>>Welcome to the cubes coverage of VMworld 2021. I'm Lisa Martin VJ ramen. Shannon joins me next VP of product management at VMware VJ. Welcome back to the program. >>Thank you. So >>We're going to be talking about disaster recovery, VMware cloud. Dr. We've had a lot of challenges with respect to cybersecurity, but the world has in the last 18 months, I'd like to get your, your thoughts on the disaster recovery as a service, the dearest market. What are some of the key trends? Anything that you've noticed have particular interest in the last year and a half? >>Yeah, actually you're right. I mean that the last one year, since the pandemic, you know, the whole, um, lot of industries want to, uh, deploy DLR systems and want to protect themselves in France, somewhere and other, uh, other areas of the Amazon predicting that the disaster service market is going to reach about $10 billion by 2025. And so we, uh, we introduced bandwidth disaster recovery, you know, the last beam work with an acquisition of a company called atrium. And since then we've had tremendous success and it was really largely driven by two key trends that we seen in the market. One is that a lot of our customers have regulatory and mandates to do have a PR plan in place. And second is ransomware and ransomware a lot more in this interview, but ransomware is top of mind for a lot of customers. So those, these two combined together is really making a huge push to, uh, to protect all the data against, uh, disasters. >>What type of customers and any particular industries that you see that are really keenly adopting VMware cloud and D anything that you think is interesting. >>Yeah, it's actually interesting that you say it's actually not a single vertical or a size of the customer. What we have again, what we're finding is that a lot of the regulated industries, I, you know, having 92 to do the art, but the existing VR and data production systems are extremely complex and not cost effective. So, you know, customers are asked to do more with less. And so a lot of our customers, a lot of those customers are asking for, uh, looking for a cost-effective way to protect all the data. And, you know, and ransomware is not something that, that impacts, you know, any single vertical or, or any single size of customer. It impacts everyone. So we're seeing interest from all different verticals, different sizes of customers, uh, across, uh, the, you know, the B cell this, >>Yeah, you're right. The ransomware is a universal problem. And as we saw in the last few months, a problem that is really one of national public health and safety and security concerns. So you mentioned that customers from a regulatory perspective, those that need to implement Dr. Ransomware, as we talked about, are there, and then you also mentioned legacy solutions are kind of costly complex. Talk to me about some of the challenges with respect to those legacy solutions that you're helping customers to address with VMware cloud disaster recovery. >>Yeah. There are a few traits of chains that are, uh, that are emerging and then the whole data production space. One is, uh, customers want to do more with the data. And so with legacy systems, what they're finding is that customers are, you know, are able to replicate the data, but the data is sitting idle and not being used. And so, um, you know, and that's extremely, very expensive for our customers on the line. And secondly, from an outpatient standpoint, backup and Dr, as kind of merging into a single single solution and ransomware protection is becoming a critical use case as we spoke about at the talk about for that. So, uh, customers are not looking to deploy different systems for different types of production. They're looking for a similar solution that, that the lowest cost and gives them enough production across all these different use cases. >>And so where the NFL disaster recovery comes into play is that, is that we are able to use the data that we protect for other uses such as, uh, such as ransomware recovery, such as data protection, such as disaster recovery. So single copy of data that's being could be used in multiple use cases. Number one. And secondly, uh, it's a very expensive, uh, proposition to have, um, you know, on-prem to on-prem, you know, having to, you know, people who shouldn't capacity just sitting idle. And so where Vizio comes into play is that they're able to use, uh, protect the data into cloud, store it in a cost effective manner, and then just use the data when it's acquired either fatal or during disasters in ransomware. And that's where you're able to in, in, in, in the market today, >>Dig through some of those differentiators, if you will, one by one, because there's so much choice out there, there's a lot of backup solutions. Some that are providing backup only some that are doing also Dr. Depending on how customers have deployed and how they're using the technology. But when you're in customer conversations, what are the three things that you articulate about VMware cloud DVR that really help it stand out above the pack? >>Yeah, number one is the cost, right? Um, we, you know, we're able to bring down the cost of, uh, of a disaster protection, uh, by 65, by 65%. And, uh, and, you know, um, that's one big value proposition that we, uh, that we know highlight in our solution. Number two, a lot of our customers also becoming environmentally friendly and, you know, and I'm in a conscious, I should say. And so, because we're able to store the data in a more cost-effective manner, in a more efficient manner in the cloud, they're able to bring down the carbon footprint by 80% compared to regular, you know, your legacy, uh, disaster recovery and data protection solution. And the third, you know, sort of major value proposition from, from, uh, from the BMS is that, you know, we're able to integrate the, uh, uh, BCDR solution, the disaster coriander data protection solution. So well into our, um, you know, into, into the ecosystem, uh, can easily operationally easily recover data into a BM ware cloud. And so for, for the BMA ecosystem, it just becomes a natural logical extension of their, uh, their, uh, toolset. >>That's huge having a console that you're familiar with, you know, the whole point of, of backing up data and the need to recover from a disaster is to be able to restore the data in a timely fashion. I talked with a lot of customers who were using legacy technologies, and that was one of the biggest challenges backup windows weren't completing, or they simply couldn't recover data that was either, um, lost in an, in a ransomware attack or accidentally lost that recovery is what it's all about. Right. >>That's it, that's exactly right. And so at this rainbow ledger using a key enhancements and features that specifically speak to that, uh, you know, to that pain point that you just mentioned, you know, uh, we are bringing down, uh, the, uh, you know, the replication time, uh, to 30 to 30 minutes. So in other words, your Delta is, is, is, uh, is at a 300 interval now compared to all us in a traditional backup system. And number two, um, we are extending, uh, you know, be in love with a copy of it regardless it's always had with single file recovery. And so, especially for the, for the ransomware, uh, use case customers are quickly able to figure out which file leads to the restore, and they're able to restore those files individually rather than restoring their entire VM for the entire data center. And so it becomes a critical, uh, use case for, uh, critical functionality, I should say, for a ransomware recovery. And the other huge announcement of a major announcement media announcement had been made, uh, uh, others be involved is the integration into the VMware cloud in such a way that customers who move are migrating data into the BMR, the cloud on AWS can, uh, have the opportunity to, um, uh, protect the data, um, you know, uh, you know, easily BCDR and >>Got it. I'd love to get an example of a customer that you helped to recover from ransomware. As we