(vibrant electronic music) >> Welcome back to SuperCloud2. You know, this event, and the Super Cloud initiative in general, it's an open industry-wide collaboration. Last August at SuperCloud22, we really honed in on the definition, which of course we've published. And there's this shared doc, which folks are still adding to and refining, in fact, just recently, Dr. Nelu Mihai added some critical points that really advanced some of the community's initial principles, and today at SuperCloud2, we're digging further into the topic with input from real world practitioners, and we're exploring that intersection of data, data mesh, and cloud, and importantly, the realities and challenges of deploying technology to drive new business capability, and I'm pleased to welcome Ash Naseer to the program. He's a Senior Director of Data Engineering at Warner Bros. Discovery. Ash, great to see you again, thanks so much for taking time with us. >> It's great to be back, these conversations are always very fun. >> I was so excited when we met last spring, I guess, so before we get started I wanted to play a clip from that conversation, it was June, it was at the Snowflake Summit in Las Vegas. And it's a comment that you made about your company but also data mesh. Guys, roll the clip. >> Yeah, so, when people think of Warner Bros., you always think of the movie studio. But we're more than that, right, I mean, you think of HBO, you think of TNT, you think of CNN. We have 30 plus brands in our portfolio, and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company, so that CNN can work at their own pace, you know, when there's election season, they can ingest their own data. And they don't have to bump up against, as an example, HBO, if Game of Thrones is goin' on. >> So-- Okay, so that's pretty interesting, so you've got these sort of different groups that have different data requirements inside of your organization. Now data mesh, it's a relatively new concept, so you're kind of ahead of the curve. So Ash, my question is, when you think about getting value from data, and how that's changed over the past decade, you've had pre-Hadoop, Hadoop, what do you see that's changed, now you got the cloud coming in, what's changed? What had to be sort of fixed? What's working now, and where do you see it going? >> Yeah, so I feel like in the last decade, we've gone through quite a maturity curve. I actually like to say that we're in the golden age of data, because the tools and technology in the data space, particularly and then broadly in the cloud, they allow us to do things that we couldn't do way back when, like you suggested, back in the Hadoop era or even before that. So there's certainly a lot of maturity, and a lot of technology that has come about. So in terms of the good, bad, and ugly, so let me kind of start with the good, right? In terms of bringing value from the data, I really feel like we're in this place where the folks that are charged with unlocking that value from the data, they're actually spending the majority of their time actually doing that. And what do I mean by that? If you think about it, 10 years ago, the data scientist was the person that was going to sort of solve all of the data problems in a company. But what happened was, companies asked these data scientists to come in and do a multitude of things. And what these data scientists found out was, they were spending most of their time on, really, data wrangling, and less on actually getting the value out of the data. And in the last decade or so, I feel like we've made the shift, and we realize that data engineering, data management, data governance, those are as important practices as data science, which is sort of getting the value out of the data. And so what that has done is, it has freed up the data scientist and the business analyst and the data analyst, and the BI expert, to really focus on how to get value out of the data, and spend less time wrangling data. So I really think that that's the good. In terms of the bad, I feel like, there's a lot of legacy data platforms out there, and I feel like there's going to be a time where we'll be in that hybrid mode. And then the ugly, I feel like, with all the data and all the technology, creates another problem of itself. Because most companies don't have arms around their data, and making sure that they know who's using the data, what they're using for, and how can the company leverage the collective intelligence. That is a bigger problem to solve today than 10 years ago. And that's where technologies like the data mesh come in. >> Yeah, so when I think of data mesh, and I say, you're an early practitioner of data mesh, you mentioned legacy technology, so the concept of data mesh is inclusive. In theory anyway, you're supposed to be including the legacy technologies. Whether it's a data lake or data warehouse or Oracle or Snowflake or whatever it is. And when you think about Jamak Dagani's principles, it's domain-centric ownership, data as product. And that creates challenges around self-serve infrastructure and automated governance, and then when you start to combine these different technologies. You got legacy, you got cloud. Everything's different. And so you have to figure out how to deal with that, so my question is, how have you dealt with that, and what role has the cloud played in solving those problems, in particular, that self-serve infrastructure, and that automated governance, and where are we in terms of solving that problem from a practitioner's standpoint? >> Yeah, I always like to say that data is a team sport, and we should sort of think of it as such, and that's, I feel like, the key of the data mesh concept, is treating it as a team sport. A lot of people ask me, they're like, "Oh hey, Ash, I've heard about this thing called data mesh. "Where can I buy one?" or, "what's the technology that I use to get a data mesh? And the reality is that there isn't one technology, you can't really buy a data mesh. It's really a way of life, it's how organizations decide to approach data, like I said, back to a team sport analogy, making sure that everyone has the seat on the table, making sure that we embrace the fact that we have a lot of data, we have a lot of data problems to solve. And the way we'll be successful is to make everyone inclusive. You know, you think about the old days, Data silos or shadow IT, some might call it. That's been around for decades. And what hasn't changed was this notion that, hey, everything needs to be sort of managed centrally. But with the cloud and with the technologies that we have today, we have the right technology and the tooling to democratize that data, and democratize not only just the access, but also sort of building building blocks and sort of taking building blocks which are relevant to your product or your business. And adding to the overall data mesh. We've got all that technology. The challenge is for us to really embrace it, and make sure that we implement it from an organizational standpoint. >> So, thinking about super cloud, there's a layer that lives above the clouds and adds value. And you think about your brands you got 30 brands, you mentioned shadow IT. If, let's say, one of those brands, HBO or TNT, whatever. They want to go, "Hey, we really like Google's analytics tools," and they maybe go off and build something, I don't know if that's even allowed, maybe it's not. But then you build this data mesh. My question is around multi-cloud, cross cloud, super cloud if you will. Is that a advantage for you as a practitioner, or does that just make things more complicated? >> I really love the idea of a multi-cloud. I think it's great, I think that it should have been the norm, not the exception, I feel like people talk about it as if it's the exception. That should have been the case. I will say, though, I feel like multi-cloud should evolve organically, so back to your point about some of these different brands, and, you know, different brands or different business units. Or even in a merger and acquisitions situation, where two different companies or multiple different companies come together with different technology stacks. You know, I feel like that's an organic evolution, and making sure that we use the concepts and the technologies around the multi-cloud to bring everyone together. That's where we need to be, and again, it talks to the fact that each of those business units and each of those groups have their own unique needs, and we need to make sure that we embrace that and we enable that, rather than stifling everything. Now where I have a little bit of a challenge with the multi-cloud is when technology leaders try to build it by design. So there's a notion there that, "Hey, you need to sort of diversify "and don't put all your eggs in one basket." And so we need to have this multi-cloud thing. I feel like that is just sort of creating more complexity where it doesn't need to be, we can all sort of simplify our lives, but where it evolves organically, absolutely, I think that's the right way to go. >> But, so Ash, if it evolves organically don't you need some kind of cloud interpreter, to create a common experience across clouds, does that exist today? What are your thoughts on that? >> There is a lot of technology that exists today, and that helps go between these different clouds, a lot of these sort of cloud agnostic technologies that you talked about, the Snowflakes and the Databricks and so forth of the world, they operate in multiple clouds, they operate in multiple regions, within a given cloud and multiple clouds. So they span all of that, and they have the tools and technology, so, I feel like the tooling is there. There does need to be more of an evolution around the tooling and I think the market's need are going to dictate that, I feel like the market is there, they're asking for it, so, there's definitely going to be that evolution, but the technology is there, I think just making sure that we embrace that and we sort of embrace that as a challenge and not try to sort of shut all of that down and box everything into one. >> What's the biggest challenge, is it governance or security? Or is it more like you're saying, adoption, cultural? >> I think it's a combination of cultural as well as governance. And so, the cultural side I've talked about, right, just making sure that we give these different teams a seat at the table, and they actually bring that technology into the mix. And we use the modern tools and technologies to make sure that everybody sort of plays nice together. That is definitely, we have ways to go there. But then, in terms of governance, that is another big problem that most companies are just starting to wrestle with. Because like I said, I mean, the data silos and shadow IT, that's been around there, right? The only difference is that we're now sort of bringing everything together in a cloud environment, the collective organization has access to that. And now we just realized, oh we have quite a data problem at our hands, so how do we sort of organize this data, make sure that the quality is there, the trust is there. When people look at that data, a lot of those questions are now coming to the forefront because everything is sort of so transparent with the cloud, right? And so I feel like, again, putting in the right processes, and the right tooling to address that is going to be critical in the next years to come. >> Is sharing data across clouds, something that is valuable to you, or even within a single cloud, being able to share data. And my question is, not just within your organization, but even outside your organization, is that something that has sort of hit your radar or is it mature or is that something that really would add value to your business? >> Data sharing is huge, and again, this is another one of those things which isn't new. You know, I remember back in the '90s, when we had to share data externally, with our partners or our vendors, they used to physically send us stacks of these tapes, or physical media on some truck. And we've evolved since then, right, I mean, it went from that to sharing files online and so forth. But data sharing as a concept and as a concept which is now very frictionless, through these different technologies that we have today, that is very new. And that is something, like I said, it's always been going on. But that needs to be really embraced more as well. We as a company heavily leverage data sharing between our own different brands and business units, that helps us make that data mesh, so that when CNN, as an example, builds their own data model based on election data and the kinds of data that they need, compare that with other data in the rest of the company, sports, entertainment, and so forth and so on. Everyone has their unique data, but that data sharing capability brings it together wherever there is a need. So you think about having a Tiger Woods documentary, as an example, on HBO Max and making sure that you reach the audiences that are interested in golf and interested in sports and so forth, right? That all comes through the magic of data sharing, so, it's really critical, internally, for us. And then externally as well, because just understanding how our products are doing on our partners' networks and different distribution channels, that's important, and then just understanding how our consumers are consuming it off properties, right, I mean, we have brands that transcend just the screen, right? We have a lot of physical merchandise that you can