um, it's quite important to understand that there might be a, uh, a level of up-skilling required. So pushing for someone to use containers, because this is the right way for you to develop your application might be not, uh, might not be the most efficient way to actually develop a product because you need to spend some time to make sure that the, the engineering team has the skills to do so. So I think it's, it's, again, going back to my answers here is like, truly be aware of how you're trying to develop how you actually collaborate and having that awareness of your platform can be quite helpful in developing your, uh, your publication, the more importantly, having less, um, maybe blockers pushing it to a production system. >>Yeah, yeah. A hundred percent. Yeah. The, uh, the cultural issue is, is, um, within the organization, right. Is a very interesting thing. And it, and I would submit that it's very hard from top down, right. Pushing down tools and processes down to the dev team, man, we'll just, we'll just rebel. It usually comes from the bottom up. Right. What's working for us, we're going to do right. And whether we do it in the shadows and don't let it know, or, or we've conformed, right. Yeah. A hundred percent. Um, interesting. I would like to think a little bit in the future, right? Like, let's say, I don't know, two, three years from now, if, if y'all could wave a and I'm from Texas. So I say y'all, uh, if you all could wave a magic wand, what, what, what would that bring about right. What, what would, what would be the best scenario? And, and we just don't have to say containers. Right. But, you know, what's the best development environment and I'm going to kick it over to you, Jacob. Cause I think you hinted at some of that with some hybrid type of stuff, but, uh, yeah. Implies, they need to keep you awake. You're, you're, you're, uh, almost on the other side of the world for me, but yeah, please. >>Um, I think, you know, it's, it's interesting because you have this technology that you've been, that's been brought from production, so it's not, um, necessarily like the right or the normal basis for development. So I think there's going to be some sort of realignment or renormalization in terms of, uh, you know, what the, what the basis and the abstractions that we're using on a daily basis are right. Like images and containers as they exist now are really designed for, um, for production use cases. And, and in terms of like, even even the ergonomics of opening a shell inside a container, I think is something that's, um, you know, not as polished or not as smooth as it could be because they've come from production. And so I think it's important, like not to, not to have people look at, look at the technology as it exists now and say like, okay, this is slightly rough around the edges, or it wasn't designed for this use case and think, oh, there's, you know, there's never any way I could use this for, for my development of workflows. >>I think it's, you know, it's something Docker's exploring now with, uh, with the, uh, dev containers, you know, it's, it's a new, and it's an experimental paradigm and it may not be what the final picture looks like. As, you know, you were saying, there's going to be kind of a baseline and you'll add features to that or iterate on that. Um, but I think that's, what's interesting about it, right? Cause it's, there's not a lot of things as developers that you get to play with that, um, that are sort of the new technology. Like if you're talking about things you're building to ship, you want to kind of use tried and true components that, you know, are gonna, that are going to be reliable. But I think containers are that interesting point where it's like, this is an established technology, but it's also being used in a way now that's completely different than what it was designed for. And, and, you know, as hackers, I think that's kind of an interesting opportunity to play with it, but I think, I think that's, what's going to happen is you're just going to see kind of those production, um, designed, uh, knobs kind of sanded down or redesigned for, for development. So that's kind of where I see it going. >>Yeah. Yeah. And I think that's what I was trying to hint out earlier is like, um, yeah, just because all these things are there, does it actually mean we need them locally? Right. Do they make sense? I, I agree. A hundred percent, uh, anybody else drawn? What are your thoughts around that? And then, and then, uh, I'll probably just ask all of you. I'd love to hear each of your thoughts of the future. >>I had a thought was maybe unrelated, but I was kind of wondering if we would see something on the side of like energy efficiency in some way. Um, and maybe it's just because I've been thinking a lot about like climate change and things like that recently, and trying to reduce like the, uh, the energy use energy use and things like that. Perhaps it's also because I recently got a new laptop, which on paper is super awesome, but in practice, as soon as you try to have like two slack tabs and a zoom call, you know, it's super fast, both for 30 seconds. And after 30 seconds, it blows its thermal budget and it's like slows down to a crawl. And I started to think, Hmm, maybe, you know, like before we, we, we were thinking about, okay, I don't have that much CPU available. So you have to be kind of mindful about that. >>And now I wonder how are we going to get in something similar to that, but where you try to save CPU cycles, not just because you don't have that many CPU cycles, but more because you know, that you can't go super fast for super long when you are on one of these like small laptops or tablets or phones, like you have this demo budget to take into account. And, um, I wonder if, and how like, is there something where goaltenders can do some things here? I guess it can be really interesting if they can do some the equivalent of like Docker top and Docker stats. And if I could see, like how much what's are these containers using, I can already do that with power top on Linux, for instance, like process by process. So I'm thinking I could see what's the power usage of, of some containers. Um, and I wonder if down the line, is this going to be something useful or is this just silly because we can just masquerade CPU usage for, for Watson and forget about it. >>Yeah. Yeah. It was super, super interesting, uh, perspective for sure. I'm going to shut up because I want to, I want to give, make sure I give Johannes and Katie time. W w what are your thoughts of the future around, let's just say, you know, container development in general, right? You want, you want to start absolutely. Oh, honest, Nate. Johns wants more time. I say, I'll try not to. Beneficiate >>Expensive here, but, um, so one of the things that we've we've touched upon earlier in the panel was multicloud strategy. And I was reading one of the data reports from it was about the concept of Kubernetes from gamer Townsville. But what is working for you to see there is that more and more organizations are thinking about multicloud strategy, which means that you need to develop an application or need an infrastructure or a component, which will allow you to run this application bead on a public cloud bead, like locally in a data center and so forth. And here, when it comes to this kind of, uh, maybe problems we come across open standards, this is where we require something, which will allow us to execute our application or to run our platform in different environments. So when you're thinking about the application or development of the application, one of the things that, um, came out in 2019 at was the Oakland. >>Um, I wish it was Kybella, which is a, um, um, an open application model based application, which allows you to describe the way you would like your service to be executed in different environments. It doesn't need to be well developed specifically for communities. However, the open application model is specialized. So specialized tries to cover multiple platforms. You will be able to execute your application anywhere you want it to. So I think that that's actually quite important because it completely obstructs what is happening underneath it, completely obstructs notions, such as containers, uh, or processes is just, I want this application and I want to have this kind of behavior is so example of, to scale in this conditions or to, um, to be exposed for these, uh, end points and so forth. And everything that I would like to mention here is that maybe this transcends again, the, uh, the logistics of the application development, but it definitely will impact the way we run our applications. >>So one of the biggest, well, one of the new trends that is kind of gaining momentum now has been around Plaza. And this is again, something which is trying to present what we have the on containers. Again, it's focusing on the, it's kind of a cyclical, um, uh, action movement that we have here. When we moved from the VMs to containers, it was smaller footprint. We want like better execution, one, this agnosticism of the platforms. We have the same thing happening here with Watson, but again, it consents a new, um, uh, kind of, well, it teaches in you, uh, in new climax here, where again, we shrink the footprint of the cluster. We have a better isolation of all the services. We have a better trend, like portability of how services and so forth. So there is a great potential out there. And again, like why I'm saying this is some of these technologies are gonna define the way we're gonna do our development of the application on our local environment. >>That's why it's important to kind of maybe have an eye there and maybe see if some of those principles of some of those technologies we can bring internally as well. And just this, like a, a final thought here, um, security has been mentioned as well. Um, I think it's something which has been, uh, at the forefront, especially when it comes to containers, uh, especially when it comes to enterprise organizations and those who are regulated, which I feel come very comfortable to run their application within a VM where you have the full isolation, you can do what we have complete control of what's happening inside that compute. So, um, again, security has been at the forefront at the moment. So I know it has mentioned in the panel before. I'd like to mention that we have the security white paper, which has been published. We have the software supply chain, white paper as well, which twice to figure out or define some of these good practices as well, again, which you can already apply from your development environment and then propagate them to production. So I'm just going to leave, uh, all of these. That's all. >>That's awesome. And yeah, well, while is very, very interesting. I saw the other day that, um, and I forget who it was, maybe, maybe all can remember, um, you know, running, running the node, um, engine inside of, you know, in Walzem inside of a browser. Right. And, uh, at first glance I said, well, we already have a JavaScript execution engine. Right. And it's kind of like Docker and Docker. So you have, uh, you know, you have the browser, then, then you have blossom and then you have a node, you know, a JavaScript runtime. And, and I didn't understand was while I was, um, you know, actually executing is JavaScript and it's not, but yeah, it's super interesting, super powerful. I always felt that the browser was, uh, Java's what write once run anywhere kind of solution, right. That never came about, they were thinking of set top, uh, TV boxes and stuff like that, which is interesting. >>I don't know, you'll some of the history of Java, but yeah. Wasm is, is very, I'm not sure how to correctly pronounce it, but yeah, it's extremely interesting because of the isolation in that boxing. Right. And running powerful languages that were used to inside of a more isolated environment. Right. And it's almost, um, yeah, it's kind of, I think I've mentioned it before that the containers inside of containers, right. Um, yeah. So Johannes, hopefully I gave you enough time. I delayed, I delayed as much as I can. My friend, you better, you better just kidding. I'm just kidding, please, please. >>It was by the way, stack let's and they worked together with Google and with Russell, um, developing the web containers, it's called there's, it's quite interesting. The research they're doing there. Yeah. Yeah. I mean, what we believe and I, I also believe is that, um, yeah, probably somebody is doing to death environments, what Docker did to servers and at least that good part. We hope that somebody will be us. Um, so what we mean by that is that, um, we think today we are still somehow emotionally attached to our dev environments. Right. We give them names, we massage them over time, which can also have its benefits, but it's, they're still pets in some way. Right. And, um, we believe that, um, environments in the future, um, will be treated similar like servers today as automated resources that you can just spin up and close down whenever you need them. >>Right. And, um, this trend essentially that you also see in serverless, if you look at what kind of Netlify is doing a bit with preview environments, what were sellers doing? Um, there, um, we believe will also arrive at, um, at Steph environments. It probably won't be there tomorrow. So it will take some time because if there's also, you know, emotion involved into, in that, in that transition, but ultimately really believe that, um, provisioning dev environments also in the cloud allows you to leverage the power of the cloud and to essentially build all that stuff that you need in order to work in advance. Right? So that's literally either command or a button. So either, I don't know, a command that spins up your local views code and SSH into, into a container, or you do it in a browser, um, will be the way that professional development teams will develop in the future. Probably let's see in our direction of document, we say it's 2000 to 23. Let's see if that holds true. >>Okay. Can we, can, we let's know. Okay. Let's just say let's have a friendly bet. I don't know that's going to be closed now, but, um, yeah, I agree. I, you know, it's my thought around is it, it's hard, right? Th these are hard. And what problems do you tackle first, right? Do you tackle the day, one of, uh, you know, of development, right. I joined a team, Hey, here's your machine? And you have Docker installed and there you go, pull, pull down your environment. Right. Is that necessarily just an image? You know, what, what exactly is that sure. Containers are involved. Right. But that's, I mean, you, you've probably all gone through it. You joined a team, new project, even open-source project, right there. There's a huge hurdle just to get everything configured, to get everything installed, to get it up and running, um, you know, set aside all understanding the code base. >>Cause that's a different issue. Right. But just getting everything running locally and to your point earlier, Jacob of around, uh, recreating, local production cues and environments and, you know, GPS or anything like that, right. Is extremely hard. You can't do a lot of that locally. Right. So I think that's one of the things I'd love to see tackled. And I think that's where we're tackling in dev environments, uh, with Docker, but then now how do you become productive? Right. And where do we go from there? And, uh, and I would love to see this kind of hybrid and you guys have been all been talking about it where I can, yes. I have it configured everything locally on my nice, you know, apple notebook. Right. And then, you know, I go with the family and we go on vacation. I don't want to drag this 16 inch, you know, Mac laptop with me. >>And I want to take my nice iPad with the magic keyboard and all the bang stuff. Right. And I just want to fire up and I pick up where I left off. Right. And I keep coding and environment feels, you know, as much as it can that I'm still working at backup my desktop. I think those, those are very interesting to me. And I think reproducing, uh, the production running runtime environments as close as possible, uh, when I develop my, I think that's extremely powerful, extremely powerful. I think that's one of the hardest things, right. It's it's, uh, you know, we used to say, we, you debug in production. Right. We would launch, right. We would do, uh, as much performance testing as possible. But until you flip that switch on a big, on a big site, that's where you really understand what is going to break. >>Right. Well, awesome. I think we're just about at time. I really, really appreciate everybody joining me. Um, it's been a pleasure talking to all of you. We have to do this again. If I, uh, hopefully, you know, I I'm in here in America and we seem to be doing okay with COVID, but I know around the world, others are not. So my heart goes out to them, but I would love to be able to get out of here and come see all of you and meet you in person, maybe break some bread together. But, um, again, it was a pleasure talking to you all, and I really appreciate you taking the time. Have a good evening. Cool. >>Thanks for having us. Thanks for joining us. Yes.