mentioned, it's on the rise. In fact, I was looking at some cybersecurity data in the last few weeks, and it's the first half of 2021 calendar. It was up nearly 11 ax. And obviously the, the, the hockey stick lists looking like it's going to continue to go up into the right. So give me an example of a customer that you helped recover after they were hit with ransomware. >>Yeah. Yeah, I lose. And in fact, before I give you one set, one statistic that I just saw recently, um, it is, um, every Lennon are going to be across the board. There's some ransomware attack and in the world. And so, uh, you know, it is a big, you know, it is a huge, huge top of mind for a lot of, uh, the CEO's across and I, you know, across the globe now, uh, we, I just give you an example of one customer that we helped, um, you know, protect the data against ransomware. Merrick is the customer name, uh, it's a public reference. It can, um, you know, it's, it's in the BMI website and they had legacy systems, just like we talked about before they had legacy systems for protecting the data and they had, you know, backup systems and they had disaster recovery systems. >>And the big pain point was that, you know, they knew that they are, you know, they needed to protect against ransomware and, but they had two different systems backup and disaster recovery, and their cost was high because they were replicating the light data or production data, uh, you know, across different sites. And so they were looking for a, uh, to lower the cost of disaster recovery, but more importantly, they're looking to, uh, to protect themselves against potential ransomware threats and, um, and they were able to deploy VCR. And how does multiple points in time? Um, you know, I, in, in, um, in the, in the cloud that are, that allows them to go to any point, uh, you know, uh, after a ransomware attack and record from it. And as I said, the single file recovery was a huge benefit for them because they can then figure out exactly which, you know, which of those files, uh, you know, required, um, recovery. And so, um, they're able to lower the cost and protect, uh, and at the same time, uh, you know, meet the regulatory requirements and mandates to have a production in place so that the women all up there in all over the place, >>As you said, there, the data show one ransomware attack occurs every 11 seconds. And of course we only hear about the ones that make the news, right, for the most part, our customers talk about, Hey, we've had this problem. So it is no longer a, if we get hit with ransomware for every industry, like you were saying before, no industry is blind to this. It's when we get hit, we've gotta be able to recover the data. It sounds like what you're talking about from a recovery perspective is it's, it's very granular. So folks can go in and find exactly what they're looking for. Like, they don't have to restore entire VM. They can go down to the file level. >>That's exactly right. And, and you need the grant of the recovery because you want to be able to quickly restore, you know, your data, uh, and get back on, uh, you know, get back in the business. And so, uh, we provide that granular, granular recovery at the file level so that you can quickly scan your data, figure out which file needs to be at least a bit of cover and recollect just those files. Of course, you can also the color. We also provide authorization for the whole data center for the whole, uh, you know, BM and all the beings in the data center, but customers when they hit the trends and where they want to be able to quickly get back, get back into production, to those flights that, you know, that they critically need. And so that's, um, yeah, that's, it's a critical functionality. >>So is this whole entire solution in the cloud, or is there anything that the customer needs to have on premise? >>So this is, uh, all the data is go to the cloud in an efficient day, in an efficient way. Again, uh, you know, this is another sort of, um, like be that behalf, which is it's easy to just store data in the cloud in a debate, but what we do is be efficiently store the data so that, you know, you, uh, you know, you can know what the cost of your storage and, uh, uh, in the cloud. And so, you know, we used to be at BCDR, we'll be in the cloud disaster recovery. Those data in the cloud is, uh, and, and, and the data repository is in the cloud. And, uh, you can either recover data back to where you need to recover, or we allow filo or orchestrate automatically feel or of, uh, workloads into VMware on AWS, again, operational consistent, because it's a BMI software that's running on ground BMI software, that's running on data and you can, um, you know, fail a lot and bring the data onto the in-vitro Needham, VSO. It's, uh, uh, it's, uh, you know, and it's all there to look for SAS customer customer doesn't have to really manage anything on prem fuel, >>Which must've been a huge advantage in the last year and a half when it was so hard to get to the on-prem locations. Right. >>That's exactly right. And this is one of the clear differentiators, you know, against, uh, you know, with, um, uh, compared to the legacy systems, because in legacy backup and disaster recovery systems, you need to manage your, not just your target tourists, but also, you know, the Asians and, you know, all the stuff that, uh, uh, all the software that goes along with that, uh, data production and, uh, and the disaster recovery solution. And so by T and Matt upgrades and patches and so on. And so what we do with, with a SAS based approach is take away that burden away from customer. So we deliver this entire service as a SAS first as a cloud service first, um, uh, delivery mechanisms of customers are don't have water. You don't have to whatever any of those things. >>And that's critical, especially as we've seen in the last 18 months with what's been going on the challenge of getting to locations, but also what's been happening as we talked about in the cybersecurity space, on the increase, the massive increase in ransomware. Talk to me a little bit about, I want to dig in before we go about some of the ways that you've simplified and integrated the way to backup VMware cloud on AWS. Talk to me a little bit more about some of those enhancements specifically. Yeah, >>Yeah. So, um, a lot of the customers, customers, as you know, are, uh, you know, have a dual pronged approach where they have, you know, some workloads running on prem and they have some workloads running and the VMware cloud on AWS and for BNB, uh, for VMs that are running on VMware cloud on AWS. Um, you know, now they have a choice of, uh, of protecting, protecting the data and the VM very simply, uh, using the McLaurin disaster cloud disaster recovery. And what that means is that they don't need to have the full band BR solution, but they can simply protect the data and automatically restore and recover of data. If they, you know, if there's a corruption or something goes wrong with their, uh, you know, the beans, they can simply restore the data without going through an entire field processes. So we provide a simplified way for customers to automatically protect data, and then that are running on VMware cloud on AWS. And that's a, and it's fully integrated with our cloud on AWS, you know, workflows. And, um, and so that's a great win for anyone who's, who's migrating data man workloads into BMC >>Is the primary objective of that to deliver a business resiliency. Dr. >>Both actually that's, that's, that's, that's a great part about that. You know, that's a bit part of the solution is that customers don't have to choose between Dr and business resiliency. They get both with a single solution. They can start off, it's a specific business resiliency and protecting the data, but if they choose to, they can them, uh, you know, add BR as well to that, to those workflows. And so it's not either, or it's both. >>Excellent. Got it. Any other enhancements that you guys are announcing at the Emerald this year? >>Yeah. I just want to