buy in the store. So again, understanding who's buying the Batman action figures after the Batman movie was released, that's another critical insight. So it all gets enabled through data sharing, and something we rely heavily on. >> So I wanted to get your perspective on this. So I feel like the nirvana of data mesh is if I want to use Google BigQuery, an Oracle database, or a Microsoft database, or Snowflake, Databricks, Amazon, whatever. That that's a node on the mesh. And in the perfect world, you can share that data, it can be governed, I don't think we're quite there today, so. But within a platform, maybe it's within Google or within Amazon or within Snowflake or Databricks. If you're in that world, maybe even Oracle. You actually can do some levels of data sharing, maybe greater with some than others. Do you mandate as an organization that you have to use this particular data platform, or are you saying "Hey, we are architecting a data mesh for the future "where we believe the technology will support that," or maybe you've invented some technology that supports that today, can you help us understand that? >> Yeah, I always feel like mandate is a strong area, and it breeds the shadow IT and the data silos. So we don't mandate, we do make sure that there's a consistent set of governance rules, policies, and tooling that's there, so that everyone is on the same page. However, at the same time our focus is really operating in a federated way, that's been our solution, right? Is to make sure that we work within a common set of tooling, which may be different technologies, which in some cases may be different clouds. Although we're not that multi-cloud. So what we're trying to do is making sure that everyone who has that technology already built, as long as it sort of follows certain standards, it's modern, it has the capabilities that will eventually allow us to be successful and eventually allow for that data sharing, amongst those different nodes, as you put it. As long as that's the case, and as long as there's a governance layer, a master governance layer, where we know where all that data is and who has access to what and we can sort of be really confident about the quality of the data, as long as that case, our approach to that is really that federated approach. >> Sorry, did I hear you correctly, you're not multi-cloud today? >> Yeah, that's correct. There are certain spots where we use that, but by and large, we rely on a particular cloud, and that's just been, like I said, it's been the evolution, it was our evolution. We decided early on to focus on a single cloud, and that's the direction we've been going in. >> So, do you want to go to a multi-cloud, or, you mentioned organic before, if a business unit wants to go there, as long as they're adhering to those standards that you put out, maybe recommendations, that that's okay? I guess my question is, does that bring benefit to your business that you'd like to tap, or do you feel like it's not necessary? >> I'll go back to the point of, if it happens organically, we're going to be open about it. Obviously we'll have to look at every situations, not all clouds are created equal as well, so there's a number of different considerations. But by and large, when it happens organically, the key is time to value, right? How do you quickly bring those technologies in, as long as you could share the data, they're interconnected, they're secured, they're governed, we are confident on the quality, as long as those principles are met, we could definitely go in that direction. But by and large, we're sort of evolving in a singular direction, but even within a singular cloud, we're a global company. And we have audiences around the world, so making sure that even within a single cloud, those different regions interoperate as one, that's a bigger challenge that we're having to solve as well. >> Last question is kind of to the future of data and cloud and how it's going to evolve, do you see a day when companies like yours are increasingly going to be offering data, their software, services, and becoming more of a technology company, sort of pointing your tooling and your proprietary knowledge at the external world, as an opportunity, as a business opportunity? >> That's a very interesting concept, and I know companies have done that, and some of them have been extremely successful, I mean, Amazon is the biggest example that comes to mind, right-- >> Yeah. >> When they launched AWS, something that they had that expertise they had internally, and they offered it to the world as a product. But by and large, I think it's going to be far and few between, especially, it's going to be focused on companies that have technology as their DNA, or almost like in the technology sector, building technology. Most other companies have different markets that they are addressing. And in my opinion, a lot of these companies, what they're trying to do is really focus on the problems that we can solve for ourselves, I think there are more problems than we have people and expertise. So my guess is that most large companies, they're going to focus on solving their own problems. A few, like I said, more tech-focused companies, that would want to be in that business, would probably branch out, but by and large, I think companies will continue to focus on serving their customers and serving their own business. >> Alright, Ash, we're going to leave it there, Ash Naseer. Thank you so much for your perspectives, it was great to see you, I'm sure we'll see you face-to-face later on this year. >> This is great, thank you for having me. >> Ah, you're welcome, alright. Keep it right there for more great content from SuperCloud2. We'll be right back. (gentle percussive music)

(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of PagerDuty Summit '22. I'm Lisa Martin. I'm here with one of our alumni. Jonathan Rendy joins me, the SVP of products at PagerDuty. Jonathan, great to have you on the program. >> It's wonderful to be here. Thank you, Lisa. >> Lisa: It's great to be back at PagerDuty Summit. So much news this morning. So much buzz and excitement. Talk to me about some of the things that you're most excited about as we are in such a massively different work environment these days. >> Yeah, so much has been going on and we've been innovating in so many areas. I think you heard in the keynote this morning, automation is such a foundational part of PagerDuty now, and that comes to us via the Rundeck acquisition from a couple of years ago. And we've also extended PagerDuty to new audiences. So we've been a big part of the back office for a long time with SREs and developers and ITOps, and we've really come to realize that the front office is so important, and one of the leading departments there that we can make an impact and extend into with our solution is customer service. >> Lisa: Customer service is absolutely critical these days as we all know. One of the things that was in very short supply the last couple of years is patience. Patience when you're a consumer, patience when you're a business person. And so the voice of the customer, being able to get things escalated quickly and resolved quickly, to those customer service folks is critical for any organization. Without that, people easily go to Twitter or Reddit and escalate problems publicly, and suddenly that becomes a brand reputation problem for the organization. >> Yeah, you're spot on. I mean expectations are at an all time high. People's tolerance is at an all time low. And that gets translated, I always think, to the front door of the organization when there is something that doesn't go right, and that's typically the poor customer service agents who have to deal with that kind of feedback and open up cases and deal with it. And, you know, unfortunately they're not armed a lot of times with the information that could help them not only be better reactive but be better proactive and have information to actually turn what could be a bad experience into a really good one. >> Lisa: You mentioned something really interesting. Jonathan had a great fireside chat this morning that I was able to watch. And you said it takes, for every negative experience that a customer or consumer has, it takes seven additional positive experiences to turn them back around. And I thought, wow, do we even have the patience or the tolerance to your point, to give a business seven more options to turn our experience around? >> Yeah, it's tough. And it's very, very hard for a lot of organizations and nobody's exempt from it. The connection between the front office and the back office, there is no real gold standard for that. And so, is there a path forward? Is there a way forward? We believe there is and we believe there's a way to help, but teams really need to focus on getting information to those folks so that these very negative kind of situations can become a customer satisfaction, can become something where a customer feels like, "Wow, I didn't expect that." There was another statistic that we heard about the other day, which is, you know, greater than 50% of issues are often identified from customers, not from the monitoring products. So, you know, whether it's 50, or 40, or 30, it doesn't really matter. The customer is a signal and it's so important to be attentive to that signal. >> Lisa: What are some, well... you'd rather have that found out before the customer even notices. Talk to me about some of the things that PagerDuty just announced that are going to help not just the front office, back office kind of blurred lines there, but also to ensure that the incident response is smarter, it's faster, and it's being able to detect things before the customer even notices. >> Yeah, so the trick, the $64,000 question, however you want to phrase it or characterize it, is all about getting teams ahead of problems. And while I think it's unrealistic to ever, like every single customer, get ahead of any issue that any customer could see, it's so important that the first customer that comes in with an issue becomes near to the last customer that comes in with an issue, meaning that one, everybody knows about that and they know how it's related to existing issues. That's important so that other customers can be preemptively explained, but then given what PagerDuty's always done, sometimes we know about issues on the back end that may be impacting customers that they don't know about yet. So a shopping cart may not be working correctly, but before somebody hits it, if the customer service team knows about that right away, they can proactively get ready for communication to their customers to let them know, "Hey, there might be an issue here. We know about it, we're working on it. Please stay tuned", or direct them to something else that can help them. >> I can imagine that goes a long way to CSAT scores NPS scores, brand reputation, reducing churn. >> Jonathan: Oh, big time, big time, whether it's CSAT or NPS, you know, everybody is familiar on that big shopping day of the year, of getting that big sale, going to, wanting to order that, and then either not being able to complete the order or having to wait too long for it to be delivered. And then you end up having to go to a brick and mortar outlet to buy it there anyway. So there's so many opportunities and those situations will happen, outages will occur, it's just a matter of when. Those can be avoided in those bad situations via the use of other discounts, coupons, other customer satisfaction areas. You can turn those bad experiences into really good ones. >> Definitely. And I think we all have that expectation that that's going to happen, when outages do happen, 'cause to your point, those are the things that it's not, "Is it going to happen?" It's when, and how quickly can we recover from that so we minimize the impact on everybody else? Couple of the things that you announced this morning, Incident Objects and Service Cloud, talk to me about what that is. It looks like a deeper partnership integration with Salesforce. What are some of the benefits that your customers can expect? >> Jonathan: Yeah, so we have several partners in the front office, and one of the biggest known to the world is Salesforce. And so we've been working with the Service Cloud team there for going on a couple of years now, better integrating our platform into what they're doing. And we've actually built an app that runs inside of Service Cloud. So a customer service agent doesn't need to swivel chair around and look at other products in order to understand what's going on in the back office, it's all built into their experience. That's one, number one. Number two, we've upped that relationship and invested more where Service Cloud, Salesforce has come out with a new incident capability. And so we're integrating directly to that so we can sync up with that system of record from PagerDuty. So wherever the issues are found, whether it's in distributed DevOps teams, or whether it's in a central team, or whether it's a case agent working on the front end, everything will be kept in sync. So we're really excited about that bidirectional integration >> That bidirectional sync is critical. We have, you know, one of the biggest challenges, we've been talking about it since we were back at HP days back in the day, Jonathan, silos, right? That's one of the biggest challenges, is there's still silos between teams and systems, which impacts, you know, time to identify an incident, time to repair that incident, and then of course let alone repair