>> Well, thank you, Gary. Well, you know, reasonable people could debate when the so-called big data era started. But for me it was in the fall of 2010 when I was sleepwalking through this conference in Dallas. And the conference was focused on data being a liability. And the whole conversation was about, how do you mitigate the risks of things like work in process and smoking-gun emails. I got a call from my business partner, John Fard, he said to me, "get to New York and come and see the future of data. We're doing theCUBE at Hadoop World tomorrow." I subsequently I canceled about a dozen meetings that I had scheduled for the week. And with only one exception, every one of the folks I was scheduled to meet said, "what's a Hadoop?" Well, I flew through an ice storm across country. I got to the New York Hilton around 3:00 AM, and I met John in the Dark Bar. If any of you remember that little facility. And I caught a little shut eye. And then the next day I met some of the most interesting people in tech during that time. They were thinking a lot differently than we were used to. They looked at data through a prism of value. And they were finding new ways to do things like deal with fraud, they were building out social networks, they were finding novel marketing vectors and identifying new investment strategies. The other thing they were doing is, they were taking these little tiny bits of code and bring it to really large sets of data. And they were doing things that I hadn't really heard of like no schema-on-write. And they were transforming their organizations by looking at data not as a liability, but as a monetization opportunity. And that opened my eyes and theCUBE, like a lot of others bet its business on data. Now over the past decade, customers have built up infrastructure and have been accommodating a lot of different use cases. Things like offloading ETL, data protection, mining data, analyzing data, visualizing. And as you know, you no doubt realize this was at a time when the cloud was, you know, really kind of nascent. And it was really about startups and experimentation. But today, we've evolved from the wild west of 2010, and many of these customers they're leveraging the cloud for of course, ease of use and flexibility it brings, but also they're finding out it brings complexity and risk. I want to tell you a quick story. Recently it was interviewing a CIO in theCUBE and he said to me, "if you just shove all your workloads into the cloud, you might get some benefit, but you're also going to miss the forest to the trees. You have to change your operating model and expand your mind as to what is cloud and create a cloud light experience that spans your on premises, workloads, multiple public clouds, and even the edge. And you have to re-imagine your business and the possibilities that this new architecture this new platform can bring." So we're going to talk about some of this today in a little bit more detail and specifically how we can better navigate the data storm. And what's the role of hybrid cloud. I'm really excited to have two great guests. Manish Dasaur is the managing director in the North America lead for analytics and artificial intelligence at Accenture. Anupam Singh is the chief customer officer for Cloudera. Gentlemen, welcome to theCUBE, great to see you. >> Hi Dave good to see you again. >> All right, guys, Anupam and Manish, you heard my little monologue upfront Anupam we'll start with you. What would you? Anything you'd add, amend, emphasize? You know, share a quick story. >> Yeah, Dave thank you for that introduction. It takes me back to the days when I was an article employee and went to this 14 people meet up. Just a couple of pizza talking about this thing called Hadoop. And I'm just amazed to see that today we are now at 2000 customers, who are using petabytes of data to make extremely critical decisions. Reminds me of the fact that this week, a lot of our customers are busy thinking about elections and what effect it would have on their data pipeline. Will it be more data? Will it be more stressful? So, totally agree with you. And also agree that cloud, is almost still in early days in times of the culture of IT on how to use the cloud. And I'm sure we'll talk about that today in greater detail. >> Yeah most definitely Manish I wonder if we could get your perspective on this. I mean, back when Anupam was at Oracle you'd shove a bunch of, you know, data, maybe you could attach a big honking disc drive, you'd buy some Oracle licenses, you know, it was a Unix box. Everything went into this, you know, this God box and then things changed quite dramatically, which was awesome, but also complex. And you guys have been there from the beginning. What's your perspective on all this? >> Yeah, it's been fascinating just to watch the market and the technology evolve. And I think the urgency to innovate is really just getting started. We're seeing companies drive growth from 20% in cloud today, to 80% cloud in the next few years. And I think the emergence of capabilities like hybrid cloud, we really get upfront a lot of flexibility for companies who need the ability to keep some data in a private setting, but be able to share the rest of the data in a public setting. I think we're just starting to scratch the surface of it. >> So let's talk a little bit about what is a hybrid cloud Anupam I wonder if you could take this one let's start with you and then Manish we come back to you and to get the customer perspective as well. I mean, it is a lot of things to a lot of people, but what is it? Why do we need it? And you know, what's the value? >> Yeah, I could speak poetic about Kubernetes and containers et cetera. But given that, you know, we talk to customers a lot, all three of us from the customer's perspective, hybrid cloud is a lot about collaboration and ease of procurement. A lot of our customers, whether they're in healthcare, banking or telco, are being asked to make the data available to regulatory authority, to subsidiaries outside of their geography. When you need that data to be available in other settings, taking a from on-prem and making it available in public cloud, enables extreme collaboration, extreme shared data experience if you will, inside the company. So we think about hybrid like that. >> Manish anything you'd add? How are your customers thinking about it? >> I mean, in a very simple way, it's a structure that where we are allowing mixed computing storage and service environments that's made of on-prem structures, private cloud structures, and public cloud structures. We're often calling it multicloud or mixcloud. And I think the really big advantage is, this model of cloud computing is enabling our clients to gain the benefits of public cloud setting, while maintaining your own private cloud for sensitive and mission critical and highly regulated computing services. That's also allowing our clients and organizations to leverage the pay-as-you-go model, which is really quite impressive and attractive to them because then they can scale their investments accordingly. Clients can combine one or more public cloud providers together in a private cloud, multicloud platform. The cloud can operate independently of each other, communicate over an encrypted connection. This dynamic solution offers a lot of flexibility and scalability which I think is really important to our clients. >> So Manish I wonder if we would stay there. How do they, how do your customers decide? How do you help them decide, you know, what the right mix is? What the equilibrium is? How much should it be in on-prem? How much should be in public or across clouds? Or, you know, eventually, well the edge will I guess decide for us. But, how do you go through, what are the decision points there? >> Yeah, I think that's a great question Dave. I would say there's several factors to consider when developing a cloud strategy that's the right strategy for you. Some of the factors that come to my mind when contemplating it, one would be security. Are there data sets that are highly sensitive that you don't want leaving the premise, versus data sets that need to be in a more shareable solution. Another factor I'd consider is speed and flexibility. Is there a need to stand up and stand down capabilities based on the seasonality of the business or some short-term demands? Is there a need to add and remove scale from the infrastructure and that quick pivot and that quick reaction is another factor they should consider. The third one I'd probably put out there is cost. Large data sets and large computing capacities often much more scalable and cost effective than a cloud infrastructure so there's lots of advantages to think through there. And maybe lastly I'd share is the native services. A lot of the cloud providers enable a set of native services for ingestion, for processing, of modeling, for machine learning, that organizations can really take advantage of. I would say if you're contemplating your strategy right now, my coaching would be, get help. It's a team sport. So definitely leverage your partners and think through the pros and cons of the strategy. Establish a primary hyperscaler, I think that's going to be super key and maximize your value through optimizing the workload, the data placement and really scaling the running operations. And lastly, maybe Dave move quickly right? Each day that you wait, you're incurring technical debt in your legacy environment, that's going to increase the cost and barrier to entry when moving to the new cloud hybrid driver. >> Thank you for that. Anupam I wonder if we could talk a little bit about the business impact. I mean, in the early days of big data, yes, it was a heavy lift, but it was really transformative. When you go to hybrid cloud, is it really about governance and compliance and security and getting the right mix in terms of latency? Are there other, you know, business impacts that are potentially as transformative as we saw in the early days? What are your thoughts on that? >> Absolutely. It's the other business impacts that are interesting. And you know, Dave, let's say you're in the line of business and I come to you and say, oh, there's cost, there's all these other security governance benefits. It doesn't ring the bell for you. But if I say, Dave used to wait 32 weeks, 32 weeks to procure hardware and install software, but I can give you the same thing in 30 minutes. It's literally that transformative, right? Even on-prem, if I use cloud native technology, I can give something today within days versus weeks. So we have banks, we have a bank in Ohio that would take 32 weeks to rack up a 42 node server. Yes, it's very powerful, you have 42 nodes on it, 42 things stacked on it, but still it's taking too much time. So when you get cloud native technologies in your data center, you start behaving like the cloud and you're responsive to the business. The responsiveness is very important. >> That's a great point. I mean, in fact, you know, there's always this debate about is the cloud public cloud probably cost more expensive? Is it more expensive to rent than it is to own? And you get debates back and forth based on your perspective. But I think at the end of the day, what, Anupam you just talked about, it may oftentimes could dwarf, you know, any cost factors, if you can actually, you know, move that fast. Now cost is always a consideration. But I want to talk about the migration path if we can Manish. Where do, how should customers think about migrating to the cloud migration's a, an evil word. How should they think about migrating to the cloud? What's the strategy there? Where should they start? >> No I think you should start with kind of a use case in mind. I think you should start with a particular data set in mind as well. I think starting with what you're really seeking to achieve from a business value perspective is always the right lens in my mind. This is the decade of time technology and cloud to the fitness value, right? So if you start with, I'm seeking to make a dramatic upsell or dramatic change to my top line or bottom line, start with the use case in mind and migrate the data sets and elements that are relevant to that use case, relevant to that value, relevant to that unlock that you're trying to create, that I think is the way to prioritize it. Most of our clients are going to have tons and tons of data in their legacy environment. I don't think the right way to start is to start with a strategy that's going to be focused on migrating all of that. I think the strategy is start with the prioritized items that are tied to the specific value or the use case you're seeking to drive and focus your transformation and your migration on that. >> So guys I've been around a long time in this business and been an observer for awhile. And back in the mainframe days, we used to have a joke called CTAM. When we talk about moving data, it was called the Chevy truck access method. So I want to ask you Anupam, how do you move the data? Do you, it's like an Einstein saying, right? Move as much data as you need to, but no more. So what's going on in that front? what's happening with data movement, and, you know, do you have to make changes to the applications when you move data to the cloud? >> So there's two design patterns, but I love your service story because you know, when you have a 30 petabyte system and you tell the customer, hey, just make a copy of the data and everything will be fine. That will take you one and a half years to make the copies aligned with each other. Instead, what we are seeing is the biggest magic is workload analysis. You analyze the workload, you analyze the behavior of the users, and say so let's say Dave runs dashboards that are very complicated and Manish waits for compute when Dave is running his dashboard. If you're able to gather that information, you can actually take some of the noise out of the system. So you take selected sets of hot data, and you move it to public cloud, process it in public cloud maybe even bring it back. Sounds like science fiction, but the good news is you don't need a Chevy to take all that data into public cloud. It's a small amount of data. That's one reason the other pattern that we have seen is, let's say Manish needs something as a data scientist. And he needs some really specific type of GPUs that are only available in the cloud. So you pull the data sets out that Manish needs so that he can get the new silicone, the new library in the cloud. Those are the two patterns that if you have a new type of compute requirement, you go to public cloud, or if you have a really noisy tenant, you take the hot data out into public cloud and process it there. Does that make sense? >> Yeah it does and it sort of sets up this notion I was sort of describing upfront that the cloud is not just, you know, the public cloud, it's the spans on-prem and multicloud and even the edge. And it seems to me that you've got a metadata opportunity I'll call it and a challenge as well. I mean, there's got to be a lot of R and D going on right now. You hear people talking about cloud native and I wonder on Anupam if you could stay on that, I mean, what's your sense as to how, what the journey is going to look like? I mean, we're not going to get there overnight. People have laid out a vision of this sort of expanding cloud and I'm saying it's a metadata opportunity, but I, you know, how do you, the system has to know what workload to put where based on a lot of those factors that you guys were talking about. The governance, the laws of the land, the latency issues, the cost issues is, you know, how is the industry Anupam sort of approaching this problem and solving this problem? >> I think the biggest thing is to reflect all your security governance across every cloud, as well as on-prem. So let's say, you know, a particular user named Manish cannot access financial data, revenue data. It's important that that data as it goes around the cloud, if it gets copied from on-prem to the cloud, it should carry that quality with it. A big danger is you copy it into