reiterate the announcements and the key enhancements and the making, making, uh, you know, the balancing beam. Well, um, the first one, as I said is, uh, uh, is 30 minutes RPO. So customers that are business critical workloads can now pro protect the data and be guaranteed that they're, you know, the, the, you know, the demo data, the data that they, um, you know, they lag behind it's, it's in the 30 minute range and not in the other screens, like with other legacy backup solutions. That's one. The second is the integration, uh, as all enhancements that, you know, that I just talked about for ransom recovery, single file, thin file restore. Um, they always had, you know, number of snapshots and, you know, failure was and so on, but silverish was a key and that's what they've been making for a ransomware recovery. And the third one is the integration with BNB coordinator. So the fully integrated solution and provides a simple, you know, sort of plug and play solution for any workload that's funding in being AWS. Those are the three Tiki announcements. There's a lot more in, um, in the world. So you'll see that in the coming weeks and months, but these are the three on to get the input, >>A lot of enhancements to a solution that was launched just about a year ago. VJ, thank you for sharing with us. What's new with VMware cloud DVR, the enhancements, what you're doing, and also how it's enabling customers to recover from that ever pressing, increasing threat of ransomware. We appreciate your thoughts and likewise for VJ Ramachandra and I'm Lisa Martin, you're watching the cubes coverage of VMworld 2021.

>>From around the globe with digital coverage of 2020 brought to you by beam. Welcome back. I'm assuming a man, and this is the cubes coverage of Veem on 2020 online. I'm really happy to welcome to the program. We had done the Milan many years, first time doing it online and we have two first time guests. the center square. We have Daniel freed. He is the GM and senior vice president of AMEA and the head of worldwide sitting on the other side of the screen. Is it David Harvey? He's the vice president of Dietrich alliances. Both of them, of course, with beam. Gentlemen, thanks so much for joining us. >>Thank you. >>All right, Daniel, maybe start with you, uh, you know, the online event, obviously, uh, you know, it gives us, you know, there's some allergens, but there's also some opportunities rather than, you know, thousands of us gathering in Las Vegas where right. There's a diversity of locations because if you look up and down the street, the strip, um, and instead we really have a global event in an operation, unity, I'm speaking to you where you are in Asia right now. What, what is, you know, the online event mean? And you know, how you can relate to, you know, how many countries do you have a attending the event. Okay. Yeah. >>Okay. So, so the good, the good thing about, about being online is, as you mentioned, as you said, is, is we can have all, all people from all countries, all around the world present. Of course we are surely, uh, now with my responsibility, my worldwide responsibility for the channels, uh, all countries in the world, we have partners of all in all countries in the world, which means that all our teams, as well as all our butlers are virtual things or the kid limits, uh, of, of joining that, that event today. But that's, that's why I'm very, very happy to have these virtual events, which is much easier. And they're heading all people try flying in from all the different parts of the world, do they guess? Right. And, and, and David, you know, also with alliances standpoint, I assume since, you know, they don't actually have to fly to Vegas. We've got the special guest appearances by Satya Nadella, uh, you know, Arvin, Krishna, you know, all of the, you know, Andy Jassy, you know, everyone's coming in, but no, and also seriousness from an Alliance standpoint, uh, you know, we'd love to hear how you're, you're working with them., uh, for, for the global event. >>Yeah, no, absolutely. And security is having a tough time keeping them at Bay right now. I mean, the online thing is handy because we can just cut them off, but, uh, yeah. Uh, but you're exactly right. It, the support of the alliances has been fantastic. Uh, everyone was trying to adjust to this new world we're in, but what you're seeing this week, um, he's a fantastic mom's body alliances. So once in Mike, all items should really work and we're doing the same for their events. And it's just a really nice >>If >>Camaraderie is coming together. And so, um, they've been great in supporting us as you've as seen through the week. Um, and we're excited about know whole vibe that getting in a commitment >>That, that we're getting from the customers I'm from the alliances, which is really, really good. Excellent. Well, we know that, you know, Veeam is a hundred percent partner focus, Daniel, maybe let's start with you, uh, you know, what, what's new kind of in the last year. So since we were together, last year, so on the new, on the new things that we have been doing for the last year, it's actually continuing first to move with our hundred percent, uh, since the beginning of, of, of Veem and all the way to the fully do squatters, that's more important even that is definitely the move that we see, uh, with working with your answers, uh, and their partners, as well as working much more with the Saudis providers, meaning the cloud service providers, where are there is a big, big trend now in the market with customers requesting more and more rather than, than I would say, technologies and products on premise. >>Uh, so we see that everywhere around the world. It is actually writing now again with the nutrition that we see, well, why, because of these, Nope, this is about situation, uh, where virtual is a big move that we, uh, we, we can see from customers and the partners that we have, the ecosystem that we've built, um, all around the world, he's helping very much in this move. Excellent. And David would love to hear the, the, the progress that, uh, your group with some of the parts. Yeah, absolutely. I mean, it's been a, it's been a really exciting ride, uh, year over year growth rates with the alliances, continue to shoot out, which we're really excited about. Um, the VTN launch was fantastic for us for most of our major strategic alliances. So we're really pleased about that. And a lot of our technical alliances as well, they really benefited from some of the new capability coming out there. >>So what we're seeing is not only are we seeing our go to market, be enriched more and have a lot of success with the strategic alliances, the technology Alliance is a really starting to benefit from some of that new innovation that just came out and funny as well. So that global systems integrators, we've seen a massive uptick in that interest in the last, in the last couple of quarters. And that's really helping too Alison tonight. Oh, I spy. So yeah, it's been a really exciting year. And certainly when you do these types of events virtually yeah. LinkedIn, your, I am, and text messages go through the roof, which is a nice way to, to keep communication with the alliances. Yeah, I did. David, I'd like to just drill in a little bit on some of the pieces that you're talking about there, uh, you know, I really feel in the last year, yeah. We saw a real maturation in what we do talk about. Yeah. Hybrid cloud and multicloud. Um, I, I know one of the, you know, key strategic Alliance is actually from day one for Veem. Yeah. And you know, every time I saw an announcement of some of the VMware Bob pieces, I usually felt like there was soon after a Veem piece of it. Uh, could you bring us inside a little bit, especially some of the cloud pieces and maybe how beam differentiate, uh, from, from some of the competition out there, you know, both VMware, >>You know, Amazon, Microsoft and that whole ecosystem. >>Yeah, absolutely. I mean, as you touched on, uh, VMware and ops have been very close, Brown is process, and