the relationship with the customer on the other end. >> Jonathan: Yeah, yeah, and there's some great examples, working with our own customers, that we run into where when we can make that golden connection between the front office and the back office and sync up customer cases with incidents, magic starts to happen. So we've seen situations where the back office team working on an incident doesn't realize that the issue is customer impacting. They don't realize that there were three, and then four, and then five case tickets opened up, that it's really impacting customers. And when they see that rise in customer impact, they change the priority. They get other people involved. The urgency changes on that issue. Imagine working in a world where that visibility doesn't exist, people continue to work at their own pace and who suffers? The customer, the customer experience. >> Lisa: Without that visibility, so much can suffer. And quickly, we also have this expectation, I mentioned one of the things that was in short supply in the pandemic as patience and tolerance, but another thing is we expect things in real time, realtime access to data, realtime access to the customer, to a product or service, is no longer a nice to have, it is business critical for organizations in every industry. >> Yeah. Yep. And you know, customer service is such a obviously service-centered activity, that it can be, you know, death by a thousand paper cuts to a customer experience. And to the point that you're raising, nobody likes to contact finally someone as an agent, and then get passed to another agent, who gets passed to another agent, and have to repeat the problem that you're having so many times. What if we could capture all that context together. What if we could empower that agent to be able to manage that case from beginning to end more effectively? Like what would the reflection be on the customers who are calling in? They would feel taken care of. They would feel like they were heard. They wouldn't feel ignored, so to speak. So all of that is a part of our solution that we're partnering not only with Salesforce, but also with Zendesk and others to deliver. >> Talk about the automation in CS Ops and some of the main benefits. Obviously, you mentioned this a minute ago, but the ability to empower those agents to have that context is night and day compared to, you know, the solutions from back in the day. >> Jonathan: Yeah. Automation is so fundamental and foundational to everything we do at PagerDuty and if you look at all the audiences that make use of PagerDuty today, whether it's developers, whether it's IT operations and now customer service agents, it's no surprise that, you know, everyone has to do more with less, everyone's working in a more siloed, disconnected manner. So the amount of potential toil, potential manual steps, having to open up a system to get the status of something and then pivot over to my other system, or do research, or ask a customer multiple times when it could automatically be captured what their problem is, what the environment is, and all that information from an agent could be automatically inserted into the case. How valuable is that? Not only for the case, but then the teams on the back end, that helps them diagnose and fix those problems. So the amount of automation that we've built and now just announced and made available as a part of Customer Service Ops just like in DevOps with our automation actions, really important to automating some of those manual toil steps for those agents where, again, 50, 60% of their time is spent doing manual activities. We can get rid of that. We can empower them to do more, to do more with less. >> To do more with less and do more faster and it makes such a huge difference there. Talk a little bit about the DevOps-CS Ops relationship. You know, one of the things that's kind of ironic is here we are in 2022, we have so many tools to collaborate and connect, yet there's still so many silos, and that can either break trust between a customer and a vendor or a solution provider, or it can really facilitate trust. And that was a big theme of the keynote this morning is that trust. But talk about the trust that is you, PagerDuty, really thinks essential between the DevOps folks and the CS Ops folks. >> Yeah. It's critical, as I kind of mentioned before, there really isn't a golden path, a golden connection, a standard that's been set between CS, the customer service organizations and the back office. And how I like to characterize it and what I've seen over the years working with customers is frequently it's almost like when I was a little kid I lived nearby a semi-pro baseball team and I could never get tickets and I would ride my bike to the back of the fence and I would look at the game through a little knot hole in the fence and I'd be like, "Man that would be so great to be in there" Well, that's essentially customer service, sitting there looking at the game happening, constantly trying to interrupt the teams and saying, "Hey, what about us?" And so, by making that a seamless connection, by making customer service a part of the solution, a part of the team in a non impactful, intrusive way, everybody gets what they need, no one's interrupted, and now those customer service agents, they're sitting in the stands. They're not looking through the little knot hole at the back of the center field. >> Lisa: Well you got to tell us, did you ever get tickets? Can you go to pro games now? >> No. No. >> Aww >> Still waiting. >> Oh man. Talk to me, last question here, I asked you before we started filming if you had a crystal ball or a Magic 8-Ball, so next time at least bring me a Magic 8-Ball. What are some of the predictions that you have as you see where we are in... now half of calendar '22 almost gone, the announcements coming from PagerDuty today, this synergy is between PagerDuty, its, what, 21,000 plus customers, your partners, What are some of the things that you're excited about that are coming? >> Jonathan: So a couple things. One is I really think the first example, we talk about the Operations Cloud, what PagerDuty is. And to me, what it really is, is it's not just the DevOps audiences and the ITOps and the SRE teams in the back offices that have to deal with interrupted realtime work, but it's other parts of the organization as well that have to get proactive versus reactive. And the first of those, the first step that kind of personifies the Operations Cloud outside of that back office is customer service. But there will be more, there will be more, whether it's security or other teams. So it's the audiences that can participate and engage in realtime work, that's one. And then I think in the area of customer service and Customer Service Operations, where we are, what we've been doing and what we've been so focused on is making sure that those agents can start to get proactive and start to get to the next step. But wouldn't it be amazing if we could help them, proactively, in a targeted way, talk to their customers and provide that as an automated part of the process. Today that's very manual, so we can empower them with information, but a lot of their communication with their customers is manual. What if we could automate that? And that's our plans, and that's what I'm really excited about doing. >> Can you imagine the trust built between an empowered, proactive CS agent and a customer on the other end. The sky is the limit on that one. >> If I'm a platinum customer or I'm a silver customer, I'm paying for a certain level of customer service. How great would it be if based on the extra that I'm paying, I'm actually getting that service proactively and I'm hearing about issues long before I see them. That to me is building trust. >> Lisa: Absolutely. Jonathan, thank you so much for joining me on theCUBE today. Great to see you back in person. Great to hear some of the things coming down the road for PagerDuty, and we're excited to see your predictions come true. Thanks for your time. >> Likewise, Lisa. Thank you very much. >> My pleasure. For Jonathan Rendy. I'm Lisa Martin covering theCUBE on the ground at PagerDuty summit '22. Stick around, I'll be right back with my next guest. (upbeat music)

(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of PagerDuty Summit '22. I'm Lisa Martin. I'm here with one of our alumni. Jonathan Rendy joins me, the SVP of products at PagerDuty. Jonathan, great to have you on the program. >> It's wonderful to be here. Thank you, Lisa. >> Lisa: It's great to be back at PagerDuty Summit. So much news this morning. So much buzz and excitement. Talk to me about some of the things that you're most excited about as we are in such a massively different work environment these days. >> Yeah, so much has been going on and we've been innovating in so many areas. I think you heard in the keynote this morning, automation is such a foundational part of PagerDuty now, and that comes to us via the Rundeck acquisition from a couple of years ago. And we've also extended PagerDuty to new audiences. So we've been a big part of the back office for a long time with SREs and developers and ITOps, and we've really come to realize that the front office is so important, and one of the leading departments there that we can make an impact and extend into with our solution is customer service. >> Lisa: Customer service is absolutely critical these days as we all know. One of the things that was in very short supply the last couple of years is patience. Patience when you're a consumer, patience when you're a business person. And so the voice of the customer, being able to get things escalated quickly and resolved quickly, to those customer service folks is critical for any organization. Without that, people easily go to Twitter or Reddit and escalate problems publicly, and suddenly that becomes a brand reputation problem for the organization. >> Yeah, you're spot on. I mean expectations are at an all time high. People's tolerance is at an all time low. And that gets translated, I always think, to the front door of the organization when there is something that doesn't go right, and that's typically the poor customer service agents who have to deal with that kind of feedback and open up cases and deal with it. And, you know, unfortunately they're not armed a lot of times with the information that could help them not only be better reactive but be better proactive and have information to actually turn what could be a bad experience into a really good one. >> Lisa: You mentioned something really interesting. Jonathan had a great fireside chat this morning that I was able to watch. And you said it takes, for every negative experience that a customer or consumer has, it takes seven additional positive experiences to turn them back around. And I thought, wow, do we even have the patience or the tolerance to your point, to give a business seven more options to turn our experience around? >> Yeah, it's tough. And it's very, very hard for a lot of organizations and nobody's exempt from it. The connection between the front office and the back office, there is no real gold standard for that. And so, is there a path forward? Is there a way forward? We believe there is and we believe there's a way to help, but teams really need to focus on getting information to those folks so that these very negative kind of situations can become a customer satisfaction, can become something where a customer feels like, "Wow, I didn't expect that." There was another statistic that we heard about the other day, which is, you know, greater than 50% of issues are often identified from customers, not from the monitoring products. So, you know, whether it's 50, or 40, or 30, it doesn't really matter. The customer is a signal and it's so important to be attentive to that signal. >> Lisa: What are some, well... you'd rather have that found out before the customer even notices. Talk to me about some of the things that PagerDuty just announced that are going to help not just the front office, back office kind of blurred lines there, but also to ensure that the incident response is smarter, it's faster, and it's being able to detect things before the customer even notices. >> Yeah, so the trick, the $64,000 question, however you want to phrase it or characterize it, is all about getting teams ahead of problems. And while I think it's unrealistic to ever, like every single customer, get ahead of any issue that any customer could see, it's so important that the first customer that comes in with an issue becomes near to the last customer that comes in with an issue, meaning that one, everybody knows about that and they know how it's related to existing issues. That's important so that other customers can be preemptively explained, but then given what PagerDuty's always done, sometimes we know about issues on the back end that may be impacting customers that they don't know about yet. So a shopping cart may not be working correctly, but before somebody hits it, if the customer service team knows about that right away, they can proactively get ready for communication to their customers to let them know, "Hey, there might be an issue here. We know about it, we're working on it. Please stay tuned", or direct them to something else that can help them. >> I can imagine that goes a long way to CSAT scores NPS scores, brand reputation, reducing churn. >> Jonathan: Oh, big time, big time, whether it's CSAT or NPS, you know, everybody is familiar on that big shopping day of the year, of getting