some optic storage, and you're absolutely right Dave metadata is the goal there. If you copy the data into an object storage and you lose all metadata, you lose all security, you lose all authorization. So we have invested heavily in something called shared data experience. Which reflects policies from on-prem all the way to the cloud and back. We've seen customers needing to invest in that, but some customers went all hog on the cloud and they realize that putting data just in these buckets of optic storage, you lose all the metadata, and then you're exposing yourself to some breach and security issues. >> Manish I wonder if we could talk about, thank you for that Anupam. Manish I wonder if we could talk about, you know, I've imagined a project, okay? Wherever I am in my journey, maybe you can pick your sort of sweet spot in the market today. You know, what's the fat middle if you will. What does a project look like when I'm migrating to the cloud? I mean, what are some of the, who are the stakeholders? What are some of the outer scope maybe expectations that I better be thinking about? What kind of timeframe? How should I tackle this and so it's not like a, you know, a big, giant expensive? Can I take it in pieces? What's the state-of-the-art of a project look like today? >> Yeah, lots of thoughts come to mind, Dave, when you ask that question. So there's lots to pack. As far as who the buyer is or what the project is for, this is out of migration is directly relevant to every officer in the C-suite in my mind. It's very relevant for the CIO and CTO obviously it's going to be their infrastructure of the future, and certainly something that they're going to need to migrate to. It's very important for the CFO as well. These things require a significant migration and a significant investment from enterprises, different kind of position there. And it's very relevant all the way up to the CEO. Because if you get it right, the truly the power it unlocks is illuminates parts of your business that allow you to capture more value, capture a higher share of wallet, allows you to pivot. A lot of our clients right now are making a pivot from going from a products organization to an as a service organization and really using the capabilities of the cloud to make that pivot happen. So it's really relevant kind of across the C-suite. As far as what a typical program looks like, I always coach my clients just like I said, to start with the value case in mind. So typically, what I'll ask them to do is kind of prioritize their top three or five use cases that they really want to drive, and then we'll land a project team that will help them make that migration and really scale out data and analytics on the cloud that are focused on those use cases. >> Great, thank you for that. I'm glad you mentioned the shift in the mindset from product to as a service. We're seeing that across the board now, even infrastructure players are jumping on the bandwagon and borrowing some sort of best practices from the SaaS vendors. And I wanted to ask you guys about, I mean, as you move to the cloud, one of the other things that strikes me is that you actually get greater scale, but there's a broader ecosystem as well. So we're kind of moving from a product centric world and with SaaS we've got this sort of platform centric, and now it seems like ecosystems are really where the innovation is coming from. I wonder if you guys could comment on that, maybe Anupam you could start. >> Yeah, many of our customers as I said right? Are all about sharing data with more and more lines of businesses. So whenever we talk to our CXO partners, our CRO partners, they are being asked to open up the big data system to more tenants. The fear is, of course, if you add more tenants to a system, it could get, you know, the operational actually might get violated. So I think that's a very important part as more and more collaboration across the company, more and more collaboration across industries. So we have customers who create sandboxes. These are healthcare customers who create sandbox environments for other pharma companies to come in and look at clinical trial data. In that case, you need to be able to create these fenced environments that can be run in public cloud, but with the same security that you expect up. >> Yeah thank you. So Manish this is your wheelhouse as Accenture. You guys are one of the top, you know, two or three or four organizations in the world in terms of dealing with complexity, you've got deep industry expertise, and it seems like some of these ecosystems as Anupam was just sort of describing it in a form are around industries, whether it's healthcare, government, financial services and the like. Maybe your thoughts on the power of ecosystems versus the, you know, the power of many versus the resources of one. >> Yeah, listen, I always talk about this is a team sport right? And it's not about doing it alone. It's about developing as ecosystem partners and really leveraging the power of that collective group. And I've been for as my clients to start thinking about, you know, the key thing you want to think about is how you migrate to becoming a data driven enterprise. And in order for you to get there, you're going to need ecosystem partners to go along the journey with you, to help you drive that innovation. You're going to need to adopt a pervasive mindset to data and democratization of that data everywhere in your enterprise. And you're going to need to refocus your decision-making based on that data, right? So I think partner ecosystem partnerships are here to stay. I think what we're going to see Dave is, you know, at the beginning of the maturity cycle, you're going to see the ecosystem expand with lots of different players and technologies kind of focused on industry. And then I think you'll get to a point where it starts to mature and starts to consolidate as ecosystem partners start to join together through acquisitions and mergers and things like that. So I think ecosystem is just starting to change. I think the key message that I would give to our clients is take advantage of that. There's too much complexity for any one person to kind of navigate through on your own. It's a team sport, so take advantage of all the partnerships you can create. >> Well, Manish one of the things you just said that it kind of reminds me, you said data data-driven, you know, organizations and, you know, if you look at the pre-COVID narrative around digital transformation, certainly there was a lot of digital transformation going on, but there was a lot of complacency too. I talked to a lot of folks, companies that say, "you know, we're doing pretty well, our banks kicking butt right now, we're making a ton of money." Or you know, all that stuff that's kind of not on my watch. I'll be retired before then. And then it was the old, "if it ain't broke, don't fix it." And then COVID breaks everything. And now if you're not digital, you're out of business. And so Anupam I'll start with you. I mean, to build a data-driven culture, what does that mean? That means putting data at the center of your organization, as opposed to around in stove pipes. And this, again, we talked about this, it sort of started in there before even the early parts of last decade. And so it seems that there's cultural aspects there's obviously technology, but there's skillsets, there's processes, you've got a data lifecycle and a data, what I sometimes call a data pipeline, meaning an end to end cycle. And organizations are having to rethink really putting data at the core. What are you seeing? And specifically as it relates to this notion of data-driven organization and data culture, what's working? >> Yeah three favorite stories, and you're a 100% right. Digital transformation has been hyperaccelerated with COVID right? So our telco customers for example, you know, Manish had some technical problems with bandwidth just 10 minutes ago. Most likely is going to call his ISP. The ISP will most likely load up a dashboard in his zip code and the reason it gives me stress, this entire story is because most likely it's starting on a big data system that has to collect data every 15 minutes, and make it available. Because you'll have a very angry Manish on the other end, if you can't explain when is the internet coming back, right? So, as you said this is accelerated. Our telco providers, our telco customers ability to ingest data, because they have to get it in 15 minute increments, not in 24 hour increments. So that's one. On the banking sector what we have seen is uncertainty has created more needs for data. So next week is going to be very uncertain all of us know elections are upcoming. We have customers who are preparing for that additional variable capacity, elastic capacity, so that if investment bankers start running hundreds and thousands of reports, they better be ready. So it's changing the culture at a very fundamental level, right? And my last story is healthcare. You're running clinical trials, but everybody wants access to the data. Your partners, the government wants access to the data, manufacturers wants access to the data. So again, you have to actualize digital transformation on how do you share very sensitive, private healthcare data without violating any policy. But you have to do it quick. That's what COVID has started. >> Thank you for that. So I want to come back to hybrid cloud. I know a lot of people in the audience are, want to learn more about that. And they have a mandate really to go to cloud generally but hybrid specifically. So Manish I wonder if you could share with us, maybe there's some challenges, I mean what's the dark side of hybrid. What should people be thinking about that they, you know, they don't want to venture into, you know, this way, they want to go that way. What are some of the challenges that you're seeing with customers? And how are they mitigating them? >> Yeah, Dave it's a great question. I think there's a few items that I would coach my clients to prioritize and really get right when thinking about making the migration happen. First of all, I would say integration. Between your private and public components that can be complex, it can be challenging. It can be complicated based on the data itself, the organizational structure of the company, the number of touches and authors we have on that data and several other factors. So I think it's really important to get this integration right, with some clear accountabilities build automation where you can and really establish some consistent governance that allows you to maintain these assets. The second one I would say is security. When it comes to hybrid cloud management, any transfers of data you need to handle the strict policies and procedures, especially in industries where that's really relevant like healthcare and financial services. So using these policies in a way that's consistent across your environment and really well understood with anyone who's touching your environment is really important. And the third I would say is cost management. All the executives that I talk about have to have a cost management angle to it. Cloud migration provides ample opportunities for cost reduction. However many migration projects can go over budget when all the costs aren't factored in, right? So your cloud vendors. You've got to be mindful of kind of the charges on accessing on premise applications and scaling costs that maybe need to be budgeted for and where if possible anticipated and really plan for. >> Excellent. So Anupam I wonder if we could go a little deeper on, we talked a little bit about this, but kind of what you put where, which workloads. What are you seeing? I mean, how are people making the choice? Are they saying, okay, this cloud is good for analytics. This cloud is good. Well, I'm a customer of their software so I'm going to use this cloud or this one is the best infrastructure and they got, you know, the most features. How are people deciding really what to put where? Or is it, "hey, I don't want to be locked in to one cloud. I want to spread my risk around. What are you seeing specifically? >> I think the biggest thing is just to echo what Manish said. Is business comes in and as a complaint. So most projects that we see on digital transformation and on public cloud adoption is because businesses complaining about something. It's not architectural goodness, it is not for just innovation for innovation's sake. So, the biggest thing that we see is what we call noisy neighbors. A lot of dashboards, you know, because business has become so intense, click, click, click, click, you're actually putting a lot of load on the system. So isolating noisy neighbors into a cloud is one of the biggest patterns that you've seen. It takes the noisiest tenant on your cluster, noisiest workload and you take them to public cloud. The other one is data scientists. They want new libraries, they want to work with GPU's. And to your point Dave, that's where you select a particular cloud. Let's say there's a particular type silicone that is available only in that cloud. That GPU is available only in that cloud or that particular artificial intelligence library is available only in a particular cloud. That's when customers say, Hey miss, they decided, why don't you go to this cloud while the main workload might still be running on them, right? That's the two patterns that we are seeing. >> Right thank you. And I wonder if we can end on a little bit of looking to the future. Maybe how this is all going to evolve over the next several years. I mean, I like to look at it at a spectrum at a journey. It's not going to all come at once. I do think the edge is part of that. But it feels like today we've got, you know, multi clouds are loosely coupled and hybrid is also loosely coupled, but we're moving very quickly to a much more integrated, I think we Manish you talked about integration. Where you've got state, you've got the control plane, you've got the data plane. And all this stuff is really becoming native to the respective clouds and even bring that on-prem and you've got now hybrid applications and much much tighter integration and build this, build out of this massively distributed, maybe going from it's a hyper-converged to hyper-distributed again including the edge. So I wonder Manish we could start with you. How are your customers thinking about the future? How are they thinking about, you know, making sure that they're not going down a path where that's going to, they're going to incur a lot of technical debt? I know there's sort of infrastructure is code and containers and that seems it seems necessary, but insufficient there's a lot of talk about, well maybe we start with a functions based or a serverless architecture. There's some bets that have to be made to make sure that you can future proof yourself. What are you recommending there Manish? >> Yeah, I, listen I think we're just getting started in this journey. And like I said, it's really exciting time and I think there's a lot of evolution in front of us that we're going to see. I, you know, I think for example, I think we're going to see hybrid technologies evolve from public and private thinking to dedicated and shared thinking instead. And I think we're going to see advances in capabilities around automation and computer federation and evolution of consumption models of that data. But I think we've got a lot of kind of technology modifications and enhancements ahead of us. As far as companies and how they future proof themselves. I would offer the following. First of all, I think it's a time for action, right? So I would encourage all my class to take action now. Every day spent in legacy adds to the technical debt that you're going to incur, and it increases your barrier to entry. The second one would be move with agility and flexibility. That's the underlying value of hybrid cloud structures. So organizations really need to learn how to operate in that way and take advantage of that agility and that flexibility. We've talked about creating partnerships in ecosystems I think that's going to be really important. Gathering partners and thought leaders to help you navigate through that complexity. And lastly I would say monetizing your data. Making a value led approach to how you viewed your data assets and force a function where each decision in your enterprise is tied to the value that it creates and is backed by the data that supports it. And I think if you get those things right, the technology and the infrastructure will serve. >> Excellent and Anupam why don't you bring us home, I mean you've got a unique combination of technical acumen and business knowledge. How do you see this evolving over the next three to five years? >> Oh, thank you Dave. So technically speaking, adoption of containers is going to steadily make sure that you're not aware even of what cloud you're running on that day. So the multicloud will not be a requirement even, it will just be obviated when you have that abstraction there. Contrarily, it's going to be a bigger challenge. I would echo what Manish said start today, especially on the cultural side. It is great that you don't have to procure hardware anymore, but that also means that many of us don't know what our cloud bill is going to be next month. It is a very scary feeling for your CIO and your CFO that you don't know how much you're going to to spend next month forget next year, right? So you have to be agile in your financial planning as much you have to be agile in your technical planning. And finally I think you hit on it. Ecosystems are what makes data great. And so you have to start from day one that if I am going on this cloud solution, is the data shareable? Am I able to create an ecosystem around that data? Because without that, it's just somebody running a report may or may not have value to the business. >> That's awesome, guys. Thanks so much for a great conversation. We're at a time and I want to wish everybody a terrific event. Let me now hand it back to Vanita. She's going to take you through the rest of the day. This is Dave Vellante for theCUBE, thanks. (smooth calm music)