we're really excited about, uh, some of the recent work has been going on with them as well. Um, we're also have tremendous steps fools with Amazon that continues to be a strong area. And the Microsoft is a cloud in the way that we continue during the harms, the way we work with their solution. Um, it's really providing right strides forwards, especially for the enterprise customers. Uh, we also were excited about the recent announcement related to Google cloud as well. So that's another big area for us. Um, and so that was another thing that continues to differentiate us. And what I would say overall though, is it's about having that philosophy as customers continue to have there philosophical view related to on premise cloud on off premise cloud. >>What we're showing is whether it's through the hardware partners, whether it's through the application partners well through the cloud is we're enabling you to decide your workflows. And I think that's the bit it's a little bit different than, and some of the others that are out there taking that heritage, should we put into the virtual world and that mentality, there's certain it departments have. It enables us to really synergize with those different partners as they go through their evolution and a certain customers move more towards the public cloud. And then you might be look towards some workplace back to the private that synergy between all of those areas is hugely important. And even for the hardware partners that we have, do you have cloud plays, mentioning some of their value solutions as well. So it's a really sort of, um, heterogeneous world that it we're really pleased on the way that the market is accepting it. Yeah. And Daniel that this, this move and a maturation of what's happened in the cloud is a significant impact on the channel. I'd love to hear, you know, anything specifically, you know, with your, uh, your viewpoint on the channel as to, you know, how your partners are now adjusting to that, you know, VMware, Microsoft, uh, some of the other pieces is that how they are now ready, uh, to help customers, uh, through these transitions. >>Yeah. And, and let me, let me make one run back, which is very important. First of all, VIM is not Mmm. The cloud provider and will not be accepted, right. Or in other words, the idea is that we will never compete with our brothers, never. Uh, so we provide technology, which is used by our partners and a number of them. I just think that technology to provide services, a number of them are using this technology to resell, uh, or to implement some additional services for the customers. And this is a key, key element. We're not there to do anything and competition. We are here to compliment and to use it, to leverage as much as possible, all our partners, as much as we can, uh, they know very good the market, they know very good at how things are moving. They know very good where they can do they know very good where they cannot do and what their customers want or, or, Oh,. >>Um, so the big, big move that we see in the market is how everyone is moving more and more to, again, there's said initially, uh, to the cloud, um, I mean, providing cloud services, whether it's multicloud hybrid cloud, as you mentioned it, as you listed them, we have all different types of scenarios. And this is a very interesting thing, is us helping them, educating them on how to use our technology, to be able to verify we be provide services and capabilities to their end customers. So we have a big, big investments in this enablement in what we call sales acceleration software, because it's all about businesses, uh, and helping our partners to get there and to move them as fast as placebo. Again, there is a big, a big need, a big request from the end customers and the role of the partners. I understand that and have to move very quickly to this new world of services. >>And we are there to help and support because we strategically no, that this is a way not only for him, but for the entire market. Yeah. And Danielle, you know, an important point. I think anybody that thinks that, okay, editor, uh, you know, to the channel or things, you know, probably doesn't matter. Okay. Or value proposition, a Veeam. What I'm curious from your standpoint is what was the impact of know wire now? You know, obviously some management changes there. Uh, I'm, I'm curious what feedback you've gotten and how that impact, uh, you know, the channel first. Yeah. I mean, let's be open as you know, it's one of, I hope one of our qualities, that theme is the transparency and the way we communicate again with the world, with our, especially with our partners. So initially the feedback that I had and with a number of partners and partners, well, a little bit of, okay, Nope, no worries. >>Uh, no, no. What is going to happen? What is next? Are we going to, to lose the DVM culture? Are we going to, are we going to go through a number of changes eventually in the strategy of him? And actually I have to say, and I'm extremely comfortable, uh, in my, let's say regular communications and connections with, with the insight partners, we have quiet team software because they think that the strategy that we had and the strategy that we have now is the strategy they want just to keep on doing, because it is a successful strategy. And by the way, when we do get the data, uh, that we got from the market from, uh, from, from some, from IDC that that was out lately, we see that Veeam is the number one in both, all around the world, compared to all the other vendors, doing the same kind of technology. >>That means that each is a successful strategy going with the partners and through the partners, he's a very successful strategy. And there is no reason that that yeah, and insight partners understands that extremely good. And I feel very comfortable with it. Yeah. With our future. That would mean more to us, but that's okay. We'll see. In the coming quarters. Well, I, I think, uh, you know, we, we, we do need to have, make sure that VMs has a little bit more focused on getting some green in your home environments there. Um, cause normally if I'm doing an interview with green, I'm expecting with BMI Mexican and a little bit more of the, of the breaker in there, David, you know, obviously, you know, the strategic alliances, uh, you know, some of those executive relationships, good morning, bring us in a little bit, as you know, Daniel was saying there's a little bit yeah. >>Of trepidation at the bit. And they've worked ruin, uh, from the Alliance standpoint, uh, you know, what is this, uh, what what's, what's transpired. Yes, true. It's, it's one of those things. It's a really unexciting answer because they aren't similar, simple answers calmness. Um, I often 24 hours, once we announced it, my call sheet was pretty, pretty empty for the simple reason being that, uh, we've spoken to everybody very quickly and the resonant feedback was that's great news. We know insight. We trust insight. We're glad it is say a growth play. Uh, also it clears up the future. And obviously, yeah, when you have strategic alliances is always in the back of their mind, wondering when is one of our competitors going to come in and Acqua you guys Mmm. Your article feedback was, this is fantastic. This is exactly what we wanted to see. >>Um, you provide clarity to our partnership. You can continue to invest in grow, which you've demonstrated for years, and you can move that forward for the next few years. Um, but also more importantly, this enables us to feel even better doubling down on veins. And so frankly, while we haven't had any issues and I'm sure a lot of the viewers out there have been through events seeing sometimes that can be crazy. It's a Daniel was pointing the strategy. Hasn't changed, we're executing, we've got the support. And the strategic Alliance is probably for the executive level and also the day to day level on leaning in more and more of them please that we're executing on our strategy, focusing in the U S with a big push. Okay. Bringing the