that big sale, going to, wanting to order that, and then either not being able to complete the order or having to wait too long for it to be delivered. And then you end up having to go to a brick and mortar outlet to buy it there anyway. So there's so many opportunities and those situations will happen, outages will occur, it's just a matter of when. Those can be avoided in those bad situations via the use of other discounts, coupons, other customer satisfaction areas. You can turn those bad experiences into really good ones. >> Definitely. And I think we all have that expectation that that's going to happen, when outages do happen, 'cause to your point, those are the things that it's not, "Is it going to happen?" It's when, and how quickly can we recover from that so we minimize the impact on everybody else? Couple of the things that you announced this morning, Incident Objects and Service Cloud, talk to me about what that is. It looks like a deeper partnership integration with Salesforce. What are some of the benefits that your customers can expect? >> Jonathan: Yeah, so we have several partners in the front office, and one of the biggest known to the world is Salesforce. And so we've been working with the Service Cloud team there for going on a couple of years now, better integrating our platform into what they're doing. And we've actually built an app that runs inside of Service Cloud. So a customer service agent doesn't need to swivel chair around and look at other products in order to understand what's going on in the back office, it's all built into their experience. That's one, number one. Number two, we've upped that relationship and invested more where Service Cloud, Salesforce has come out with a new incident capability. And so we're integrating directly to that so we can sync up with that system of record from PagerDuty. So wherever the issues are found, whether it's in distributed DevOps teams, or whether it's in a central team, or whether it's a case agent working on the front end, everything will be kept in sync. So we're really excited about that bidirectional integration >> That bidirectional sync is critical. We have, you know, one of the biggest challenges, we've been talking about it since we were back at HP days back in the day, Jonathan, silos, right? That's one of the biggest challenges, is there's still silos between teams and systems, which impacts, you know, time to identify an incident, time to repair that incident, and then of course let alone repair the relationship with the customer on the other end. >> Jonathan: Yeah, yeah, and there's some great examples, working with our own customers, that we run into where when we can make that golden connection between the front office and the back office and sync up customer cases with incidents, magic starts to happen. So we've seen situations where the back office team working on an incident doesn't realize that the issue is customer impacting. They don't realize that there were three, and then four, and then five case tickets opened up, that it's really impacting customers. And when they see that rise in customer impact, they change the priority. They get other people involved. The urgency changes on that issue. Imagine working in a world where that visibility doesn't exist, people continue to work at their own pace and who suffers? The customer, the customer experience. >> Lisa: Without that visibility, so much can suffer. And quickly, we also have this expectation, I mentioned one of the things that was in short supply in the pandemic as patience and tolerance, but another thing is we expect things in real time, realtime access to data, realtime access to the customer, to a product or service, is no longer a nice to have, it is business critical for organizations in every industry. >> Yeah. Yep. And you know, customer service is such a obviously service-centered activity, that it can be, you know, death by a thousand paper cuts to a customer experience. And to the point that you're raising, nobody likes to contact finally someone as an agent, and then get passed to another agent, who gets passed to another agent, and have to repeat the problem that you're having so many times. What if we could capture all that context together. What if we could empower that agent to be able to manage that case from beginning to end more effectively? Like what would the reflection be on the customers who are calling in? They would feel taken care of. They would feel like they were heard. They wouldn't feel ignored, so to speak. So all of that is a part of our solution that we're partnering not only with Salesforce, but also with Zendesk and others to deliver. >> Talk about the automation in CS Ops and some of the main benefits. Obviously, you mentioned this a minute ago, but the ability to empower those agents to have that context is night and day compared to, you know, the solutions from back in the day. >> Jonathan: Yeah. Automation is so fundamental and foundational to everything we do at PagerDuty and if you look at all the audiences that make use of PagerDuty today, whether it's developers, whether it's IT operations and now customer service agents, it's no surprise that, you know, everyone has to do more with less, everyone's working in a more siloed, disconnected manner. So the amount of potential toil, potential manual steps, having to open up a system to get the status of something and then pivot over to my other system, or do research, or ask a customer multiple times when it could automatically be captured what their problem is, what the environment is, and all that information from an agent could be automatically inserted into the case. How valuable is that? Not only for the case, but then the teams on the back end, that helps them diagnose and fix those problems. So the amount of automation that we've built and now just announced and made available as a part of Customer Service Ops just like in DevOps with our automation actions, really important to automating some of those manual toil steps for those agents where, again, 50, 60% of their time is spent doing manual activities. We can get rid of that. We can empower them to do more, to do more with less. >> To do more with less and do more faster and it makes such a huge difference there. Talk a little bit about the DevOps-CS Ops relationship. You know, one of the things that's kind of ironic is here we are in 2022, we have so many tools to collaborate and connect, yet there's still so many silos, and that can either break trust between a customer and a vendor or a solution provider, or it can really facilitate trust. And that was a big theme of the keynote this morning is that trust. But talk about the trust that is you, PagerDuty, really thinks essential between the DevOps folks and the CS Ops folks. >> Yeah. It's critical, as I kind of mentioned before, there really isn't a golden path, a golden connection, a standard that's been set between CS, the customer service organizations and the back office. And how I like to characterize it and what I've seen over the years working with customers is frequently it's almost like when I was a little kid I lived nearby a semi-pro baseball team and I could never get tickets and I would ride my bike to the back of the fence and I would look at the game through a little knot hole in the fence and I'd be like, "Man that would be so great to be in there" Well, that's essentially customer service, sitting there looking at the game happening, constantly trying to interrupt the teams and saying, "Hey, what about us?" And so, by making that a seamless connection, by making customer service a part of the solution, a part of the team in a non impactful, intrusive way, everybody gets what they need, no one's interrupted, and now those customer service agents, they're sitting in the stands. They're not looking through the little knot hole at the back of the center field. >> Lisa: Well you got to tell us, did you ever get tickets? Can you go to pro games now? >> No. No. >> Aww >> Still waiting. >> Oh man. Talk to me, last question here, I asked you before we started filming if you had a crystal ball or a Magic 8-Ball, so next time at least bring me a Magic 8-Ball. What are some of the predictions that you have as you see where we are in... now half of calendar '22 almost gone, the announcements coming from PagerDuty today, this synergy is between PagerDuty, its, what, 21,000 plus customers, your partners, What are some of the things that you're excited about that are coming? >> Jonathan: So a couple things. One is I really think the first example, we talk about the Operations Cloud, what PagerDuty is. And to me, what it really is, is it's not just the DevOps audiences and the ITOps and the SRE teams in the back offices that have to deal with interrupted realtime work, but it's other parts of the organization as well that have to get proactive versus reactive. And the first of those, the first step that kind of personifies the Operations Cloud outside of that back office is customer service. But there will be more, there will be more, whether it's security or other teams. So it's the audiences that can participate and engage in realtime work, that's one. And then I think in the area of customer service and Customer Service Operations, where we are, what we've been doing and what we've been so focused on is making sure that those agents can start to get proactive and start to get to the next step. But wouldn't it be amazing if we could help them, proactively, in a targeted way, talk to their customers and provide that as an automated part of the process. Today that's very manual, so we can empower them with information, but a lot of their communication with their customers is manual. What if we could automate that? And that's our plans, and that's what I'm really excited about doing. >> Can you imagine the trust built between an empowered, proactive CS agent and a customer on the other end. The sky is the limit on that one. >> If I'm a platinum customer or I'm a silver customer, I'm paying for a certain level of customer service. How great would it be if based on the extra that I'm paying, I'm actually getting that service proactively and I'm hearing about issues long before I see them. That to me is building trust. >> Lisa: Absolutely. Jonathan, thank you so much for joining me on theCUBE today. Great to see you back in person. Great to hear some of the things coming down the road for PagerDuty, and we're excited to see your predictions come true. Thanks for your time. >> Likewise, Lisa. Thank you very much. >> My pleasure. For Jonathan Rendy. I'm Lisa Martin covering theCUBE on the ground at PagerDuty summit '22. Stick around, I'll be right back with my next guest. (upbeat music)

(upbeat music) >> Welcome back to the Javits in the big apple, New York City. This is theCUBE's coverage of MongoDB World 2022. We're here for a full day of coverage. We're talking to customers, partners, executives and analysts as well. Peder Ulander is here. He's the Chief Marketing Officer of MongoDB and he's joined by Radhika Krishnan, who's the Chief Product Officer at Hitachi Ventara. Folks, welcome back to theCUBE. Great to see you both again. >> Good to see you. >> Thank you David, it's good to be back again. >> Peder, first time since 2019, we've been doing a lot of these conferences and many of them, it's the first time people have been out in a physical event in three years. Amazing. >> I mean, after three years to come back here in our hometown of New York and get together with a few thousand of our favorite customers, partners, analysts, and such, to have real good discussions around where we're taking the world with regards to our developer data platform. It's been great. >> I think a big part of that story of course, is ecosystem and partnerships and Radhika, I remember I was at an event when Hitachi announced its strategy and it's name change, and really tried to understand why and the what's behind that. And of course, Hitachi's a company that looks out over the long term, and of course it has to perform tactically, but it thinks about the future. So give us the update on what's new at Hitachi Ventara, especially as it relates to data. >> Sure thing, Dave. As many, many folks might be aware, there's a very strong heritage that Hitachi has had in the data space, right. By virtue of our products and our presence in the data storage market, which dates back to many decades, right? And then on the industrial side, the parent company Hitachi has been heavily focused on the OT sector. And as you know, there is a pretty significant digital transformation underway in the OT arena, which is all being led by data. So if you look at our mission statement, for instance, it's actually engineering the data driven because we do believe that data is the fundamental platform that's going to drive that digital transformation, irrespective of what industry you're in. >> So one of the themes that you guys both talk about is modernization. I mean, you can take a cloud, I remember Alan Nance, who was at the time, he was a CIO at Philips, he said, look, you could take a cloud workload, or on-prem workload, stick it into the cloud and lift it and shift it. And in your case, you could just put it on, run it on an RDBMS, but you're not going to affect the operational models. >> Peder Ulander: It's just your mess for less, man. >> If you do that. >> It's your mess, for less. >> And so, he goes, you'll get a few, you know, you'll get a couple of zeros out of that. But if you want to have, in his