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back

>>Hello. My name is Isaac twang and I am on the faculty at MIT in electrical engineering and computer science and in physics. And it is a pleasure for me to be presenting at today's NTT research symposium of 2020 to share a little bit with you about programmable quantum simulators theory and practice the simulation of physical systems as described by their Hamiltonian. It's a fundamental problem which Richard Fineman identified early on as one of the most promising applications of a hypothetical quantum computer. The real world around us, especially at the molecular level is described by Hamiltonians, which captured the interaction of electrons and nuclei. What we desire to understand from Hamiltonian simulation is properties of complex molecules, such as this iron molded to them. Cofactor an important catalyst. We desire there are ground States, reaction rates, reaction dynamics, and other chemical properties, among many things for a molecule of N Adams, a classical simulation must scale exponentially within, but for a quantum simulation, there is a potential for this simulation to scale polynomials instead. >>And this would be a significant advantage if realizable. So where are we today in realizing such a quantum advantage today? I would like to share with you a story about two things in this quest first, a theoretical optimal quantum simulation, awkward them, which achieves the best possible runtime for generic Hamiltonian. Second, let me share with you experimental results from a quantum simulation implemented using available quantum computing hardware today with a hardware efficient model that goes beyond what is utilized by today's algorithms. I will begin with the theoretically optimal quantum simulation uncle rhythm in principle. The goal of quantum simulation is to take a time independent Hamiltonian age and solve Schrodinger's equation has given here. This problem is as hard as the hardest quantum computation. It is known as being BQ P complete a simplification, which is physically reasonable and important in practice is to assume that the Hamiltonian is a sum over terms which are local. >>For example, due to allow to structure these local terms, typically do not commute, but their locality means that each term is reasonably small, therefore, as was first shown by Seth Lloyd in 1996, one way to compute the time evolution that is the exponentiation of H with time is to use the lead product formula, which involves a successive approximation by repetitive small time steps. The cost of this charterization procedure is a number of elementary steps, which scales quadratically with the time desired and inverse with the error desired for the simulation output here then is the number of local terms in the Hamiltonian. And T is the desired simulation time where Epsilon is the desired simulation error. Today. We know that for special systems and higher or expansions of this formula, a better result can be obtained such as scaling as N squared, but as synthetically linear in time, this however is for a special case, the latest Hamiltonians and it would be desirable to scale generally with time T for a order T time simulation. >>So how could such an optimal quantum simulation be constructed? An important ingredient is to transform the quantum simulation into a quantum walk. This was done over 12 years ago, Andrew trials showing that for sparse Hamiltonians with around de non-zero entries per row, such as shown in this graphic here, one can do a quantum walk very much like a classical walk, but in a superposition of right and left shown here in this quantum circuit, where the H stands for a hazard market in this particular circuit, the head Mar turns the zero into a superposition of zero and one, which then activate the left. And the right walk in superposition to graph of the walk is defined by the Hamiltonian age. And in doing so Childs and collaborators were able to show the walk, produces a unitary transform, which goes as E to the minus arc co-sign of H times time. >>So this comes close, but it still has this transcendental function of age, instead of just simply age. This can be fixed with some effort, which results in an algorithm, which scales approximately as towel log one over Epsilon with how is proportional to the sparsity of the Hamiltonian and the simulation time. But again, the scaling here is a multiplicative product rather than an additive one, an interesting insight into the dynamics of a cubit. The simplest component of a quantum computer provides a way to improve upon this single cubits evolve as rotations in a sphere. For example, here is shown a rotation operator, which rotates around the axis fi in the X, Y plane by angle theta. If one, the result of this rotation as a projection along the Z axis, the result is a co-sign squared function. That is well-known as a Ravi oscillation. On the other hand, if a cubit is rotated around multiple angles in the X Y plane, say around the fee equals zero fee equals 1.5 and fee equals zero access again, then the resulting response function looks like a flat top. >>And in fact, generalizing this to five or more pulses gives not just flattered hops, but in fact, arbitrary functions such as the Chevy chef polynomial shown here, which gets transplants like bullying or, and majority functions remarkably. If one does rotations by angle theta about D different angles in the X Y plane, the result is a response function, which is a polynomial of order T in co-sign furthermore, as captured by this theorem, given a nearly arbitrary degree polynomial there exists angles fi such that one can achieve the desired polynomial. This is the result that derives from the Remez exchange algorithm used in classical discreet time signal processing. So how does this relate to quantum simulation? Well recall that a quantum walk essentially embeds a Hamiltonian insight, the unitary transform of a quantum circuit, this embedding generalize might be called and it involves the use of a cubit acting as a projector to control the application of H if we generalize the quantum walk to include a rotation about access fee in the X Y plane, it turns out that one obtains a polynomial transform of H itself. >>And this it's the same as the polynomial in the quantum signal processing theorem. This is a remarkable result known as the quantum synchrony value transformed theorem from contrast Julian and Nathan weep published last year. This provides a quantum simulation auger them using quantum signal processing. For example, can start with the quantum walk result and then apply quantum signal processing to undo the arc co-sign transformation and therefore obtain the ideal expected Hamiltonian evolution E to the minus I H T the resulting algorithm costs a number of elementary steps, which scales as just the sum of the evolution time and the log of one over the error desired this saturates, the known lower bound, and thus is the optimal quantum simulation algorithm. This table from a recent review article summarizes a comparison of the query complexities of the known major quantum simulation algorithms showing that the cubitus station and quantum sequel processing algorithm is indeed optimal. >>Of course, this optimality is a theoretical result. What does one do in practice? Let me now share with you the story of a hardware efficient realization of a quantum simulation on actual hardware. The promise of quantum computation traditionally rests on a circuit model, such as the one we just used with quantum circuits, acting on cubits in contrast, consider a real physical problem from quantum chemistry, finding the structure of a molecule. The starting point is the point Oppenheimer separation of the electronic and vibrational States. For example, to connect it, nuclei, share a vibrational mode, the potential energy of this nonlinear spring, maybe model as a harmonic oscillator since the spring's energy is determined by the electronic structure. When the molecule becomes electronically excited, this vibrational mode changes one obtains, a different frequency and different equilibrium positions for the nuclei. This corresponds to a change in the spring, constant as well as a displacement of the nuclear positions. >>And we may write down a full Hamiltonian for this system. The interesting quantum chemistry question is known as the Frank Condon problem. What is the probability of transition between the original ground state and a given vibrational state in the excited state spectrum of the molecule, the Frank content factor, which gives this transition probability is foundational to quantum chemistry and a very hard and generic question to answer, which may be amiable to solution on a quantum computer in particular and natural quantum computer to use might be one which already has harmonic oscillators rather than one, which has just cubits. This has provided any Sonic quantum processors, such as the superconducting cubits system shown here. This processor has both cubits as embodied by the Joseph's injunctions shown here, and a harmonic oscillator as embodied by the resonant mode of the transmission cavity. Given here more over the output of this planar superconducting circuit can be connected to three dimensional cavities instead of using cubit Gates. >>One may perform direct transformations on the bull's Arctic state using for example, beam splitters, phase shifters, displacement, and squeezing operators, and the harmonic oscillator, and may be initialized and manipulated directly. The availability of the cubit allows photon number resolve counting for simulating a tri atomic two mode, Frank Condon factor problem. This superconducting cubits system with 3d cavities was to resonators cavity a and cavity B represent the breathing and wiggling modes of a Triumeq molecule. As depicted here. The coupling of these moles was mediated by a superconducting cubit and read out was accomplished by two additional superconducting cubits, coupled to each one of the cavities due to the superconducting resonators used each one of the cavities had a, a long coherence time while resonator States could be prepared and measured using these strong coupling of cubits to the cavity. And Posana quantum operations could be realized by modulating the coupling cubit in between the two cavities, the cavities are holes drilled into pure aluminum, kept superconducting by millikelvin scale. >>Temperatures microfiber, KT chips with superconducting cubits are inserted into ports to couple via a antenna to the microwave cavities. Each of the cavities has a quality factor so high that the coherence times can reach milliseconds. A coupling cubit chip is inserted into the port in between the cavities and the readout and preparation cubit chips are inserted into ports on the sides. For sake of brevity, I will skip the experimental details and present just the results shown here is the fibrotic spectrum obtained for a water molecule using the Pulsonix superconducting processor. This is a typical Frank content spectrum giving the intensity of lions versus frequency in wave number where the solid line depicts the theoretically expected result and the purple and red dots show two sets of experimental data. One taken quickly and another taken with exhaustive statistics. In both cases, the experimental results have good agreement with the theoretical expectations. >>The programmability of this system is demonstrated by showing how it can easily calculate the Frank Condon spectrum for a wide variety of molecules. Here's another one, the ozone and ion. Again, we see that the experimental data shown in points agrees well with the theoretical expectation shown as a solid line. Let me emphasize that this quantum simulation result was obtained not by using a quantum computer with cubits, but rather one with resonators, one resonator representing each one of the modes of vibration in this trial, atomic molecule. This approach represents a far more efficient utilization of hardware resources compared with the standard cubit model because of the natural match of the resonators with the physical system being simulated in comparison, if cubit Gates had been utilized to perform the same simulation on the order of a thousand cubit Gates would have been required compared with the order of 10 operations, which were performed for this post Sonic realization. >>As in topically, the Cupid motto would have required significantly more operations because of the need to retire each one of the harmonic oscillators into some max Hilbert space size compared with the optimal quantum simulation auger rhythms shown in the first half of this talk, we see that there is a significant gap between available quantum computing hardware can perform and what optimal quantum simulations demand in terms of the number of Gates required for a simulation. Nevertheless, many of the techniques that are used for optimal quantum simulation algorithms may become useful, especially if they are adapted to available hardware, moving for the future, holds some interesting challenges for this field. Real physical systems are not cubits, rather they are composed from bolt-ons and from yawns and from yawns need global anti-Semitism nation. This is a huge challenge for electronic structure calculation in molecules, real physical systems also have symmetries, but current quantum simulation algorithms are largely governed by a theorem, which says that the number of times steps required is proportional to the simulation time. Desired. Finally, real physical systems are not purely quantum or purely classical, but rather have many messy quantum classical boundaries. In fact, perhaps the most important systems to simulate are really open quantum systems. And these dynamics are described by a mixture of quantum and classical evolution and the desired results are often thermal and statistical properties. >>I hope this presentation of the theory and practice of quantum simulation has been interesting and worthwhile. Thank you.