investment, moving forward, stabilizing the leadership team. It's just been overall. It's been fantastic. So yeah. >>Yeah. It's, it's a really unexciting new soundbite answer, but that's a, how long has inclarity clarity has been a real takeaway? Excellent. Well, one of the, the key messages in the keynote, of course talking about a digital transformation, we'd love to hear, uh, for, from both of you, uh, you know, what you're seeing and hearing how beam's message is a, you know, engaging with both partners and ultimately the, the end user itself, uh, Daniel, maybe we'll start with you on that. Yeah. Yeah. Thanks. Thanks for asking. It's usually always comes from the end customers and their needs, and we all know that the need for data uh he's he's getting exponential. Uh, so that is why we can't do things manually anymore. So it has to be digitalized everywhere. Yeah. The very interesting thing is that not only something that express with the end customers, but we see more and more because it's an absolute need. Uh, when partners are providing, uh, services or providing all night, chubby she's out services or providing even, even products, they have digitalize also themselves. They are doing it at very, very high speed. But I know I'm mentioning that because I'm extremely pleased with the ecosystems of partners that we have >>Because they understand it's very good, how the market is, is evolving. I'm still only about the customers, but it's also about themselves. Yeah. That they are evolving 21st. And did you digitalization of all the processors? Well, the way they work with their customers, it's definitely one of the key elements, uh, which is going to be extremely good for the future. That's why, because of all this moves in a very positive dynamic way, there is no reasons why we should change our strategies and no remaining said our rights, uh, lions first, whatever it is, uh, continue driving the ecosystem, building the ecosystems, organizing the acquisition. And he's absolutely key for the success of everyone, including people, Brittany and David, please from the Alliance side. Yeah, it's do, I'm sure you'll notice, but in anybody and, uh, we're in a fortunate situation that we probably both get to sit through, uh, all of the strategies that a lot of the Titans of industry are all focused on right now and, and, and having ecosystem we do in your line side, that rich tapestry from the very large to very small is focused on that digital transformation. >>And I think that the good news from my point of view, and I'm going to touch on one of the points Daniel mentioned before was we don't eat with them. And so, yeah, he volunteers, we've got his work hogging, a piece of that, the strategy that they're looking for, the criticality of data three is transformation is huge as everybody knows. Um, and what we're finding right now is that the approach that we take yep. Approach to focus on doing what we do extremely well is synergizing with the evolution of the customer is seeing as they go through that transformation and transformation, sometimes a scary transformation sometimes brings nervousness and they want to do it with a lot of their thought leaders. They working with the VM-ware has the Microsoft, the HBS, and then apps, et cetera. And so from that point of view, the fact that we can providing them with that peace of mind for the complete solution, it's been fantastic. >>So, you know, when you look at a 75 plus partners, there's always going to be one way you need to thread the needle. Shall we say on exactly where intellectual property provides that value to them? But the good news is we don't have to spend a lot of time on that because we're clear, we're concise. Uh, and a lot of times they've been involved in a lot of our strategy sessions. So they're on board with us. And I think the Daniels area as well with the channel, the channel sees that as well. And that's why, whether it's through the alliances channel or with us directly to the resellers, uh, we're finding that, uh, that harmony is bringing a lot of peace of mind. So you can focus on the pains of the customer. I'm not worried about your technology partners fighting with themselves. And that's really where we are, right. Uh, the overall ethic of the company. All right. Well, the final item I have for, for both of you is, you know, normally, you know, but we have a certain understanding of where we are and what the roadmap is. Look, of course, we're dealing with a global pandemic, right? So >>As we look forward to the outlook, uh, I'd love to be able to hear a little bit about, you know, what you're hearing from your partners, how that is coloring, you know, decisions that are made really for the rest of kind of the next 12 months or so. Um, and you know, okay. Any other data points that you have, uh, from your broad perspectives as to how people think the recovery is going to be know, obviously we understand there's a lot of inserts. Nope. Daniel, you've got a, uh, great global viewpoint. We understand, uh, you know, what, what is happening impacts differently locally quite a bit, but, um, what are you seeing going forward and do you know the impact? Bye bye. Yeah. So I couldn't say the contrary. Yeah. So they correct. And we see it in our numbers that the countries, which are the most impacted, I would buy the QVC. >>I would have been more difficulties than the others, uh, to move, to move forward for a business standpoint, uh, which everybody understands, but we've received in the numbers. No, the thing. And this is what I liked very much about, but our ecosystem and where is we had a plan, uh, that we said that we said in 2019 before we knew anything about curvy a con for 2020, and you know what, uh, we are now in no, in, in, in our, the second part of the month of the year, you too, and are going to make our numbers. We are going to make our plans and why are we going to make it? That's the only because, you know, it's just been because perfect, but he's very, very much because of all our partners who, despite all the issues that are, they are in country because of coverage are just getting there, biking, helping themselves, helping us, and altogether as, as a big business machine, as big business system, we all just making success. >>And this will only show extremely good at the end of the year. When we look at the market share, Jamie's going to gain again, uh, with all our butters, it will be the, the results of the success. So good results. Very good results. No. And, and do you mean just continuing to move with these, he's a network of fathers and David, obviously we've seen, you know, you know, many of the big partners, you know, uh, you know, very circumstance and their response, you know, nobody wants, are you seen as, uh, you know, doing something that is untoward towards customers taking care of business. Okay. So, you know, how how's this impacting, you know, what you're doing with your partners? And it gives a little bit of the outlook going forward. Yeah. I mean, why not use for this as energy? Mmm. Some of these headlines that you see, of course, they're not going to get picked up with the impact related to it on a day to day basis, through the discussions with the executives are in the field level, we're seeing the energy with same people want to make sure on what is a tricky situation was a very impactful situation. >>Um, but what, we're not seeing people Mmm. He was onto it. We're seeing people really want to, um, make sure that they are also relating to the needs of their customers today, whether it's more and point whether it's moving towards the user experience, but also taking this time to keep building the foundation for a lot of that infrastructure related to data protection, data availability, um, that we've enjoyed for a long period of time. So yeah, you know, you, you have a degree of disruption, but the objective that I'm seeing from all the major guys that are out there is let's make sure we drive hard. Let's not take the pedal off the metal. Let's not use this as