case, billion dollar impact to the business, you have to modernize. So what does modernize mean to each of you? >> Maybe Peder, you can start. >> Yeah, no, I'm happy to start. I think it comes down to what's going on in the industry. I mean, we are truly moving from a world of data centers to centers of data, and these centers of data are happening further and further out along the network, all the way down to the edges. And if you look at the transformation of infrastructure or software that has enabled us to get there, we've seen apps go from monoliths to microservices. We've seen compute go from physical to serverless. We've seen networking go from old wireline copper to high powered 5G networks. They've all transformed. What's the one layer that hasn't completely transformed yet, data, right? So if we do see this world where things are getting further and further out, you've got to rethink your data architecture and how you basically support this move to modernization. And we feel that MongoDB with our partners, especially with Hitachi, we're best suited to really kind of help with this transition for our customers as they move from data centers to centers of data. >> So architecture. And at the failure, I will say this and you tell me if you agree or not. A lot of the failures of sort of the big data architectures of today are there's, everything's in this monolithic database, you've got to go through a series of hyper-specialized professionals to get to the data. If you're a business individual, you're so frustrated because the market's changing faster than you can get answers. So you guys, I know, use this concept of data fabric, people talk about data mesh. So how do you think, Radhika, about modernization in the future of data, which by its very nature is distributed? >> Yeah. So Dave, everybody talks about the hybrid cloud, right? And so the reality is, every one of our customers is having to deal with data that's straddled across on-prem as well as the public cloud and many other places as well. And so it becomes incredibly important that you have a fairly seamless framework, that's relatively low friction, that allows you to go from the capture of the data, which could be happening at the edge, could be happening at the core, any number of places, all the way to publish, right. Which is ultimately what you want to do with data because data exists to deliver insights, right? And therefore you dramatically want to minimize the friction in the process. And that is exactly what we're attempting to do with our data fabric construct, right. We're essentially saying, customers don't have to worry about, like you mentioned, they may have federated data structures, architectures, data lakes, fitting in multiple locations. How do you ensure that you're not having to double up custom code in order to drive the pipelines, in order to drive the data movement from one location to the other and so forth. And so essentially what we're providing is a mechanism whereby they can be confident about the quality of the data at the end of the day. And this is so paramount. Every customer that I talk to is most worried about ensuring that they have data that is trustworthy. >> So this is a really important point because I've always felt like, from a data quality standpoint, you know you get the data engineers who might not have any business context, trying to figure out the quality problem. If you can put the data responsibility in the hands of the business owner, who, he or she, has context, that maybe starts to solve this problem. There's some buts though. So infrastructure becomes an operational detail. Let's hide that. Don't worry about it. Figure it out, okay, so the business can run, but you need self-service infrastructure and you have to figure out how to have federated governance so that the right people can have access. So how do you guys think about that problem in the future? 'Cause it's almost like this vision creates those two challenges. Oh, by the way, you got to get your organization behind it. Right, 'cause there's an organizational construct as well. But those are, to me, wonderful opportunities but they create technology challenges. So how are you guys thinking about that and how are you working on it? >> Yeah, no, that's exactly right, Dave. As we talk to data practitioners, the recurring theme that we keep hearing is, there is just a lot of use cases that require you to have deep understanding of data and require you to have that background in data sciences and so on, such as data governance and vary for their use cases. But ultimately, the reason that data exists is to be able to drive those insights for the end customer, for the domain expert, for the end user. And therefore it becomes incredibly important that we be able to bridge that chasm that exists today between the data universe and the end customer. And that is what we essentially are focused on by virtue of leaning into capabilities like publishing, right? Like self, ad hoc reporting and things that allow citizen data scientists to be able to take advantage of the plethora of data that exists. >> Peder, I'm interested in this notion of IT and OT. Of course, Hitachi is a partner, established in both. Talk about Mongo's position in thinking. 'Cause you've got on-prem customers, you're running now across all clouds. I call it super cloud connecting all these things. But part of that is the edge. Is Mongo running there? Can Mongo run there, sort of a lightweight version? How do you see that evolve? Give us some details there. >> So I think first and foremost, we were born on-prem, obviously with the origins of MongoDB, a little over five years ago, we introduced Atlas and today we run across a hundred different availability zones around the globe, so we're pretty well covered there. The third bit that I think people miss is we also picked up a product called Realm. Realm is an embedded database for mobile devices. So if you think about car companies, Toyota, for example, building connected cars, they'll have Realm in the car for the telemetry, connects back into an Atlas system for the bigger operational side of things. So there's this seamless kind of, or consistency that runs between data center to cloud to edge to device, that MongoDB plays across all the way through. And then taking that to the next level. We talked about this before we sat down, we're also building in the security elements of that because obviously you not only have that data in rest and data in motion, but what happens when you have that data in use? And announced, I think today? We purchased a little company, Aroki, experts in encryption, some of the smartest security minds on the planet. And today we introduce query-able encryption, which basically enables developers, without any security background, to be able to build searchable capabilities into their applications to access data and do it in a way where the security rules and the privacy all remain constant, regardless of whether that developer or the end user actually knows how that works. >> This is a great example of people talk about shift left, designing security in, for the developer, right from the start, not as a bolt-on. It's a great example. >> And I'm actually going to ground that with a real life customer example, if that's okay, Dave. We actually have a utility company in North Carolina that's responsible for energy and water. And so you can imagine, I mean, you alluded to the IO to use case, the industrial use case and this particular customer has to contend with millions of sensors that are constantly streaming data back, right. And now think about the challenge that they were encountering. They had all this data streaming in and in large quantities and they were actually resident on numerous databases, right. And so they had this very real challenge of getting to that quality data that I, data quality that I talked about earlier, as well, they had this challenge of being able to consolidate all of it and make sense of it. And so that's where our partnership with MongoDB really paid off where we were able to leverage Pentaho to integrate all of the data, have that be resident on MongoDB. And now they're leveraging some of the data capabilities, the data fabric capabilities that we bring to the table to actually deliver meaningful insights to their customers. Now their customers are actually able to save on their electricity and water bills. So great success story right there. >> So I love the business impact there, and also you mentioned Pentaho, I remember that acquisition was transformative for Hitachi because it was the beginning of sort of your new vector, which became Hitachi Ventara. What is Lumada? That's, I presume the evolution of Pentaho? You brought in organic, and added capabilities on top of that, bringing in your knowledge of IOT and OT? Explain what Lumada is. >> Yeah, no, that's a great question, Dave. And I'll say this, I mentioned this early on, we fundamentally believe that data is the backbone for all digital transformation. And so to that end, Hitachi has actually been making a series of acquisitions as well as investing organically to build up these data capabilities. And so Pentaho, as you know, gives us some of that front-end capability in terms of integrations and so forth. And the Lumada platform, the umbrella brand name is really connoting everything that we do in the data space that allow customers to go through that, to derive those meaningful insights. Lumada literally stands for illuminating data. And so that's exactly what we do. Irrespective of what vertical, what use case we're talking about. As you know very well, Hitachi is very prominent in just about every vertical. We're in like 90% of the Fortune 500 customers across banking and financial, retail, telecom. And as you know very well, very, very strong in the industrial space as well. >> You know, it's interesting, Peder, you and Radhika were both talking about this sort of edge model. And so if I understand it correctly, and maybe you could bring in sort of the IOT requirements as well. You think about AI, most of the AI that's done today is modeling in the cloud. But in the future and as we're seeing this, it's real-time inferencing at the edge and it's massive amounts of data. But you're probably not, you're going to persist some, I'm hearing, probably not going to persist all of it, some of it's going to be throwaway. And then you're going to send some back to the cloud. I think of EVs or, a deer runs in front of the vehicle and they capture that, okay, send that back. The amounts of data is just massive. Is that the right way to think about this new model? Is that going to require new architectures and hearing that Mongo fits in. >> Yeah. >> Beautifully with that. >> So this is a little bit what we talked about earlier, where historically there have been three silos of data. Whether it's classic system of record, system of engagement or system of intelligence and they've each operated independently. But as applications are pushing in further and further to the edge and real time becomes more and more important, you need to be able to take all three types of workloads or models, data models and actually incorporate it into a single platform. That's the vision we have behind our developer data platform. And it enables us to handle those transactional, operational and analytical workloads in real time, right. One of the things that we announced here this week was our columnar indexing, which enables some of that step into the analytics so that we can actually do in-app analytics for those things that are not going back into the data warehouse or not going back into the cloud, real time happening with the application itself. >> As you add, this is interesting, as basically Mongo's becoming this all-in-one database, as you add those capabilities, are you able to preserve, it sounds like you've still focused on simplicity, developer product productivity. Are there trade off, as you add, does it detract from those things or are you able to architecturally preserve those? >> I think it comes down to how we're thinking through the use case and what's going to be important for the developers. So if you look at the model today, the legacy model was, let's put it all in one big monolith. We recognize that that doesn't work for everyone but the counter to that was this explosion of niche databases, right? You go to certain cloud providers, you get to choose between 15 different databases for whatever workload you want. Time series here, graph here, in-memory here. It becomes a big mess that is pushed back on the company to glue back together and figure out how to work within those systems. We're focused on really kind of embracing the document model. We obviously believe that's a great general purpose model for all types of workloads. And then focusing in on not taking a full search platform that's doing everything from log management all the way through in-app, we're optimizing for in-app experiences. We're optimizing analytics for in-app experiences. We're optimizing all of the different things we're doing for what the developer is trying to go accomplish. That helps us maintain consistency on the architectural design. It helps us maintain consistency in the model by which we're engaging with our customers. And I think it helps us innovate as quickly as we've been been able to innovate. >> Great, thank you. Radhika, we'll give you the last word. We're seeing this convergence of function in the data based, data models, but at the