>> Introducer: From the CUBE studios in Palo Alto and in Boston connecting with thought leaders all around the world. This is a CUBE Conversation. >> Hi everybody this is Dave Volante. Welcome to the special CUBE Conversation. I'm really excited to invite in my mentor and friend. We go way back. Fred Moore is here. He's the president of Horizon Information Strategies. We going to talk about managing data in the zettabyte era. Fred, I think when we first met, we were talking about like the megabyte era. >> Right, exactly. I think back then we had, you know, maybe 10 bytes in our telephone and one on the wristwatch, you know, but now you can put a whole data center in a single cartridge of tape and take off. Things that really changed. >> It's pretty amazing. And of course, for those who don't know Fred, he was the first a systems engineer at Storage Tech. And as I said, somebody who taught me a lot in my early days, of course he's very famous for the term that everybody uses today. Backup is one thing, recovery is everything. And Fred just wrote, you know, this fantastic paper. He's done this year after year after year. He's just dug in, he's a clear thinker, strategic planner with a technical bent in a business bent. You're like one of those five tool baseball players, Fred. But tell me about this paper. Why, did you write it? >> Well, the reason I wrote that is there's been so much focus in the last year or so on the archive component of the storage hierarchy. And the thing that's happening, we're generating data lots faster than we're analyzing it. So it's piling up being unanalyzed and sitting basically untapped for years at a time. So that has posed a big challenge for people. The other thing that got me deeper into this last year was the Hyperscale market. They are, those people are so big in terms of footprint and infrastructure that they can no longer keep everything on disk. It's just economically not possible. The energy consumption per disk, the infrastructure costs, the frequency of, you know, taking a disc out every three, four or five years for just for replacement, has made it very difficult to do that. So Hyperscale has gone to tape in a big way, and it's kind of where most of the tape business in the future is going to wind up in these Hyperscale businesses. >> Right. >> We know tape doesn't exist in the home. It doesn't exist in a small data center. It's only a large scale data center technology, but that whole cosmos led me into the archive space and in a need for a new archive technology beyond tape. >> So, I want to set up the premise here. Just going to pull this out of your paper. It says a 60% of all data is archival, and could reach 80% or more by 2024, making archival data by far the largest storage class. And given this trajectory, the traditional storage hierarchy paradigm is going to to need to disrupt itself. And quickly we're going to talk about that. That really is the premise of your paper here, isn't it? >> It is, you know, to do all this with traditional technologies is going to get very painful for a variety of reasons. So the stage is set for a new tier and a new technology to appear in the next five years. Fortunately, I'm actually working with somebody who is after this in a big way, and in a different way than what you and I know. So I think there is some hope here that we can redefine and really add a new tier down at the bottom. You see it kind of emerging on that picture of the deep archive tier it's. Beginning to show up now and it's, you know, infinite storage. I mean, if you look at major league sports, the world series and Superbowl, you know, that data will never be deleted. It'll be here forever. It'll be used periodically based on circumstances. >> Yeah, well, we've got that pyramid chart up here. I mean, you invented this chart, essentially. At least you were the first person that ever showed it to me. I honestly think that you first created this concept where you had a high performance tier, and a high cost per bit, and then an archive tier. Maybe it wasn't this granular, you know, back in the '70s and '80s? But it's constantly been changing with different media types and different use cases. >> You know, you're right. I mean, and you all know this because you know, when storage deck introduced the nearline architecture, nearline set in between online and offline storage, we called it nearline, and trademarked that term. So that was the tape library concept to move data from offline status to online status, with a robotic library. So that brought up that third tier online, nearline, and offline, but you're right. This pyramid has evolved and morphed into several things. And, you know, I keep it alive. Somebody said, I'll have a pyramid on my tombstone instead of my name when I go down. (both chuckles) But it's really the heart and soul of the infrastructure for data. And then out of this comes all the management and security, the deletion, the immutable storage concepts, the whole thing starts here. So it's like your house, you got to have a foundation, then you can build everything on top of it. >> Well, and as you pointed out in your paper, a minute ago, it always comes down to economics. So I want to bring up the sort of 10 year expected cost of ownership the TCO for the three levels you got all disk, you got all cloud and you got LTO and you got the different aspects of the cost. The purple is always the biggest piece of cost. It's the labor costs. But of course, you know, in cloud, you've got the big media cost because they've done so much automation. I wonder if you could take us through this slide, what are the key takeaways there? >> Well, you know the thing that hurts here with all these technologies is, as you can see up on top up there, what the key issues are with this and the staff and personnel. So the less people you have to manage data, the better off you are. And then, you know, it's pretty high for disk compared to a lot of things to do on desk, but lack of manage a lot of, you know, sadly what you and I had to deal with years ago and provision kind of, I mean, a lot of this stuff is just labor intensive. The further you get, the further down the pyramid and you also get less labor intensive storage. And that helps then you get a lower cost for energy and cost of ownership. The TCO thing is kind of taking on a new meaning. I hate to put up a TCO chart in some regards, because it's all based on what your input variables are. So you can decide something different, but we've tried to normalize all kinds of pricing and come up with everything. And the cloud is a big question for most people as to how does it stack up. And if you don't ever touch the data in the cloud, you know, the price comes way down. If you want to start moving data in and out of the cloud, you're going to have to ante up in a big way like that. But, you know we're going to see dollar a terabyte storage prices down at the bottom of this pyramid here in the next five years. But hey, you can get down to four or five terabyte with drives media in libraries tape, just entire flash and certainly higher than that. But you know, we're going to have the race to a dollar a terabyte, total TCO cost here in 2025. >> So when Amazon announced, they just announced a glacier. Everybody said, okay, what is that? Is that tape is that, you know, this spun down disk, cause it took a while to get it back. But you're kind of seeing that tape technology as you said, really move into the Hyperscale space and that's going to accommodate this massive, you know, lower part of the pyramid, isn't it? >> Exactly. Yeah. And we don't have a spin down disk solution today. I was actually on the board of a company that started that called Copay and years ago, right up here near Boulder. >> You watch him (both chuckles) You absolutely right. And a few other people that, you know also, but the spin down disk never made it. And you know, you can spin up and down on a desk on your desktop computer, but doing that in a data center, then on a fiber channel drive never made it. So we don't have a spin down disk to do that. The archive space is kind of dominated by very high capacity disc and then tape. And most of the archive data in the world today, unfortunately sits on display. It's not used and spinning seven by 24, three 65 and not touch much. So that's a bad economic move, but customers just found that easier to handle by doing that then going back to tape. So we've got a lot of data stored in the wrong place from a total economics point of view. >> But the Hyperscalers are solving this problem, or they're not through automation. And, you know, you referenced storage, tiering, really trying to take the labor cost out. How are they doing? Are they doing a good job? >> They've done really well taking the labor costs down, I mean, they have optimized every screw, nut and bolt in the 42 chassis that you could imagine to make it as clean as possible to do that. So they've done a whole lot to bring that cost down, but still the magnitude of these data centers, we're going to finish the year 2020 with about 570 Hyperscale data centers. So it's going right now around the world. You know, each one of these things is 350 400,000 square feet, and up of race wars space. And the economics just don't allow you to keep putting inactive data on spinning disk. We don't have to spin down disk, tape You know, I feel like the only guy in the industry that says this sometimes, but, you know, tapes had a, you know, a renaissance. That people don't appreciate in terms of reliability, throughput, you know, tapes three orders of reliability higher than disc right now. And most people don't know this. So tape's viable, the Hyperscalers see that. And read one Hyperscalers or you know, by over a million pieces of LTO tape last year alone. Just to handle this, you know, be the pressure valve to take all of this inactive stuff off of the gigantic disc farms that they have. >> Well, so let's talk about that a little bit. So you just try to keep it simple. You've got, you know, flash disk and tape. It feels like disc is getting squeezed. We know what flash has done in terms of eating into disc. And you see in that, in the storage market generally, it's soft right now. And I've posited that a lot of that is the headroom that data centers have with flash, is they don't have to buy spindles anymore for performance reasons. And the market is soft. Only pure is showing consistent growth, and ends up a little bit, cause because of mainframe, you've got Dell popping back and forth, but generally speaking, the primary storage market is not a great place to be right now, all the actions and sort of secondary storage and data protection. And so just going to get squeezed, and you mentioned tape, you said that if your only person talking about it, but you said in your paper, you know, it's sequential. So time to first bite is, is sometimes problematic, but you can front end a tape with cash. You can use algorithms and, you know, smart scans and to really address that problem. And dramatically lower the cost. Plus you could do things like you tell me Fred, you're the technologists here, but you're going to have multiple heads things that you can't necessarily do in a hermetically sealed disc drive. >> (chuckles) You can. And what you just described is called the active archive layer in the pyramid. So when you front end a tape library with a disk array for a cash buffer, you create an active archive and that data will sit in there three or four or five days before it gets demoted based on inactivity. So, you know for repetitive use and you're going to get dislike performance for tape data, and that's the same cash in concept that deserve systems had 30 years ago. So that does work and the active archive has got a lot of momentum right now. There's right here near me, where I live in Boulder. We have the Active Archive Alliances headquarters, and I get to do their annual report every year. And this whole active archives thing is a big way to make and overcome that time, the first bike problem that we've had in tape. And we'll have for quite a while. >> In your paper, you've talked about some of the use cases and workloads and you laid out, you know basically taking the pyramid and saying, okay based on the workload, some certain percentage should be up at the top of the pyramid for the high performance stuff. And of course lower for the, you know, the less, you know, important traditional workloads, et cetera. And it was striking to see the Delta between annual, the highest performance we had 70% , I think was up in the top of the pyramid versus, you know the last use case. So in you're talking about what it costs to store a zettabyte in services is that if I talk about 108 million at the high end versus a about 11 or 12 million, so huge Delta 10 X Delta between the top and the bottom based on those, you know allocations based on the workload. >> Yeah, I tried to get at the value of tiered storage based on your individual workload in your business. So I looked at five different workloads, the top one that you referenced. That was in there at 108 million, you know, is the HPC market. I mean, when I visited a few of the HPC people, you know, their DOD agencies in many cases, you know that and I threw the pyramid up. The first thing they would say our permanents inverted. You know (chuckles), all of our archive data is about 10%. You know, we were all flash as much as we can. And we have a little bit archived, we're in constant. Simulation and compute mode and producing results like crazy from the data. So we do an IO, bring in maybe a whole file at a time and compute for minutes before we come up with an answer. So just the reverse. And then I got to look into all the different workloads talking to people, and that's how we develop these profiles. >> So let's pull up this future of the storage hierarchy, was again kind of of talks to the premise of your paper. Walk us through this like, what changes should we be expecting, and you got air gap in here. We're going to, I'm going to ask you about remastering and lifespan, but take us through this. >> Yeah, you know, the traditional chart that you had up on the first big year had four tiers, you know, two disturbs and solid state at the top. And then the big archive tier, which is kind of everything falling down into tape at this point. But you know again, tape has some challenges. You know time to first bite and sequential access on. And then when we couple using tape or disc as an archive, most of that data that's archival is captured as unstructured data. So we don't have, we don't have tags, we don't have metadata, we don't have indices, and that has led to the movement for object storage, to be a primary, maybe in the next five years, the primary format in store archived data, because it's got all that information inside of it. So now we have a way to search things and we can get to objects, but in the interim, you know, it's hard to find and search out things that are unstructured and, you know, most estimates would say 80% of the world's data is at least that much is unstructured. So archives are hard to find once you store it, there's one storing is one thing, retrieving it is another thing. And that's led to the formation of another layer in the story tier. It's going to be data that doesn't have to be remastered or converted to a new technology. in the case of the disc, every three, four or five years or tape drive every eight, maybe 10 years take large lost. Kate Media can go 30 years, but with all new modern tape media, but unfortunately, you know, the underlying drive doesn't go back that far, you can't support that many different versions. So the media life is actually longer than it needs to be. So the stage is set for a new technology to appear down here to deal with this archives. So it'll have faster access will not need to be remastered every five or 10 years, but you'll have, you know, a 50 year life in here. And I believe me, I've been looking for a long time to be able find something like this. And, you know we have a shot at this now, and I'm actually working with the technology that could pull this off. >> Well, it's interesting also as well, you calling out the air gap and the chart we go back to our mainframe guesses, is not a lot we haven't seen before, you know, maybe data D duplication, but you know, the adversary has become a lot more sophisticated. And so air gaps and, you know, ransomware on everybody's mind today, but you've sort of highlighted three layers of the pyramid that are actually candidates for that air gapping. >> Yeah. The active archive up there, of course, you know, with the disk and tape combined, then just pure tape. And then this new technology, which can be removable. You know, when you have removability you create an air gap. little did we know when you and I met that removability would be important to take. We thought we were trying to get rid of the Chevy truck access method, and now without electricity with a terrorist attack and pandemic or whatever. The fastest way to move data is put it on a truck and get it out of town. So that has got renewed life right now. Removability much to my shock from where we started. >> You talked about remastering and you said it's a costly labor intensive process that typically migrates previously archived data to new media every five to 10 years. First of all, explain why you have to do that and how a data center operators can solve that problem. >> Yeah. And let's start with data where most of it sits today on described, you know it describes useful life is four to five years before it either fails or is replaced. That's pretty much common now. So then they have to start replacing these things. And that means you have to copy, you know, read the data off the disk and write it somewhere else, big data move. And as the years go by that amount of data to revamp or gets bigger and bigger. So, I mean, you can do the math as you well know, you want to move, you know, 50 petabytes of data. It's going to take several weeks to do that electronically. So this gets to be a real time consuming effort. So most data centers that I've seen will keep about one fifth of their