an excuse. Let's keep moving. What, uh, I mean, I sh I would say our engagement with them has increased in sort of happened. Um, and so I don't think we ever expected to be running into tempo. >>We're running bean does it as standard, but we don't normally I have that same temperature. Okay. From some of the, uh, some of the alliances we're really pushing hard with him. So, yeah, we're excited. And we continue to evolve rudeness how, in a situation, everyone's going to be employees with a lot of aggression, a lot of desire to keep capitalizing on the work we've done together. The key solving the customer demands that are going to come over the next 18 to 24 months, um, and reading, make sure that, uh, this is really okay. Yeah. It's impactful just to be clear, but, but not one that we're going to let define our future. I'm looking into that together. So I think from us, um, we're excited about not only as Daniel said, beam success. Well, what, we're starting to see us really good attitudes, uh, from all of our lines bombs, which we love. Yeah. All right. Well, Daniel and David, thank you so much for the update. Great. Yep. Okay. Thank you. Thanks. All right. Lots more covered from Veeam on 2020 online. I'm assuming a minute. Thank you. Oh, wow. The cube.

>> Narrator: Live from Boston, Massachusetts, this is The Cube, covering Spark Summit East 2017. Brought to you by Data Bricks. Now, here are your hosts, Dave Valente and George Gilbert. >> Boston, everybody. Nick Pentry this year, he's a principal engineer a the IBM Spark Technology Center in South Africa. Welcome to The Cube. >> Thank you. >> Great to see you. >> Great to see you. >> So let's see, it's a different time of year, here that you're used to. >> I've flown from, I don't know the Fahrenheit's equivalent, but 30 degrees Celsius heat and sunshine to snow and sleet, so. >> Yeah, yeah. So it's a lot chillier there. Wait until tomorrow. But, so we were joking. You probably get the T-shirt for the longest flight here, so welcome. >> Yeah, I actually need the parka, or like a beanie. (all laugh) >> Little better. Long sleeve. So Nick, tell us about the Spark Technology Center, STC is its acronym and your role, there. >> Sure, yeah, thank you. So Spark Technology Center was formed by IBM a little over a year ago, and its mission is to focus on the Open Source world, particularly Apache Spark and the ecosystem around that, and to really drive forward the community and to make contributions to both the core project and the ecosystem. The overarching goal is to help drive adoption, yeah, and particularly enterprise customers, the kind of customers that IBM typically serves. And to harden Spark and to make it really enterprise ready. >> So why Spark? I mean, we've watched IBM do this now for several years. The famous example that I like to use is Linux. When IBM put $1 billion into Linux, it really went all in on Open Source, and it drove a lot of IBM value, both internally and externally for customers. So what was it about Spark? I mean, you could have made a similar bet on Hadoop. You decided not to, you sort of waited to see that market evolve. What was the catalyst for having you guys all go in on Spark? >> Yeah, good question. I don't know all the details, certainly, of what was the internal drivers because I joined HTC a little under a year ago, so I'm fairly new. >> Translate the hallway talk, maybe. (Nick laughs) >> Essentially, I think you raise very good parallels to Linux and also Java. >> Absolutely. >> So Spark, sorry, IBM, made these investments and Open Source technologies that had ceased to be transformational and kind of game-changing. And I think, you know, most people will probably admit within IBM that they maybe missed the boat, actually, on Hadoop and saw Spark as the successor and actually saw a chance to really dive into that and kind of almost leap frog and say, "We're going to "back this as the next generation analytics platform "and operating system for analytics "and big debt in the enterprise." >> Well, I don't know if you happened to watch the Super Bowl, but there's a saying that it's sometimes better to be lucky than good. (Nick laughs) And that sort of applies, and so, in some respects, maybe missing the window on Hadoop was not a bad thing for IBM >> Yeah, exactly because not a lot of people made a ton of dough on Hadoop and they're still sort of struggling to figure it out. And now along comes Spark, and you've got this more real time nature. IBM talks a lot about bringing analytics and transactions together. They've made some announcements about that and affecting business outcomes in near real time. I mean, that's really what it's all about and one of your areas of expertise is machine learning. And so, talk about that relationship and what it means for organizations, your mission. >> Yeah, machine learning is a key part of the mission. And you've seen the kind of big debt in enterprise story, starting with the kind of Hadoop and data lakes. And that's evolved into, now we've, before we just dumped all of this data into these data lakes and these silos and maybe we had some Hadoop jobs and so on. But now we've got all this data we can store, what are we actually going to do with it? So part of that is the traditional data warehousing and business intelligence and analytics, but more and more, we're seeing there's a rich value in this data, and to unlock it, you really need intelligent systems. You need machine learning, you need AI, you need real time decision making that starts transcending the boundaries of all the rule-based systems and human-based systems. So we see machine learning as one of the key tools and one of the key unlockers of value in these enterprise data stores. >> So Nick, perhaps paint us a picture of someone who's advanced enough to be working with machine learning with BMI and we know that the tool chain's kind of immature. Although, IBM with Data Works or Data First has a fairly broad end-to-end sort of suit of tools, but what are the early-use cases? And what needs to mature to go into higher volume production apps or higher-value production apps? >> I think the early-use cases for machine learning in general and certainly at scale are numerous and they're growing, but classic examples are, let's say, recommendation engines. That's an area that's close to my heart. In my previous life before IBM, I bought the startup that had a recommendation engine service targeting online stores and new commerce players and social networks and so on. So this is a great kind of example use case. We've got all this data about, let's say, customer behavior in your retail store or your video-sharing site, and in order to serve those customers better and make more money, if you can make good recommendations about what they should buy, what they should watch, or what they should listen to, that's a classic use case for machine learning and unlocking the data that is there, so that is one of the drivers of some of these systems, players like Amazon, they're sort of good examples of the recommendation use case. Another is fraud detection, and that is a classic example in financial services, enterprise, which is a kind of staple of IBM's customer base. So these are a couple of examples of the use cases, but the tool sets, traditionally, have been kind of cumbersome. So Amazon bought everything from scratch themselves using customized systems, and they've got teams and teams of people. Nowadays, you've got this bold into Apache Spark, you've got it in Spark, a machine learning library, you've got good models to do that kind of thing. So I think from an algorithmic perspective, there's been a lot of advancement and there's a lot of standardization and almost commoditization of the model side. So what is missing? >> George: Yeah, what else? >> And what are the shortfalls currently? So there's a big difference between the current view, I guess the hype of the machine learning as you've got data, you apply some machine learning, and then you get profit, right? But really, there's