same time, we're seeing the distribution of data. We're not, you're clearly not fighting that, you're embracing that. What does the future look like from Hitachi Ventara's standpoint over the next half decade or even further out? >> So, we're trying to lean into what customers are trying to solve for, Dave. And so that fundamentally comes down to use cases and the approaches just may look dramatically different with every customer and every use case, right? And that's perfectly fine. We're leaning into those models, whether that is data refining on the edge or the core or the cloud. We're leaning into it. And our intent really is to ensure that we're providing that frictionless experience from end to end, right. And I'll give a couple of examples. We had this very large bank, one of the top 10 banks here in the US, that essentially had multiple data catalogs that they were using to essentially sort through their metadata and make sense of all of this data that was coming into their systems. And we were able to essentially, dramatically simplify it. Cut down on the amount of time that it takes to deliver insights to them, right. And it was like, the metric shared was 600% improvement. And so this is the kind of thing that we're manically focused on is, how do we deliver that quantifiable end-customer improvement, right? Whether it's in terms of shortening the amount to drive the insights, whether it's in terms of the number of data practitioners that they have to throw at a problem, the level of manual intervention that is required, so we're automating everything. We're trying to build in a lot of security as Peder talked about, that is a common goal for both sides. We're trying to address it through a combination of security solutions at varying ends of the spectrum. And then finally, as well, delivering that resiliency and scale that is required. Because again, the one thing we know for sure that we can take for granted is data is exploding, right? And so you need that scale, you need that resiliency. You need for customers to feel like there is high quality, it's not dirty, it's not dark and it's something that they can rely upon. >> Yeah, if it's not trusted, they're not going to use it. The interesting thing about the partnership, especially with Hitachi, is you're in so many different examples and use cases. You've got IT. You've got OT. You've got industrial and so many different examples. And if Mongo can truly fit into all those, it's just, the rocket ship's going to continue. Peder, Radhika, thank you so much for coming back in theCUBE, it's great to see you both. >> Thank you, appreciate it. >> Thank you, my pleasure. >> All right. Keep it right there. This is Dave Vellante from the Javits Center in New York City at MongoDB World 2022. We'll be right back. (upbeat music)

(upbeat music) >> Hey guys and girls. Welcome back to theCUBE's coverage of Coupa Inspire 2022, from the Cosmopolitan, in bustling Las Vegas. Lisa Martin here, and as I mentioned, day two of our coverage and fresh from the main stage, Raja Hammoud joins me, the Executive Vice President of products at Coupa. Raja, welcome back to theCUBE and happy 10th anniversary at Coupa. >> Oh, thank you, thank you, thank you, and welcome back to Inspire. >> Thank you. It's so great- >> We're so happy you're here. >> It's great to be here. So you're just about coming up on your 10 anniversary with Coupa. You showed some great photos of your time there but you've seen, you've lived the evolution that is this rocket ship that's Coupa. >> Raja: It's been incredible journey. I really couldn't believe at first it's been 10. This is the longest I've ever been anywhere. And I honestly feel more refreshed and excited than even when I joined back in the day 10 years ago. And so much has changed, but also so much has not. >> Lisa: Yeah. >> The size of course. We were like 60 people when I joined, the product development team was one person in, in a product, roughly 12 engineers, and fast forward to the scale that's today, it's phenomenal difference. But what has not changed is the, the core values, how, the hustle, how people love working with each other, how we support customers, how we keep stepping up our game how we believe none of us is as smart as all of us, and the community keeps getting stronger and stronger. It's been, it's been really exciting journey. >> The theme of none of us is as smarter as all of us, I'm not sure if I got that right, but the idea is you feel that when you're talking to Coupa partners, I've had the opportunity to talk with Coupa partners and customers and Coupa folks that, that is not just a value statement, people are living that. >> Raja: Yeah. It's, it's everywhere. In the, in the company walls, outside the company walls, you often see product people in different organizations where, they start living in an ivory tower, they think they know everything, I mean, back to what we were discussing earlier about Barbara, when she talked about, get out of your doors, right? A lot of people can tend to do that. We always, from the beginning, believed in the best ideas are out there and you collaborate with each other. And I truly, truly believe that the success that we have achieved today to our community is in a large, large part, because we believed in that. So like on Monday, we hosted, I can't keep track of the number now, so, so many in-parallel Community Advisory Board meetings, and just talking to the products managers and everybody is buzzing with new ideas. And when we go back, there's so much new innovation that has just been co-created here in this conference, and this keeps going on and on and on. >> Lisa: Yeah. I like how you call it, the Community Advisory Board. I'm still used to hearing CAB as Customer Advisory Board, but what Coupa has built, especially with the launch of the Moonshot, the, the community AI, is, is just that. >> Yes. >> It's a very collaborative community. One of the things that's around here, hashtags everywhere, but #United by the Power of Spend. >> Yes. >> What does that mean to you as the EVP of products, and what do you think that means to the community? >> When I think... What we are doing, we're building this platform that is powering all these businesses out there. And the reality of it is you can only, only do so much when you try to do things alone. When we are doing things together, we are way more successful, we are more profitable, we are more sustainable, we are more efficient. And community.ai from a technology standpoint, is making that happen, because what we are doing is taking AI, applying it to all this 3.3 trillion in data, and then bringing back prescriptions that we give back to each and every customer so that everybody can see where they are, how they up their games, and we connect them with other people like them. Now, people love coming to conferences like this, but even in conferences like this, if you think about it, the people you're going to meet, it's, some people are going to do matchmaking but you are also losing an opportunities of meeting the maximum number of people who've done exactly the thing that you did. But when you have the ability to look at all of that data and you can match make people. So we did that already with, for sourcing professionals. So if you are somebody who source a certain category, we can tell somebody else has done something like this in this geography and we offer you to connect to each other. >> Lisa: Wow. >> So this is incredibly powerful way where we are really uniting the whole community by spend, making everybody truly stronger together. >> Lisa: Matchmaker in, in a sense. >> It is matchmaking. >> But it's, but it's- >> It's Spend matchmaking. >> Spend matchmaking, but it's also the opportunity to unite professionals across sourcing, procurement- >> Raja: Yes. >> ... finance, treasury. >> Raja: Yes. >> To your point, and, and Rob said this in his keynote, and he said it here on theCUBE, you know, we've got to break down these silos. >> Raja: Yes. >> People and companies functioning in silos are not going to be successful. >> Raja: Yes. This has been one of the, probably one of the things that we were talking earlier, what has changed, what hasn't. This is one of the fundamental things that has never changed since I've joined. The vision has been very clear. The execution on it, of how we drive successful business spend management program is by breaking down the silos and this idea of sweet synergy, where in product, you start building these capabilities that helps these professionals in the different organizations to actually connect on the touch points, where, where things really matter. >> Lisa: Sweet synergy, was that thing from a concept perspective, did that come from the community, in terms of Coupa going, this is actually what's happening, this synergy across the BSM suite? >> Yes. So in the very beginning, it was early idea. I would say in the first two Inspires that we did, we hadn't given it actually the name itself, and we used to call it unified capabilities, and it started with the first silos we broke down. The first silos we broke down were procurement and AP. And they didn't even used to talk in the same room or even want to care about each other. So we started building so many capabilities that brought these teams together and little by little the community started to feel that and see the value of that. And then the community started to ask us to go break down more silos. So in the beginning, I would say the, the vision before I even joined, the company was on that trajectory. And the early customers saw that and they championed it and then they drove us to do more. So they came to us and said could you please do what you did here in contract? Could you please do what you did here in sourcing? And I was in a meeting last week, a leadership meeting, and one question was asked to leaders in the services team about what are they hearing about, from the customers, about a particular area. And it was music to our ears when we heard the customers are asking for more synergy, right? So, they even have the name for it and they're asking for more and more, and we have built hundreds of these already, but the reality is there is so much opportunity. >> Lisa: Right. >> The world is siloed, no technology has attempted to do that. And I think that's what's a exciting is to go and forge new grounds and do something very special to unite everyone together. >> You guys talked about the waves. Rob talked about the waves yesterday. You talked about it again this morning. And when I think of Inspired community, as that third wave, I see it on both sides. I see the Inspired community that is the Coupa community, but also what you just talked about, that flywheel of that sort of symbiotic relationship that you guys have with your customers as Coupa in and of itself being in a community inspired by the community that it has built. >> Raja: Yes, it's, it's very, very, it's a circular effect. Like it, we inspire one another, and we strengthen one another, and it's, it's just a beautiful, beautiful thing. One of the special things that we are starting to do is we want to take the whole product experience itself, to be a complete community experience. So anywhere you are going to Coupa, when it makes sense, of course, you are not only looking at your data, you are getting connected with people for that particular thing. So we've done that already for 15 different product areas and we're constantly doing more and more and more and more. You can imagine one day we can, where we can start within the product pages themselves, where we host community experts to talk via video and connect with others. So you bring that whole community experience alive in a product in enterprise software, which has not been done. >> Kind of like creating your own influencer network. >> Yes, yes, yes. And give people their voice and, and, and it becomes exciting. It is very different when you're just working on your own and driving goals, and you have no idea how good that can pass on the world. And then when right then and there, you get to learn that some people have hit that, some people have achieved these goals, you just get excited, "I want to hit that goal too. Who are these people? Connect me with these leaders. Let's have a conversation. How did they do it?" And they start creating best practices together. We even have started places where they collaborate on actual documents and templates, and they put them in the community exchange as a way for people to share with others, even taking templates from the product putting them back into a community exchange. So it is sharing, being enabled on the platform, platform itself. >> Lisa: How did you guys function during the pandemic, the last two years when we couldn't get together? >> Raja: Yeah. >> I know that your customers are really the lifeblood of Coupa and vice versa. >> Raja: Yes. >> But talk to me about some of the things that Coupa did with its customers, you know, by video conferencing, for example, that really helped the evolution and some of the innovations that you announced this morning. >> When we first... when the pandemic first hit I think like we all didn't believe what, what is going on. And there was this, I would call it a beautiful period in a way, despite how horrific that was, and that period was where everyone rose to the occasion, everybody wanted to help one another. Across Coupa everywhere, we started having documents of how can step up and help our customers, help our communities. We