disposal every year migrating to a new technology, just kind of rolling forward as they go like that rather than do the whole thing every five years. So that's the new build in the disc world. And then for tape the drive stay in there longer, you know the LTO family drives a good read. You know two generations back from the current one that's been there. They cut that off a year ago. They'll go back to something like this soon. But you know, you can go into 10 years on a tape drive. The media life because of very unfair right media, which was already oxidized the last 30 years or more. The old media metal particle was not oxidized. So, you know, the oxidized flake, the particles would fall off people will say shit. I've had this in here eight years, you know, and it's kind flake it I put it back in. So that didn't work well. But now that we had various Verite Media, it was all oxidized, the media lives skyrocket. So that was the whole trick with tape to get into something that was preoxidized before time could cause it to decay. So the remastering is a lot, is less on tape by two to one to three to one, but still when you've got petabytes, maybe an exabyte sitting on tape in the future, that's going to take a long time to do that. >> Right. >> So remastering you'd love a way to scale capacity without having to continue to move the data to something new ever so often. >> So my last question is you've , you know, you went from a technical role into a strategic planning role, which of course the more technical you are in that role, the better off you're going to be. You don't understand that the guardrails, but you've always had a sort of telescope in the industry and you close the paper and it's kind of where I want to end here on, you know, what's ahead. And you talk about some of the technologies that obviously have legs, like three D NAND and obviously magnetic storage. You got optical in here, but then you've got all these other ones that you even mentioned, you know, don't hold your breath waiting for these multilayer photonics and dedic DNA. What class media, holographic storage, quantum storage we do a lot about quantum. What should we be thinking about and expecting as observers as to, you know, new technologies that might drive some innovation in the storage business? >> Well, I've listed the ones that are in the lab that have any life at all, right on this paper. So, you know can kind of take your pick at what goes on there. I mean, optical disk has not made it in the data center. We talked about it for 35 years. We invested in it in storage deck and never saw the light of day. You know, optical disk has remained an entertainment technology throughout the last 35 years. And the bigger rate is very low compared to data center technology. So, you know optical would have to take a huge step going forward. We got a lot of legs left in the solid state business. That's really active SSB, the whole nonvolatile memory spaces. Probably not 45% of the total disc shipments in terms of units, from what it was at it's high and in 2010. Unbelievable though. You know, in disc shipment 650 million drives a year announced just under 400, 35,400. So flashes has taken this stuff away, like crazy. Tape shouldn't be taking just away, but the tape industry doesn't do a very effective job of marketing itself. Most people still don't know what's going on with tape. They're still looking out of the roof, still looking out of the rear view mirror at a tape, as opposed to the front windshield. We see all the new things that have happened. So, you know they have bad memories of taping the past load stretch, edge damage tape, wouldn't work a tear or anything like that. It was a problem. Oh, that's pretty well gone away now. In a moderate tape is a whole different ball game, but most people don't know that. So, you know tapes going to have to struggle with access time and sequential reality. They've done a few things to come over excess time and the order request now to take the optimizer based on physical movement on the tape that can take out 50% of your access time for multiple requests on a cartridge. The one on here that's got the most promise right now would be a version of a multilayer photonic storage, which is. I would say sort like optical, but, you know, with data center, class characteristics, multi-layer recording capability on that random access, which tape doesn't have. And, you know, I would say that's probably the one that you would want to take some look at going forward like this. The others are highly specular. You know, we've been talking about DNA since we were kids. So we don't have a DNA product out here yet. You know, it's access times eight hours. It's probably not going to work for us. That's your, that's not your deep archive anymore. That's your time capsule storage. >> Yeah, right. >> Lock the earth. So, I mean, I think you kind of see what's here. I mean, the chances are it's still going to be the magnetic technologies tape disc, and then the solid state number and stuff. >> Right. >> But these are the ones that I'm tracking and looking at, trying to have worked with a few of the companies that are in this. Future list and I'd love to see something breakthrough out there, but it's like, we've always said about a holographic storage. For example, you know, there's been more written about it than there's ever been written on it. (both chuckles) >> Well, the paper's called Reinventing Archival Storage. You can get it on your website I presume Fredhorizon.com >> Yep, absolutely. >> Awesome. >> Fred Moore, great to see you again. Thanks so much for coming on the CUBE. >> My pleasure, Dave. Thanks a lot. Great job. >> All right. And thank you for watching everybody. This is Dave Volante for the CUBE. We'll see you next time. (upbeat music)

>>This is >>Dave Volante, and welcome to Storage Day. We're here at Amazon and Boston and you're watching the Cube. Wayne do so is here. He's the general manager of a lot of stuff. File hybrid edge transfer and data protection Service is at Amazon. Web service is good to see you, Wayne. Thanks. >>Good to see you. >>So let's talk about that. That's a pretty vast portfolio that you have explained that to our audience. >>Sure thinks so. The portfolio that I'm responsible for covers a vast swath of our stories portfolio on AWS. So in that we cover all of our files. Service's s Oh, that's E f s and FSX. Our data transport service is which includes data sync, transfer for sftp and our snowball or snow service's. And then also hybrid edge, which includes our snowball, compute and our stories, Gateway Service's and then data protection, which includes a W s back up. >>Wow. Okay, great. Congratulations on that portfolio. And, you know, I said I said earlier on it started with s3, and it's just exploded. Now all the service is this is part of what we sometimes call tongue and cheek cloud to 0.0, there's more work loads, more capabilities, more granularity. But talk about some of the big picture macro trends that you guys see in the marketplace. Specific Thio Sort of your area. >>Yes. So, uh, actually, it's so many, uh, think you said things are expanding. Things are accelerating in our space. One of things I like thio talk about with respect to our portfolio is we have storage service is dated. Transport service is to match the needs of your workloads and your applications. So all of these service is a purpose built for the type of storage that you need, the programming model that you need for your applications and workloads. So whether it's object storage with s3 and glacier or block storage with BBS or most recently, file service with F s and F S X file service is so you have the tools at your disposal. It'll that you need based on your on your application. Workloads. >>Talk more about the programming model. What? How do you envision that? What do you What do you mean? What's your mental model of the different >>process? You're so forever. People have been programming based on, you know, whether it's performance or or some scale of some sort. Um, you know, uh, databases traditionally used block storage because they don't need a lot of logic between them and the storage medium itself. File storage is been used for 50 years and has a very specific program model that exist in every operating system in every programming language. You know, whether it's an open, ah, read right, see close. It's a common paradigm that is used all over the place and that capability in the performance that you need to satisfy those applications and workloads very specific. And so for for aws, we provide those final systems for for Lennox, if you would with F House Windows, which is ever sex for Windows and for very high performance computing on luster. We've had an amazing storage platform, which is s3 and S three forms the basis for a lot of our customers data lakes on and basically storage data repositories, for which there are many integrations. With that, there are other >>sword service's. I often joke that, you know, if your expertise is is unpacking boxes plugging in setting up storage arrays, managing London's you, you might want to think about updating. You know your skill sets right, But so that's another big mega trend that we certainly see is people just don't see a lot of value in planning and managing and migrating over six month periods. Storage a raise. It's It's something that really doesn't have a lot of value to the business. So you guys have announced all these service is over the years and you've got some new announcements as well, that kind of play into some of the trends that we've been talking about. Talk about the news. >>Yes, that the news is pretty rich. Uh, for this season, let's let's start off with FSX eso FSX is our service for bringing fully manage third party or open source file systems, um, to our customers. And so Fsx Windows, as example, was launched last year, reinvent and has been rolling out the whole Siri's of features throughout the year, and we have a nice set of features coming out this year. So, as example today, effort, Sex Windows is a single ese service. We are rolling out multi easy capability, >>okay? And you you Sometimes you guys make the point that the beauty is there's no change required in APS, and we talked earlier about the program. We'll talk a little bit more about that. Why is that important to customers, >>you know and all index on FXX windows for another minute. A lot of abs been written to use the semantics of a particular file system in case of Windows will say NT f s and their written for that specific file system. We've provided customers with the capability of bringing those applications to AWS without any wary of compatibility. It's a pure lift and shift model. S O makes it really easy for them to bring their workloads. They should bring their workload so they don't have to deal with some of things you brought up early around provisioning buying systems, having to worry about saying that, planning for all of that. We take all of that work away from them and they get full compatibility based on what they need today and with some of the additional capabilities we're bringing to bear with the integrations in the ecosystem and heat up US ecosystem, they'll be able to appreciate those as well. >>Let's talk a little bit about more about that because you're basically, I'm inferring you're saying, Hey, this compelling reasons why you should move into the cloud. For instance, File Service's into the cloud. What's the difference between my on Prem? Isn't just on Prem Nass stuffing it into the cloud? Or is it more than you touched on integration? So convince me, why should I move? >>It's so much more than that. So if we if we look at the basic infrastructure once you literally click three or four buttons, Thio started files and creative file system, you no longer have to worry about it ever again. So the things that you have done on Prem, you no longer have to worry about having a sword administrator or having to provision in by storage and maintain it. We take care of all that would take care of all the security elements. I'm so important to your data to make sure that's in a in a secure environment. Security. It's job number one for us. So all of these capabilities and the ability to stand it up to never have to manage it never adorable security. We take care of all the capabilities like you should really be bringing those workloads onto a platform like this so that you can spend your time on added value. Um, service is our applications for your >>business, while in the integration is also a key piece of it. I mean, you know, for years, customers and customers still sometimes want to roll their own. You know, they like to have the you know, the knobs and turn them. But but many customers that we talked or saying Listen, it's too expensive. I don't want to be a systems integrator anymore in the cloud. How can they take advantage of those? Like sometimes they call it the flywheel effect. But the other innovations that you're bringing, whether it's machine learning or other service, is that you guys are bringing in. Is that how tight is that? Integration. >>So those integrations are ongoing, and they're there forever. It goes back to what I said a minute around over a three year period. All of these capabilities gonna be delivered to them, if you would at this at the same cost as the basic service. So let's talk about what happened this year. Um ah, lot of our customers are using sage maker for their M. L A I capabilities and sage maker is deeply integrated with both fsx luster and uh, E F s so that customers again don't have to worry about stories. They're not the way about sharing that are scaling. It's all there for them. >>You mentioned. Also you responsible for the snow product convention an edge. I was what it was to me. It was your first move, so the hybrid, I'll call it. But I always joke that, but it's true. The fastest way to get data from Point A to point B is a Chevy truck, and so, but you're referring to a sort of an edge play. You talk a little bit more about that, help us understand it. >>Sure, so Snowball, a service launched about five years ago. We initially launched a service as a bulk data migration service, and it's it's been that service for roughly four years. About a year ago, a little over a year ago, we started introducing thehe bility to have compute as part of that device, and the reason for that was customers were telling us as we're moving the data, we would like to be able to do some pre processing before it makes it onto AWS before it goes into history, is example. So we started providing that capability that ended up expanding into a full blown if you would cloud platform on a device that could be run in disconnected environments or stare environments. So with Snowball today of the ability to have easy two instances CBS storage s3 storage all in one device. And so that's a really powerful construct because you can build your applications on AWS using the same service is prove out if you wouldn't Dev UPS model that there what you need to be and then literally lift them onto, ah, snowball device and have those executing in the field as if they were running directly in the cloud. >>Change the subject a little bit when I look at the logo slide of all your customers, a lot of big names on their their global companies, a lot of things. So I run a cloud and they got a data center. You know he's Boston or something. No offense if you have a data center, East Boston, but regions are critical, um, especially for global scale. Cloud brings global scale, but it's also important to have data approximate to the users. So you're reducing late and see there's availability and redundancy aspects. Talk about your philosophy around regions and how it fits into your portfolio. How do you take advantage of all that capability? >>So a lot of our customers have global presence and the ability for them to have their application to have their business function in the regions that they're doing business and have those little agencies and also the availability model of being in multiple places. Case of disasters super important. Um, are regions are built, have at minimum three availability zones and an availability zone. You could think of boat as, ah, data center. So, for example, with the F. S. When you stand up a file system with the F S, your file system is automatically distributed, replicated across all three availability zones within that region. But as the user, you don't worry about any of that. We take care of it all for you. In the unfortunate event that our availability zone is made unavailable, your data is still fine. You still have access to that data all time? >>Yeah, and your customers, I think increasingly understanding this the beginning toe architect around regions and availability zones. It's a different way of thinking, but it's in some respects sort of the modern way of thinking. >>If you if you if you go back a few years and you think about all of the disaster recovery or business continue in software and capabilities that had been created, we're providing all of those capabilities today in our regional construct. >>Yeah, well, you know this. I mean, you both better have been around for a while, and we've seen the unnatural acts that you had to do to sort of create that level of redundancy and business continuance. And it was extremely expensive, complex and really risky to test. So I'll, uh, I'll leave you with the last word. Any other thoughts that you want to share with our audience? We're >>We're We're just first off. Thank you for giving you the time. Today. We're really excited about what we're doing with each of these. Service is we're very excited about the portfolio overall on the value that it's going to bring, and he's bringing to our customers today. We're excited about all the announcements. >>Yeah, we'll say we're seeing a lot of innovation. Expansion of the Amazon portfolio. Optionality, granularity performance, horses for courses, the right tool for the right job way. Thanks so much for coming to >>my pleasure. Thank you. >>You're welcome. All right. Keep it right to everybody. You watching the cube storage day from Amazon in Boston? Right back.