a hugely complex workflow that involves this end-to-end story. You've got data coming from various data sources, you have to feed it into one centralized system, transform and process it, extract your features and do your sort of hardcore data signs, which is the core piece that everyone sort of thinks about as the only piece, but that's kind of in the middle and it makes up a relatively small proportion of the overall chain. And once you've got that, you do model training and selection testing, and you now have to take that model, that machine-learning algorithm and you need to deploy it into a real system to make real decisions. And that's not even the end of it because once you've got that, you need to close the loop, what we call the feedback loop, and you need to monitor the performance of that model in the real world. You need to make sure that it's not deteriorating, that it's adding business value. All of these ind of things. So I think that is the real, the piece of the puzzle that's missing at the moment is this end-to-end, delivering this end-to-end story and doing it at scale, securely, enterprise-grade. >> And the business impact of that presumably will be a better-quality experience. I mean, recommendation engines and fraud detection have been around for a while, they're just not that good. Retargeting systems are too little too late, and kind of cumbersome fraud detection. Still a lot of false positives. Getting much better, certainly compressing the time. It used to be six months, >> Yes, yes. Now it's minutes or second, but a lot of false positives still, so, but are you suggesting that by closing that gap, that we'll start to see from a consumer standpoint much better experiences? >> Well, I think that's imperative because if you don't see that from a consumer standpoint, then the mission is failing because ultimately, it's not magic that you just simply throw machine learning at something and you unlock business value and everyone's happy. You have to, you know, there's a human in the loop, there. You have to fulfill the customer's need, you have to fulfill consumer needs, and the better you do that, the more successful your business is. You mentioned the time scale, and I think that's a key piece, here. >> Yeah. >> What makes better decisions? What makes a machine-learning system better? Well, it's better data and more data, and faster decisions. So I think all of those three are coming into play with Apache Spark, end-to-end's story streaming systems, and the models are getting better and better because they're getting more data and better data. >> So I think we've, the industry, has pretty much attacked the time problem. Certainly for fraud detection and recommendation systems the quality issue. Are we close? I mean, are we're talking about 6-12 months before we really sort of start to see a major impact to the consumer and ultimately, to the company who's providing those services? >> Nick: Well, >> Or is it further away than that, you think? >> You know, it's always difficult to make predictions about timeframes, but I think there's a long way to go to go from, yeah, as you mentioned where we are, the algorithms and the models are quite commoditized. The time gap to make predictions is kind of down to this real-time nature. >> Yeah. >> So what is missing? I think it's actually less about the traditional machine-learning algorithms and more about making the systems better and getting better feedback, better monitoring, so improving the end user's experience of these systems. >> Yeah. >> And that's actually, I don't think it's, I think there's a lot of work to be done. I don't think it's a 6-12 month thing, necessarily. I don't think that in 12 months, certainly, you know, everything's going to be perfectly recommended. I think there's areas of active research in the kind of academic fields of how to improve these things, but I think there's a big engineering challenge to bring in more disparate data sources, to better, to improve data quality, to improve these feedback loops, to try and get systems that are serving customer needs better. So improving recommendations, improving the quality of fraud detection systems. Everything from that to medical imaging and counter detection. I think we've got a long way to go. >> Would it be fair to say that we've done a pretty good job with traditional application lifecycle in terms of DevOps, but we now need the DevOps for the data scientists and their collaborators? >> Nick: Yeah, I think that's >> And where is BMI along that? >> Yeah, that's a good question, and I think you kind of hit the nail on the head, that the enterprise applied machine learning problem has moved from the kind of academic to the software engineering and actually, DevOps. Internally, someone mentioned the word train ops, so it's almost like, you know, the machine learning workflow and actually professionalizing and operationalizing that. So recently, IBM, for one, has announced what's in data platform and now, what's in machine learning. And that really tries to address that problem. So really, the aim is to simplify and productionize these end-to-end machine-learning workflows. So that is the product push that IBM has at the moment. >> George: Okay, that's helpful. >> Yeah, and right. I was at the Watson data platform announcement you call the Data Works. I think they changed the branding. >> Nick: Yeah. >> It looked like there were numerous components that IBM had in its portfolio that's now strung together. And to create that end-to-end system that you're describing. Is that a fair characterization, or is it underplaying? I'm sure it is. The work that went into it, but help us maybe understand that better. >> Yeah, I should caveat it by saying we're fairly focused, very focused at HTC on the Open Source side of things, So my work is predominately within the Apache Spark project and I'm less involved in the data bank. >> Dave: So you didn't contribute specifically to Watson data platform? >> Not to the product line, so, you know, >> Yeah, so its really not an appropriate question for you? >> I wouldn't want to kind of, >> Yeah. >> To talk too deeply about it >> Yeah, yeah, so that, >> Simply because I haven't been involved. >> Yeah, that's, I don't want to push you on that because it's not your wheelhouse, but then, help me understand how you will commercialize the activities that you do, or is that not necessarily the intent? >> So the intent with HTC particularly is that we focus on Open Source and a core part of that is that we, being within IBM, we have the opportunity to interface with other product groups and customer groups. >> George: Right. >> So while we're not directly focused on, let's say, the commercial aspect, we want to effectively leverage the ability to talk to real-world customers and find the use cases, talk to other product groups that are building this Watson data platform and all the product lines and the features, data sans experience, it's all built on top of Apache Apache Spark and platform. >> Dave: So your role is really to innovate? >> Exactly, yeah. >> Leverage and Open Source and innovate. >> Both innovate and kind of improve, so improve performance improve efficiency. When you are operating at the scale of a company such as IBM and other large players, your customers and you as product teams and builders of products will come into contact with all the kind of little issues and bugs >> Right. >> And performance >> Make it better. Problems, yeah. And that is the feedback that we take on board and we try and make it better, not just for IBM and their customers. Because it's an Apache product and everyone benefits. So that's really the idea. Take all the feedback and learnings from enterprise customers and product groups and centralize that in the Open Source contributions that we make. >> Great. Would it be, so would it be fair to say you're focusing on making the core Spark, Spark ML and Spark ML Lib capabilities sort of machine learning libraries and in the pipeline, more robust? >> Yes. >> And if