started to look at how we get PPE, and get it in the hands of our customers. We have access to suppliers. We started looking at helping suppliers with digital payments to speed things up. So, so many things we started doing as a community to just help each other. And then as we got to the next level, then we started, of course, starting to do things over, over zoom. And the big surprise, was we were incredibly productive. If anything, we were worried about people feeling burnt out. >> Yeah. >> Because they were just in it, completely in it. And it created a lot of new avenues for us because often you go and do these meetings in person. Now you could have a user experience session with a customer very easily, they're available more often than they used to. >> Lisa: Right. >> So we did not miss a beat with the community. We moved into virtual caps. We had the advantage of having them recorded as well, where we could have the global development teams learn and see exactly what the, what the customers are are co-creating together. And our goal lives accelerated, because a lot of these implementations, they used to happen in person, so schedules, they actually got accelerated- >> Lisa: Right. >> ...through that. Now of course, there is nothing that matches to this. You can do it, you can do a lot, but a ton of the collaboration comes from real life dialogue and kind of conversation. So it's that balance between the two that I think will be great. >> Lisa: What are some of the things that you've heard the last few days? You mentioned the Partners Summit and, and the Community Advisory Boards on Monday, yesterday, everything kicked off today. What are some of the things that you've heard in your meetings that really inspire you on say the next 10 years at Coupa? >> Raja: By far, by far, by far, it's a validation of, that what we are doing is, we're absolutely on target with it, and that, we just can do so much more. The silos are massive and there are so so many opportunities that you hear in every different areas that we could be doing this, we could be doing this together. So we can break down more and more silos. And using community.ai is just the tip of the iceberg of what we are, what we are doing. Yes, we created tens and tens of capabilities, helping, helping the community with all of that, but data drives everything. And when you look at that, every single process in every single silo can be informed by the power of data within your own company, and then even better, data across. And, and to the point where we're talking about concepts that customers are really excited about, even thinking about this community, they're customers of each other. And when you are a customer of each other what are the different ways as a community, you can help one another more. So we're talking about community netting as new types of concepts. >> Lisa: Talk to me a little about that. You mentioned the community netting this morning but I didn't quite... Help me understand. >> Raja: It is very simple terms is if, if we are buying from each other and we have to do money movements every time I have to pay you, I have to incur fees and likewise, but since we are part of this community we can manage that relationship. So we just pay the Delta, we net it out. So it, it saves reconciliation times it saves money movement. And these are tip of the icebergs of these very cool things that we're doing together. >> Wow. That's fantastic. Last question for you, as you talk with prospects who are in the early stages, or, or still determining, do we go through like a supply chain digital transformation? I mean, I think of companies that probably haven't now or need to get on the bandwagon. >> Raja: Yeah. >> What are some of the things that you advise to those customers to be able to do what Mick Ebeling talked about this morning and that is, commit and then figure it out? >> Raja: Yes. The number one thing is just make sure you don't do the analysis paralysis. There are just so many opportunities so many opportunities start with a project, get going, and it creates incredible momentum, and then you can move on from one to another, to another, to another, instead of trying to just go for a year or two, trying to look at how the world has changed in that process. And so often you could see that projects pay for themselves within the first month of go life. You do that, you'll create another one. And it's not like you are coming in to do something so new nobody has done. Hundreds and hundreds and thousands as a matter of fact, of other community members have done that. It is proven. So get started with those and then continue. Other things I will be talking to them about is to make sure that they understand the way we work is all about partnerships spread. Often people who haven't worked with us in the enterprise software, they're used to working with vendors. We are not that. We never were that. Like the number one, if we're not going to be real partners, honest, transparent and work with each other, we don't waste each other's time. >> Lisa: Well, Raja, it's been great having you on the program. I've really enjoyed your keynote this morning. Congratulations on your 10 years at Coupa. >> Raja: Thank you. >> I'm excited to see what the next 10 years brings for you. We appreciate your insites and everything that Coupa is doing in partnership with its customers is very evident in an event like this. >> Raja: Thank you. And thank you for coming and covering us as well. We really appreciate it. >> Lisa: It's our pleasure to be here. >> Thank you. >> For Raja Hammoud, I'm Lisa Martin. You're watching theCUBE's coverage, day two of Coupa Inspire 2022, from Las Vegas. (upbeat music)

>>mm we are back and we're continuing the coverage on H P E. Aruba's news today around the D S X six S C X 10-K with Pensando. Now we want to get the perspective of a system Integrator because they're in the front lines, they understand how to put the pieces together where you're happy to bring john Galatea of Dasher technologies dashes and in the end I. T solutions provider, they gotta focus a lot of expertise on infrastructure, jOHn welcome. Good to see you. >>Thank you for having me. Good to be here. >>That's our pleasure. So I wonder if you could give us a little bit more color on Dasher where you focus what your core companies competencies are, what industries you focus on etcetera. >>Yeah, absolutely. So a dasher, we assess architect implement and manage I. T. Solutions that digitally transform businesses. Our practice areas include cybersecurity, networking, cloud data center and we also offer pro professional services around those practice areas. We partner with all the major tech companies in the space. Some of the examples are HP, Cisco, Aruba Palo Alto eight Ws and many others that fill out the, you know that practice area. >>Well that's great. So you have a very wide observation space, that's why we like talking to SA as you have an independent mindset and you can kind of tell it like it is. But so what are you seeing with customers? It's exactly, we hear a lot about digital transformation, you mentioned security, you're obviously doing cloud that's it's almost like john these pieces are all coming together to power Digital and digital transformation and we were forced into it over the past 18 months. And now people are stepping back saying hey okay we have all these resources, how do we put them together and really transform our business? What do you see? >>Yeah, seeing similar things. So you know, our customers are telling us that they're looking for more speed, more agility, um you know, limited complexity because they're trying to do more every single day with less staffing and a sophistication of integrating functionality that breaks down I. T. Silos. Um there also evaluating security span versus effectiveness And they're moving towards zero trust. >>Yeah. So I want to, I'm gonna come back and ask you about that. So I've written a lot about this is that you look at how much we spend versus as you say the effectiveness and there's sort of an imbalance there, it's like we can't spend enough, it budgets they're not infinite. And even though security is top of top priority for ceos, they've got other things that they have to fund and then zero trust, you know, before the pandemic john that was a buzzword and now it's become a mandate. Any thoughts on that >>in terms of zero Trust? Absolutely yeah it is a mandate, we've seen more and more of our customers moving toward in this direction and defending themselves against cyber threats and yeah absolutely. It accelerated during the pandemic and is continuing to accelerate today. >>Right? And I think there's some things that were reported now going to be permanent with regard to obviously hybrid and the like, cloud security and so forth. So, okay, let's get into some of the news here. What's the big trend, john can you explain the relevance of the H P E, Aruba and Pensando news? >>Yeah, I mean when I first heard of it, you know, I I looked at it as a whole new category because it's a category that's going to deliver cloud scale distributed services closer to where applications are. It's going to simplify. One of the things we mentioned earlier was limiting complexity. So it simplifies the network um, by putting security provisions and operations in a unified management platform and it helps improve your security posture around moving towards zero trust and limits the appliance and vendor sprawl that you might ordinarily have in a in a existing network today. >>Okay, so that's kind of the business cases, you're consolidating a lot of piece parts and that's, you know, from a system integrator standpoint. You know, it's funny people often say, well, isn't that bad for the s I'm like, no, they don't want to be in the business of plumbing, they want to be in the business of, you know, more strategy if they if they just end up bolting stuff together, they're going to go out of business, They need to extend their value. So as a strategic partner, you got an early preview of this launch? The D S S D S S C X 10,000, what was your initial impression reaction you called it? A new category? What do you mean by that? >>Well, it's a new category of of of a data center switch in the digital infrastructure because it includes or incorporates security. Um And more specifically it includes security around east west traffic, which is it doesn't eliminate your perimeter firewall but it actually incorporates more functionality which leads to better simplicity and easier use of management of a platform. So for us, I'm really excited to go position and talk to our clients about this. >>Yes. So we're seeing the flattening of that network, that's even it's obviously been accelerated by the pandemic, everybody talks about that. But if you think about the traditional headquarter hierarchical network and now all of a sudden everybody's working remotely using more cloud. Using more distributed infrastructure that flattens the network. That creates security challenges because you can't just build a perimeter and say, okay, we're safe. You now have to go to where the adversary is and that's everywhere. So what's your sense as to how customers are going to react to this new category of switch? >>I think really my sense is that I've got a really positive outlook on this product. I mean hardware, firewalls are costly and deploying software agents can be very disruptive and when you're integrating it into the switch layer. So um I think the C X 10,000 provides a great alternative to an embedded accelerated services embedded in accelerate service into the D C fabric. Um, it's great for brownfield migration, um, rack pod and you know, and the standards based leaf, you know, L two, L three um, and it doesn't necessarily replace, as I mentioned earlier the perimeter security, but um, it can cap and grow with DSS and east west firewall traffic. >>Yeah. And I think we've seen when we talked to see so, so like you said, it does, it doesn't replace the traditional perimeter security but you're going to see a shift and spending priorities obviously to a comedy because as I said earlier, there's not infinite budget but john give us the big takeaway, Bring us home. What what, what do you want to leave our audience with? >>Yeah, I think, you know, the number one takeaway is that it's a massive opportunity to reduce complexity, enhanced security and lower costs in the data center by eliminating dedicated devices and embedding services through software capability in the network closer to where workloads are are moving. So that's the big takeaway for me and for, I think for our clients, um, you know, other things are, you know, you're the data center perimeter is no longer confined and open an on prem location but extends out, right. We're seeing customers extend out to the cloud and across uh, you know, disparate locations, co locations. So The traditional architecture isn't going to be well suited for this, and I think the CX- 10,000 and its feature set are going to be really great for addressing the changing market. >>Yeah, that's, that's all. I mean, again, we're seeing the democratization of everything and and networking is, is no exception. The notion of simplify simplification, john really appreciate your time. Thanks for coming on. >>Thank you for having me. >>You're welcome. Okay, keep it right. There were unpacking the changing trends in networking generally, and specifically switch networking with HP, Aruba and Pensando and the cube. Keep it right there.