>> from Bahrain. It's the Q covering AWS Public sector Bahrain brought to you by Amazon Web service is >> over and welcome back to the Cube. Special coverage here in by rain in the Middle East for Amazon Web service is eight of US Summit. I'm John for the Cube our second year, where cloud computing has changed in the landscape. It's changing the entrepreneur equation. It's changing the money equation, and Fintech is very popular. Next guest. Special guests. Ideal worrying. Who's the Deputy Group? CEO Bank, ABC. A legend in the industry. Also the founder. The Fintech Forum Coming up on your fourth big event, But you're keynoting here at Teresa Carlson and A W s Summit car on digital only bank. Welcome to the Cube. Thank you, John. Thank you. Pleasure to speak to you today, so I got a lot to talk about, but digital only bank. This is a really special time in history. Now we're living in a digital error and the digital is driving business change and digital business is on the on the plate of every major executive in the world. How are you enabling digital business, >> John? The way, The way I look at things that two years ago I have went to my board. I said, I wanna disrupt the bank. I want to develop a business digital strategy. And to do that, we have three pictures. We have to run the band first and transform the back. To do that, we have to continue investing on modernizing the bank. Then you evolve the bank investment by creating new product. And finally you just hop the back and how you disrupt the bank. And she's using the digital highway and introducing the cloud computing on bringing a new tools wishes changed the framework and changed the mechanics of the bank. So we created way are not a digital only bank which will be going live in two months time. We launch a wallet on all that sitting on our eco systems it in the cloud the very brave and bold move. But I can tell you, without that you will lose the transformation. Banking today is different than they're gonna be banking of tomorrow. Yeah, we have to start getting into the journey of transformation. >> Bold moves require bold leadership obscenity. You are that this is hard. It sounds easy on paper. You got people to convince. I agree. Changes hard people. Cultural change. How did you do it? What >> was the great question? John are started that in December 2016 I went to my board when the frantic start moving heating our partnership. Look, I want to be on the transformation. Jr and I worked for about 6 to 9 months in greeting our niche with the board unit. Explain to them frantic abroad. Them actually a special fintech speakers guidelines. So I invested heavily. I would always take holder board level people, groups Vo and tell him about the impact of digitization. New cannot afford distant idea. Feel distant. I didn't do nothing. He parked the chain was moved. We'll go without you. So what you're saying is that investment is awareness. You have to explain to him why you cannot treat fintech with the threat. You have to embrace it. >> I love that. I love that leadership. It takes time to nurture and unset the table. Absolutely. And then understand the cloud only strategy. And then you got to sell it and then you gotta implement. These are the new dynamic >> fears fears. John is that most of the stakeholder. They think of cloud as secure and the spending of times They're not security. Instead, he was spending the time questioning spend their time to improve the security with Amazon. We are partners Now we sit with them and doing a demon years. And I tell you, I'm putting all my critical banking system on the ground. >> When you talk to Amazon, you're a bank. Thanks. Have money. Thanks can be act. There were people who worried about all this. Trust is important opportunity. What made you decide to go a W s? >> There's a very first of all. When I decided to individuals with my decision, I looked at you guys, the investment you made it in security, the investment you make or not. Indeed, I looked you committed to the region. I was someone my neighbor you guys have invested that made a bold move to be in Bahrain That did a lot, picked a lot of boxes for me and that was an important move from AWS. And I tell you many regulators today they were going to cloud eventually. So it's very important that anybody is Bridget himself in the >> region. But we love a W s because we covered them. And in depth is part of our media business. And we look at disruptions to and I want to get your thoughts on fintech disruption. We were talking before we came on camera about some key times in history where disruption happened in trade. That is a tale sign for what's happening in that. Tell the story. >> All right, let me tell you the story. In 1953 the first word cargo ship sent from New York to Houston. That chip change the word made history. Why? Because it has the container. So you could supplier trade, sending through the container to different destinations. You know, before how you used to be put them in the trade through It was a nightmare. That disruption have actually improved the globalization of increased trade business. Today all this equipment here comes Chicago comes through container and each container is labelled through computer. So we re vision today Fintech made the same making same disruption in banking on my notions, everything >> and wears a similarity. Scale >> matter, united disruption. This came a volume betrayed should become disrupted. Everybody start using your container. The business the globalization fintech, globalization, skin awareness. So what? The way I see similar to that we had a disruption. Trade become more familiar. Lifestyle has improved. You can now put your house. Your TV is coming From where? From? Your Maybe >> all the cargo containers have changed. Shipping fintech is changing banking, banking >> and lifestyle. You got it. You got a job. >> And so now, well, ironically, two containers or changing the cloud Because containers and kubernetes and frustration, This is about software. That's right. Software money has been a term kicked around. When you hear the word software defined currency software to find money, what do you think of? >> Look, at the end of the day, who writes software people? Human. So basically, the thinking has become from you and I for the people that sit and think about the codes. Yeah, programmer the corner. But there's somebody behind it thinking So you have a thinker on this thing? Got Arjun you the word not the coda. >> Big moves and Finn ticker happening. I want to get your thoughts on leadership in this world where cloud has pretty much instant benefits if you execute properly. Absolutely. There's also architectural thinking. So the word business architecture is not something that they teach in business school or sometimes has to be learned over time to operate a business and go have a growth strategy in changing technological landscapes requires a business. Architecture has to have a ballistic systems thinking this is hard. You've done that for multiple years. What if you learned what were some of the challenges that you came? >> You have to look at when you're running a big enterprise. You have a lot of investment around the world, and a lot of duplication are out of inefficiency at the leader. Money to show my steak stakeholder efficiency. I want our own better shop. Now, if technology can help me to do that, why not? You'll have to jump onto the wagon? Yeah, yeah, I cannot sit idle, and I see a better efficient shop but bank running more efficiently. So I looked with technology addition to disrupt the way I work, but positive disruption. Yeah. The main thing is that the disruption should be a catalyst for a positive change. So what I learned I learned efficiency would the digital disruption with the cloud today and instead of putting 25,000 servants to serve my 18 countries. I put in the cloud. You done it. You know the economy is Ken Jordan and the shaving >> the flywheels. Amazing. The operational efficiency are amazing. As an executive, you managed to results at the end of the day. Business is business. Results matter. How are your results? You gotta make money >> at the bank. It I have a good show. Uh, results. >> Got to make some money. How do you see that? Evolving digital only business. What's your strategy? What's your roadmap? For? How you see the money making kicking >> in, John, that our clients becoming sophisticated. My corporate client today if I don't deliver in a digital solution through his statement, give the payment, Foster. He's gonna go for a fintech company because today, front companies are competing with me eating my lunch. So I have to prove, if my client becoming digital Chevy more sophisticated, I cannot sit and watch. So I have to invest heavily. You make sure my client is satisfied giving the right cash management to transaction banking. All this the book payments or this have to be alternated. So I have to be continue looking after my client. That's where the money >> are you happy where you are, Where you're at right now? Are you happy with work? >> There's always room for improvement. I will continue. Invest on Are we innovate because you cannot stop. I mean, look, I'm a zone today. Would they stop? >> No, no, they're not stopping >> way. We need to continue. >> What's your areas that you think about for the next five years? Fintech. What are an important area? >> You know what? I think Joe's gonna affect our lifestyle. A I artificial intel. I feel way only seen the beginning. We have seen nothing out of the way. A bank, ABC. We just lost our first digital employees. Uh, her name is Fatima, and we will be coming live in November. I tell you, this is the beginning. I feel artificial intelligence is gonna affect our work force in the world. >> Tell me more about this digital employees concept. >> Well, that until passionate about what we worked with a company called So machine in you Zeeland on there are the same people who actually invented you've seen the movie of it all. Yeah. So the same people actually design movie and avatar work. We work with them to create a footman. Fatma is already being trained. Do help a new Crying Toe Bank, ABC. The Tissue Bank Open account. Two shoes. A correct credit cards for you Help you and also to be able to have a chat with her to be able to ask you a question. Jerry. A question about the Bahrain about the population but banking. It's a journey on. The journey's so far being successful, and I continue. If you ask me the question in a year's time, I would tell you probably thought it would be somebody else. >> This is helping augment the experience for your customers. That's the goal, >> absolutely. And from experience with my consumer. >> Okay, I want to change the subject and talk about the fact you're the founder of the Fintech Forum. You've had your third edition for the coming up. Talk about the event. What's the purpose? >> Uh, in in about 2016 December, I went to them sentimental by rain Governor and I said, We want to sponsor a frantic conference so self like impressed that immediately and British idea. And then I went back to my board my good CEO, I said, We want to sponsor the fish interconference on my papers at the time, the warning Because at that time in nearly 2017 we hear different around the region. Nobody actually sat there and say, Was the interactive printed on banking? You see, other victim. So I launched the first winter conference Was its success 2nd 1 also a sex. And the 3rd 1 was a kook because we put the top speaker on the world. And now people are judging me for the 4th 1 event business Now. No way about Responsive Bank. NBC's was the sole sponsor. >> That's exciting. And I think these events are changing, too. The fact that you're getting into events, you're contributing your knowledge, your also sponsoring providing some working capital >> contribution as a bank. International bank. At least I can do to support my reign infrastructure in the vision in a fintech. Sorry, >> no problem. Think out of the water. I want to say I want to get your thoughts on something. I feels important. I said this last year and the year before, when Amazon launches a new region, yeah, it creates a revitalisation. It has computing power has all those things. It's a center point of innovation. How do you see that same thing? And what's, um, things that people might not know about the Amazon relationship to the area? Because all this innovation and enablement fintech societal change the government ministries are coming online here by rain. Entrepreneurs are creating value. They're getting funded this liquidity banks, air going fintech A modernization wave is happening with a new generation of young people and existing businesses. This is a digital complete modernisation. Your thoughts on on all this digital transformation Societal at a societal level with Amazon >> Amazon already contributed to the digital economy was in it in the world. And I've seen already the impact this part of the world. The fact that this conference is summer today I can't answer your question. Look at the contribution. You have about 2000 people here look the excitement of what to bring into the region. I have seen people today from Chaudhary from Kuwait, from Morocco, from Amman, from Egypt here. So you are actually building the knowledge excitement and you also have been people to understand the ecosystem and what was missing. So what you doing any bill Us now actually investing heavily on educating awareness of the digital destruction and digital economy. You are participating in the digital economy. I mean, also today I heard from Malaysia. Very good. Exactly. Sponsoring a program a degree with University of Bahrain doesn't pass just free. So this is basically you Continue doing that. And you find AWS is already effective. Life like the clock is gonna be so I think it's body is already contributing the digital economy worldwide. >> You know, I'm fascinated. I'd love to have more conversations with you on this, Maybe at your forum. But one thing I want to get your thoughts on is with digital collaboration is not just face to face. You meet people here from different countries, but then we go back to our place is but we're still together digitally. So the scale of cloud computing and digital is impacting not just money collaboration. What's your vision on how collaboration and the role of people are going to play in this new dynamic? John, >> if you have asked me three years ago our video I was a threat with winter company with your banker. They're gonna eat my lunch. But today you realize with time the only way you can move on trust through collaborating with the company. That's why today I'm sitting with AWS sitting with other victim basically breaking people. I know my banks, but I don't know how to build a clock ticking. So I caught a break. I don't know how to move on a new Sophia or so I go with. I want to use it to get that. You see what I mean? >> Yeah, I think you have another good point that we reported on many times. And that is that when you collaborate with these technologies, it makes the domain expertise and the data that you >> have >> more intellectual, more emotional property because, you know, banking intimately. You have data, you have customers. That's your intellectual property. You could use that faster with the resource. This is a new competitive advantage >> with analytics would get the science. The data is the new order, if you should, but you need the tours. You need another unique data scientist. And when you have the distant scientist dental become than yours. >> So he'll thank you for sharing your awesome insights here and let you hear about rain. Really appreciated. Congratulations. Tino speaking. Bank, ABC. Thank you. Going all digital. Bold moves. Bold leadership. Thank you. Thank you very much. We're here in the Cube. We're live broadcasting here and by rain. 80. This summit. I'm John Ferrier. Thanks for watching.