that's the case, we know there needs to be improvements in its ability to serve predictions in real time, like high speed. We know there's a need to take the pipeline and sort of share it with other tools, perhaps. Or collaborate with other tool chains. >> Nick: Yeah. >> What are some of the things that the Enterprise customers are looking for along the lines? >> Yeah, that's a great question and very topical at the moment. So both from an Open Source community perspective and Enterprise customer perspective, this is one of the, if not the key, I think, kind of missing pieces within the Spark machine-learning kind of community at the moment, and it's one of the things that comes up most often. So it is a missing piece, and we as a community need to work together and decide, is this something that we built within Spark and provide that functionality? Is is something where we try and adopt open standards that will benefit everybody and that provides a kind of one standardized format, or way or serving models? Or is it something where there's a few Open Source projects out there that might serve for this purpose, and do we get behind those? So I don't have the answer because this is ongoing work, but it's definitely one of the most critical kind of blockers, or, let's say, areas that needs work at the moment. >> One quick question, then, along those lines. IBM, the first thing IBM contributed to the Spark community was Spark ML, which is, as I understand it, it was an ability to, I think, create an ensemble sort of set of models to do a better job or create a more, >> So are you referring to system ML, I think it is? >> System ML. >> System ML, yeah, yeah. >> What are they, I forgot. >> Yeah, so, so. >> Yeah, where does that fit? >> System ML started out as a IBM research project and perhaps the simplest way to describe it is, as a kind of sequel optimizer is to take sequel queries and decide how to execute them in the most efficient way, system ML takes a kind of high-level mathematical language and compiles it down to a execution plan that runs in a distributed system. So in much the same way as your sequel operators allow this very flexible and high-level language, you don't have to worry about how things are done, you just tell the system what you want done. System ML aims to do that for mathematical and machine learning problems, so it's now an Apache project. It's been donated to Open Source and it's an incubating project under very active development. And that is really, there's a couple of different aspects to it, but that's the high-level goal. The underlying execution engine is Spark. It can run on Hadoop and it can run locally, but really, the main focus is to execute on Spark and then expose these kind of higher level APRs that are familiar to users of languages like R and Python, for example, to be able to write their algorithms and not necessarily worry about how do I do large scale matrix operations on a cluster? System ML will compile that down and execute that for them. >> So really quickly, follow up, what that means is if it's a higher level way for people who sort of cluster aware to write machine-learning algorithms that are cluster aware? >> Nick: Precisely, yeah. >> That's very, very valuable. When it works. >> When it works, yeah. So it does, again, with the caveat that I'm mostly focused on Spark and not so much the System ML side of things, so I'm definitely not an expert. I don't claim to be an expert in it. But it does, you know, it works at the moment. It works for a large class of machine-learning problems. It's very powerful, but again, it's a young project and there's always work to be done, so exactly the areas that I know that they're focusing on are these areas of usability, hardening up the APRs and making them easier to use and easier to access for users coming from the R and Python communities who, again are, as you said, they're not necessarily experts on distributed systems and cluster awareness, but they know how to write a very complex machine-learning model in R, for example. And it's really trying to enable them with a set of APR tools. So in terms of the underlying engine, they are, I don't know how many hundreds of thousands, millions of lines of code and years and years of research that's gone into that, so it's an extremely powerful set of tools. But yes, a lot of work still to be done there and ongoing to make it, in a way to make it user ready and Enterprise ready in a sense of making it easier for people to use it and adopt it and to put it into their systems and production. >> So I wonder if we can close, Nick, just a few questions on STC, so the Spark Technology Centers in Cape Town, is that a global expertise center? Is is STC a virtual sort of IBM community, or? >> I'm the only member visiting Cape Town, >> David: Okay. >> So I'm kind of fairly lucky from that perspective, to be able to kind of live at home. The rest of the team is mostly in San Francisco, so there's an office there that's co-located with the Watson west office >> Yeah. >> And Watson teams >> Sure. >> That are based there in Howard Street, I think it is. >> Dave: How often do you get there? >> I'll be there next week. >> Okay. >> So I typically, sort of two or three times a year, I try and get across there >> Right. And interface with the team, >> So, >> But we are a fairly, I mean, IBM is obviously a global company, and I've been surprised actually, pleasantly surprised there are team members pretty much everywhere. Our team has a few scattered around including me, but in general, when we interface with various teams, they pop up in all kinds of geographical locations, and I think it's great, you know, a huge diversity of people and locations, so. >> Anything, I mean, these early days here, early day one, but anything you saw in the morning keynotes or things you hope to learn here? Anything that's excited you so far? >> A couple of the morning keynotes, but had to dash out to kind of prepare for, I'm doing a talk later, actually on feature hashing for scalable machine learning, so that's at 12:20, please come and see it. >> Dave: A breakout session, it's at what, 12:20? >> 20 past 12:00, yeah. >> Okay. >> So in room 302, I think, >> Okay. >> I'll be talking about that, so I needed to prepare, but I think some of the key exciting things that I have seen that I would like to go and take a look at are kind of related to the deep learning on Spark. I think that's been a hot topic recently in one of the areas, again, Spark is, perhaps, hasn't been the strongest contender, let's say, but there's some really interesting work coming out of Intel, it looks like. >> They're talking here on The Cube in a couple hours. >> Yeah. >> Yeah. >> I'd really like to see their work. >> Yeah. >> And that sounds very exciting, so yeah. I think every time I come to a Spark summit, they always need projects from the community, various companies, some of them big, some of them startups that are pushing the envelope, whether it's research projects in machine learning, whether it's adding deep learning libraries, whether it's improving performance for kind of commodity clusters or for single, very powerful single modes, there's always people pushing the envelope, and that's what's great about being involved in an Open Source community project and being part of those communities, so yeah. That's one of the talks that I would like to go and see. And I think I, unfortunately, had to miss some of the Netflix talks on their recommendation pipeline. That's always interesting to see. >> Dave: Right. >> But I'll have to check them on the video (laughs). >> Well, there's always another project in Open Source land. Nick, thanks very much for coming on The Cube and good luck. Cool, thanks very much. Thanks for having me. >> Have a good trip, stay warm, hang in there. (Nick laughs) Alright, keep it right there. My buddy George and I will be back with our next guest. We're live. This is The Cube from Sparks Summit East, #sparksummit. We'll be right back. (upbeat music) (gentle music)