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody welcome to this special cube conversations part of our partner series sponsored by HP e hewlett-packard enterprise and we've been drilling into the role that partners play the value that they add as as they emerge is the sort of new breed of nimble system integrator Tim Ferriss is here and he's with green pages we're going to do a disaster recovery drill down it's a topic that is extremely important it's relevant to this day and age Jim thanks for coming on my pleasure thank you for having Saudi our it's it's traditionally been an expensive complicated hairy scary but necessary what should we know about dr today in the state of dr well like you said i think a lot of people have written it off as prohibitively expensive and certainly in the small and medium business but you know with the advent of cloud with it with the explosion of cloud services dr as a service has made a cloud cloud base dr and disaster recovery affordable for even a small business it's taken a lot of the complexity some of the complexity out of it and it's certainly some for some clients it's the first steps toward a cloud journey you know my my friend Fred Moore former storage tech senior vice president of strategic planning is famous for coining the term backup is one thing recovery is there anything it applies to dr you know failover is one thing failback is is everything and you know a lot of times it's just you know too dangerous to test failing back how is dr evolving particularly for the small and mid-sized businesses so they can have confidence that not only can they check a box sure for their corporate boards but they if there's a disaster they can actually recover the sim is similar to that to that phrase yeah DR is not just a replication exercise right not getting just getting data from point A to point B but automating that and automating the the testing and creating run books around around that data I think some things have certainly made that easier over the years you know I I was an early delivery consultant for solution for VMware Site Recovery Manager thankfully I've used it much more for in cases of data center migration than I have for actual disasters but you know it was a fantastic automation tool but used other technologies to get the data from point A to point B and replicate that data some things that have made that easier over the years for people in more affordable a bandwidth is cheaper so you've got to get that data it still gotta get that data from point A to point B and it was prohibitively the pipe was prohibitively expensive could it keep up with my rate of change so but bandwidth is becoming less and less expensive and less and less of a hindrance there the software and the technologies typically back in the old days it was a ray based replication you needed to have like arrays and in production in dr so i i have an all flash array in production i need that same array in dr well maybe that's maybe i want to spend money on an all flash array for a use case that I hope I never need you know I'll test but never need it and you know our partner HPE has done some great things they're letting you replicate from a nimble all flash array to a hybrid array and ER let's some people save some money there but for our small and medium business for those who want to get out of the data center business maybe they want to start with dr dr as a service has been a you know a big big mover for us you know a lot of a lot of traction with that over the past year into so i mean one of the concerns that you hear this from security practitioners all the time is that they're drowning in point products and sort of dr was sort of the same I asked the customer had to become the system integrator or had engaged and spent a lot of money figuring out that that system so the dr as a service kind of takes care of all that doesn't it does it offloads not only the operational maintenance of the of the dr infrastructure but your you can leverage their years of expertise in indy our functions you know again hopefully folks don't have a ton of experience failing over from disasters you know hopefully you only have that never happens or it happens once but but these folks have are seasoned veterans in indy are so you get to not only leverage them their service taking care of the operations of it but you get their expertise for design so i can actually you mentioned bandwidth in vo is joke gold mainframes that the the fastest way to get data from point A to point B is the Chevy truck access method and and so and that was tape and in the day since big large companies still use tape I mean the big hyper scalar guys use K tape I presume it's not is if I froze it was pretty much dead in small business and maybe even it's very word I get dirty looks yesterday but do people still use tape for dr type of thing people do I would say increasingly if people are using tape it's used for those work those less critical workloads those people are looking people you know hopefully anybody who's performing a business continuity initiative will tear their workloads you know they have their tier zero those things that need to be up and running hot in the data center those tier ones with the RTOS the recovery time objectives in the minutes tape you only want to use that for recovery time objectives maybe in the weeks okay so pretty much I mean I've always hated tape but but but it's still not dead yet now people are trying through okay so thinking as an architect let's say I'm a small let's say midsize business because there's some other challenges that their and I and I used to have you know sort of backup over here and recovery and I think about dr it wasn't integrated what should I be doing in terms of bringing those disciplines together how should I be thinking about architecting a disaster recovery solution from from my client where do I start well you you should start by assessing the the applications so don't start at the VM level or the the physical workload level here from your business what are those services that they need to provide in the event of a disaster so a business continuity plan needs to be in place before we you should take on a disaster recovery architecture initiative so having that input is key to the to the to the disaster recovery process so assess assess what services need to be up and when tear them so and then investigate we investigate with our clients several different methods of protection and a dr a dr architecture won't just consist of dr as a service or a physical prem the prem replication environment it could contain many different types of protection deer as for some products for our virtual workloads application based hot protection for sequel or database workloads and that sort of thing using native application replication so a lot of different things you can you can do and it's not just the one size fits all it's really a mosaic of things tailoring the solution based on the applications value yes that gets into so you know the funny discussions with you know people always say well speak in business terms and so you sit down as business people say what do you want your RPO and arts go to the end they go what ok RPO how much data you're willing to lose and they go how much a problem how fast do you want to get it back what are you talking about instantaneously how much money do you have so it's the notion of recovery point objective recovery time objective it's sometimes not business speak how do you translate a geek into wallet wallet yeah well yeah signing have you you ask the question have you assigned a value to downtime you know how much is it gonna cost you to be down and I don't like to go into customers and hit them with a lot of FUD you know fear uncertainty and doubt and but you you know it should a good business should value how much downtime or loss of data will cost the business and then use that to determine what they need to spend on on dr in order to make sure that that doesn't happen well you're suggesting so and and having had those conversations with many CIOs in the past it used to be email was mission-critical and it still is in many ways but of course the vast majority people have outsourced their their email to the right or ever Microsoft or whomever at Google and so now it becomes so the answer to that question is what does it cost you when it sounds well it depends what system is down you know if it's my transaction system and I'm a retailer online well and it's it's this Black Friday I'm losing a lot of money right and so do people have a sense of the cost of downtime or the the value of their data and their applications I I think a lot of times they do not and and it takes some encouragement in order to to help them realize that I think for some it's just so for our retail customers I think it's just so obvious that to them they're they're hyper focused on on on that value so we get just like you know it's it's unfortunate but during hurricane season we have a lot of conversations with folks about about dr because it's top of mind for everybody for our retail customers their hurricane season is you know Black Friday and beyond they want to make sure that they have a solid solution leading in the Black Friday because you know a minute of downtime can mean you know thousands and thousands of dollars worth of lost business and revenue so I think more and more it's becoming a common place for people to put value on it but you still run into folks who haven't okay so and I get it this it's an insurance cell it's somewhat of a fear it's not a fear of missing out it's a fear of losing you know all your data yeah and and so okay so let's let's assume so you guys can help me get through the business case let's assume I I get there um how are people sort of moving forward how fast are they moving forward and how critical is it for their digital transformations so so how fast are people people I think are moving we're having the conversation with more and more folks more and more folks or finding value in disaster recovery and we are helping them through that helping them through that assessment and providing the value I think another big value for the IT for the IT establishment is not just providing a service the best that they can do but getting some buy-in from the business on let's let's agree what what a reasonable recovery time objective is and let's agree understand that yeah I can give you a zero RPO recovery point objective or a near zero a synchronous replication but it's gonna cost X amount of money so that the business is taking some ownership for the quality of the of the disaster recovery solution and the the tightness of the RPO and RTO and you you empower the business to make those decisions by giving them options and I think we help our businesses the customers we work with so it's important I mean maybe it's worthwhile getting a little didactic here but but we're talking about you know RPO zero it means you're essentially you're not losing any data right on a disaster which is very very probably there's no such thing technically as our poz the closest is you know synchronous replication and and that sort of thing so near zero right so so you take you do synchronous replication you know within some physical metro area metro area yeah of course the problem is if you get hit with a major disaster then they both go out so you have to do async yeah yeah frankly just understanding what type of disaster you're asking me to engineer for is it or is it a localized fire in the data center centers away in an earthquake and regional disaster affecting the whole country now right yeah so so understanding what your or is it you know we to this day IT organizations are getting calls from upper management you know if they have a power failure in the building you know okay let's failover to our disaster recovery site and the power is going to be on in an hour or so and you know knowing when to make that decision is is critical as well and not using it too trivially so if you're in a zone where you have a high probability of some kind of disaster that's gonna wipe out both synchronous you know platforms you go asynchronous but then the problem becomes speed of light there's a there's a little bit of you know or it could be a lot it could affect the performance of the application - while you're waiting for that sink that's right yeah so that could be a revenue hit but it could be you know it can you handle fifty five minutes of lost data yeah yeah sure I can probably recreate that about 15 minutes yeah maybe I'll how about an hour how about half a day mm-hmm how about a day now you start to get into the business discussion of really what's the value and now you can architect around those things you can pretty pretty much if you throw money at it you can solve any problem as an architect mostly you absolutely finish that balance of the business case right exactly so so yeah by and by showing in what we'll often do is we'll do the assessment and we'll perform a workshop on various different ways in which we can solve a problem and we can show the client in the business okay well we can do what you asked for it will cost X and that's very expensive but we can do do it this way a little bit differently or combine a couple ways that may increase your RPO a little bit but they're much more affordable you know is that a and they can make a decision based on something you said before Tim resonated with me which was it's not one size fits all which says to me I need the technology to be able to give me the granularity that I can map to the application based on the cost of downtime or the value of the application it right and it sounds like I'm inferring that that type of modern technology exists today absolutely so besides just that there are a number of different ways that applications can be protected you know Active Directory needs to be protected using its native replication Oracle and sequel have their own methods of protection so does so does exchange but virtual workloads certainly you can dial up or down the protection using dr.azz with a product behind it like a like as erto a replication and automation host based replication capability and it you know host based it makes things a bit easier for clients because they can very granularly choose individual VMS without having to house them on a specific volume that's replicated and and have to do all that mapping in the backend it takes a lot of the complexity out of things and you can assign different priorities to those machines so I could be replicating a hundred machines but ten of them are more important I want to make sure that those ten get all the bandwidth they need to keep the lowest possible RPO and certainly there are technologies out there and we are partners with with some providers who can let you dial in what role does HPE play in this whole equation right so so HPE for four Prem the Prem disaster recovery technologies it's it's fantastic because I think I mentioned it earlier you know it used to be we have some we have some very high end workloads residing in primary data centers living on all flash arrays so a nimble or a three-part all flash array those are those are expensive technologies necessary to run the business in in normal circumstances but for dr for a solution that you hope you never need you can replicate to an all flash nimble to a hybrid solution a hybrid nimble in dr thereby saving yourself some money so you know a hybrid flash array and adaptive flash array in dr that is fronted by SSD and ram but costs more like an HDD or a spinning disk array so HP is allowing us to do some things that help help save some money there as well alright Tim thanks very much it was a great conversation and really appreciate your perspectives all right thank you Dave 500 you're welcome ok thank you for watching everybody this is Dave a latte with cube we'll see you next time