(upbeat electronic music plays) >> Hey everyone, welcome back to theCube's coverage of .NEXT 2021 Virtual. I'm John Furrier, hosts of theCube. We have two great guests, Rajiv Mirani, who's the Chief Technology Officer, and Thomas Cornely, SVP of Product Management. Day Two keynote product, the platform, announcements, news. A lot of people, Rajiv, are super excited about the, the platform, uh, moving to a subscription model. Everything's kind of coming into place. How are the customers, uh, seeing this? How they adopted hybrid cloud as a hybrid, hybrid, hybrid, data, data, data? Those are the, those are the, that's the, that's where the puck is right now. You guys are there. How are customers seeing this? >> Mirani: Um, um, great question, John, by the way, great to be back here on theCube again this year. So when we talk to our customers, pretty much, all of them agreed that for them, the ideal state that they want to be in is a hybrid world, right? That they want to essentially be able to run both of those, both on the private data center and the public cloud, and sort of have a common platform, common experience, common, uh, skillset, same people managing, managing workloads across both locations. And unfortunately, most of them don't have that that tooling available today to do so, right. And that's where the platform, the Nutanix platform's come a long way. We've always been great at running in the data center, running every single workload, we continue to make great strides on our core with the increased performance for, for the most demanding, uh, workloads out there. But what we have done in the last couple of years has also extended this platform to run in the public cloud and essentially provide the same capabilities, the same operational behavior across locations. And that's when you're seeing a lot of excitement from our customers because they really want to be in that state, for it to have the common tooling across work locations, as you can imagine, we're getting traction. Customers who want to move workloads to public cloud, they don't want to spend the effort to refactor them. Or for customers who really want to operate in a hybrid mode with things like disaster recovery, cloud bursting, workloads like that. So, you know, I think we've made a great step in that direction. And we look forward to doing more with our customers. >> Furrier: What is the big challenge that you're seeing with this hybrid transition from your customers and how are you solving that specifically? >> Mirani: Yeah. If you look at how public and private operate today, they're very different in the kind of technologies used. And most customers today will have two separate teams, like one for their on-prem workloads, using a certain set of tooling, a second completely different team, managing a completely different set of workloads, but with different technologies. And that's not an ideal state in some senses, that's not true hybrid, right? It's like creating two new silos, if anything. And our vision is that you get to a point where both of these operate in the same manner, you've got the same people managing all of them, the same workloads anyway, but similar performance, similar SaaS. So they're going to literally get to point where applications and data can move back and forth. And that's, that's, that's where I think the real future is for hybrid >> Furrier: I have to ask you a personal question. As the CTO, you've got be excited with the architecture that's evolving with hybrid and multi-cloud, I mean, I mean, it's pretty, pretty exciting from a tech standpoint, what is your reaction to that? >> Mirani: %100 and it's been a long time coming, right? We have been building pieces of this over years. And if you look at all the product announcements, Nutanix has made over the last few years and the acquisitions that made them and so on, there's been a purpose behind them. That's been a purpose to get to this model where we can operate a customer's workloads in a hybrid environment. So really, really happy to see all of that come together. Years and years of work finally finally bearing fruit. >> Furrier: Well, we've had many conversations in the past, but it congratulates a lot more to do with so much more action happening. Thomas, you get the keys to the kingdom, okay, and the product management you've got to prioritize, you've got to put it together. What are the key components of this Nutanix cloud platform? The hybrid cloud, multi-cloud strategy that's in place, because there's a lot of headroom there, but take us through the key components today and then how that translates into hybrid multi-cloud for the future. >> Cornely: Certainly, John, thank you again and great to be here, and kind of, Rajiv, you said really nicely here. If you look at our portfolio at Nutanix, what we have is great technologies. They've been sold as a lot of different products in the past, right. And what we've done last few months is we kind of bring things together, simplify and streamline, and we align everything around a cloud platform, right? And this is really the messaging that we're going after is look, it's not about the price of our solutions, but business outcomes for customers. And so are we focusing on pushing the cloud platform, which we encompasses five key areas for us, which we refer to as cloud infrastructure, no deficiencies running your workloads. Cloud management, which is how you're going to go and actually manage, operate, automate, and get governance. And then services on top that started on all around data, right? So we have unified storage, finding the objects, data services. We have database services. Now we have outset of desktop services, which is for EMC. So all of this, the big change for us is this is something that, you know, you can consume in terms of solutions and consume on premises. As Rajiv discussed, you know, we can take the same platform and deploy it in public cloud regions now, right? So you can now get no seamless hybrid cloud, same operating model. But increasingly what we're doing is taking your solutions and re-targeting issues and problems at workers running native public clouds. So think of this as going, after automating more governance, security, you know, finding objects, database services, wherever you're workload is running. So this is taking this portfolio and reapplying it, and targeting on prem at the edge in hybrid and in christening public cloud in ATV. >> Furrier: That's awesome. I've been watching some of the footage and I was noticing quite a lot of innovation around virtualized, networking, disaster, recovery security, and data services. It's all good. You guys were, and this is in your wheelhouse. I know you guys are doing this for many, many years. I want to dive deeper into that because the theme right now that we've been reporting on, you guys are hitting right here what the keynote is cloud scale is about faster development, right? Cloud native is about speed, it's about not waiting for these old departments, IT or security to get back to them in days or weeks and responding to either policy or some changes, you got to move faster. And data, data is critical in all of this. So we'll start with virtualized networking because networking again is a key part of it. The developers want to go faster. They're shifting left, take us through the virtualization piece of how important that is. >> Mirani: Yeah, that's actually a great question as well. So if you think about it, virtual networking is the first step towards building a real cloud like infrastructure on premises that extends out to include networking as well. So one of the key components of any cloud is automation. Another key component is self service and with the API, is it bigger on virtual networking All of that becomes much simpler, much more possible than having to, you know, qualify it, work with someone there to reconfigure physical networks and slots. We can, we can do that in a self service way, much more automated way. But beyond that, the, the, the notion of watching networks is really powerful because it helps us to now essentially extend networks and, and replicate networks anywhere on the private data center, but in the public cloud as well. So now when customers move their workloads, we'd already made that very simple with our clusters offering. But if you're only peek behind the layers a little bit, it's like, well, yea, but the network's not the same on the side. So now it, now it means that a go re IP, my workloads create new subnets and all of that. So there was a little bit of complication left in that process. So to actual network that goes away also. So essentially you can repeat the same network in both locations. You can literally move your workloads, no redesign of your network acquired and still get that self service and automation capabilities of which cookies so great step forward, it really helps us complete the infrastructure as a service stack. We had great storage capabilities before, we create compute capabilities before, and sort of networking the third leg and all of that. >> Furrier: Talk about the complexity here, because I think a lot of people will look at dev ops movement and say, infrastructure is code when you go to one cloud, it's okay. You can, you can, you know, make things easier. Programmable. When, when you start getting into data center, private data centers, or essentially edges now, cause if it's distributed cloud environment or cloud operations, it's essentially one big cloud operation. So the networks are different. As you said, this is a big deal. Okay. This is sort of make infrastructure as code happen in multiple environments across multiple clouds is not trivial. Could you talk about the main trends and how you guys see this evolving and how you solve that? >> Mirani: Yeah. Well, the beauty here is that we are actually creating the same environment everywhere, right? From, from, from point of view of networking, compute, and storage, but also things like security. So when you move workloads, things with security, posture also moves, which is also super important. It's a really hard problem, and something a lot of CIO's struggle with, but having the same security posture in public and private clouds reporting as well. So with this, with this clusters offering and our on-prem offering competing with the infrastructure service stack, you may not have this capability where your operations really are unified across multicloud hybrid cloud in any way you run. >> Furrier: Okay, so if I have multiple cloud vendors, there are different vendors. You guys are creating a connection unifying those three. Is that right? >> Mirani: Essentially, yes, so we're running the same stack on all of them and abstracting away the differences between the clouds that you can run operations. >> Furrier: And when the benefits, the benefits of the customers are what? What's the main, what's the main benefit there? >> Mirani: Essentially. They don't have to worry about, about where their workloads are running. Then they can pick the best cloud for their workloads. It can seamlessly move them between Cloud. They can move their data over easily, and essentially stop worrying about getting locked into a single, into a single cloud either in a multi-cloud scenario or in a hybrid cloud scenario, right. There many, many companies now were started on a cloud first mandate, but over time realized that they want to move workloads back to on-prem or the other way around. They have traditional workloads that they started on prem and want to move them to public cloud now. And we make that really simple. >> Furrier: Yeah. It's kind of a trick question. I wanted to tee that up for Thomas, because I love that kind of that horizontal scales, what the cloud's all about, but when you factor data into it, this is the sweet spot, because this is where, you know, I think it gets really exciting and complicated too, because, you know, data's got, can get unwieldy pretty quickly. You got state got multiple applications, Thomas, what's your, what can you share the data aspect of this? This is super, super important. >> Absolutely. It's, you know, it's really our core source of differentiation, when you think about it. That's what makes Nutanix special right? In, in the market. When we talk about cloud, right. Actually, if you've been following Nutanix for years, you know, we've been talking a lot about making infrastructure invisible, right? The new way for us to talk about what we're doing, with our vision is, is to make clouds invisible so that in the end, you can focus on your own business, right? So how do you make Cloud invisible? Lots of technology is at the application layer to go and containerize applications, you know, make them portable, modernize them, make them cloud native. That's all fine when you're not talking of state class containers, that the simplest thing to move around. Right. But as we all know, you know, applications end of the day, rely on data and measure the data across all of these different locations. I'm not even going to go seconds. Cause that's almost a given, you're talking about attribution. You can go straight from edge to on-prem to hybrid, to different public cloud regions. You know, how do you go into the key control of that and get consistency of all of this, right? So that's part of it is being aware of where your data is, right? But the other part is that inconsistency of set up data services regardless of where you're running. And so this is something that we look at the cloud platform, where we provide you the cloud infrastructure go and run the applications. But we also built into the cloud platform. You get all of your core data services, whether you have to consume file services, object services, or database services to really support your application. And that will move with your application, that is the key thing here by bringing everything onto the same platform. You now can see all operations, regardless of where you're running the application. The last thing that we're adding, and this is a new offering that we're just launching, which is a service, it's called, delete the dead ends. Which is a solution that gives you visibility and allow you to go and get better governance around all your data, wherever it may live, across on-prem edge and public clouds. That's a big deal again, because to manage it, you first have to make sense of it and get control over it. And that's what data answer's is going to be all about. >> Furrier: You know, one of the things we've we've been reporting on is data is now a competitive advantage, especially when you have workflows involved, um, super important. Um, how do you see customers going to the edge? Because if you have this environment, how does the data equation, Thomas, go to the edge? How do you see that evolving? >> Cornely: So it's yeah. I mean, edge is not one thing. And that's actually the biggest part of the challenge of defining what the edge is depending on the customer that you're working with. But in many cases you get data ingesting or being treated at the edge that you then have to go move to either your private cloud or your public cloud environment to go and basically aggregate it, analyze it and get insights from it. Right? So this is where a lot of our technologies, whether it's, I think the object's offering built in, we'll ask you to go and make the ingest over great distances over the network, right? And then have your common data to actually do an ethics audit over our own object store. Right? Again, announcements, we brought into our storage solutions here, we want to then actually organize it then actually organize it directly onto the objects store solution. Nope. Using things, things like or SG select built into our protocols. So again, make it easy for you to go in ingest anywhere, consolidate your data, and then get value out of it. Using some of the latest announcements on the API forms. >> Furrier: Rajiv databases are still the heart of most applications in the enterprise these days, but databases are not just the data is a lot of different data. Moving around. You have a lot a new data engineering platforms coming in. A lot of customers are scratching their head and, and they want to kind of be, be ready and be ready today. Talk about your view of the database services space and what you guys are doing to help enterprise, operate, manage their databases. >> Mirani: Yeah, it's a super important area, right? I mean, databases are probably the most important workload customers run on premises and pretty close on the public cloud as well. And if you look at it recently, the tooling that's available on premises, fairly traditional, but the clouds, when we integrate innovation, we're going to be looking at things like Amazon's relational database service makes it an order of magnitude simpler for our customers to manage the database. At the same time, also a proliferation of databases and we have the traditional Oracle and SQL server. But if you have open source Mongo, DB, and my SQL, and a lot of post-grads, it's a lot of different kinds of databases that people have to manage. And now it just becomes this cable. I have the spoke tooling for each one of them. So with our Arab product, what we're doing is essentially creating a data management layer, a database management layer that unifies operations across your databases and across locations, public cloud and private clouds. So all the operations that you need, you do, which are very complicated in, in, in, in with traditional tooling now, provisioning of databases backing up and restoring them providing a true time machine capabilities, so you can pull back transactions. We can copy data management for your data first. All of that has been tested in Era for a wide variety of database engines, your choice of database engine at the back end. And so the new capabilities are adding sort of extend that lead that we have in that space. Right? So, so one of the things we announced at .Next is, is, is, is one-click storage scaling. So one of the common problems with databases is as they grow over time, it's not running out of storage capacity. Now re-provisions to storage for a database, migrate all the data where it's weeks and months of look, right? Well, guess what? With Era, you can do that in one click, it uses the underlying AOS scale-out architecture to provision more storage and it does it have zero downtime. So on the fly, you can resize your databases that speed, you're adding some security capabilities. You're adding some capabilities around resilience. Era continues to be a very exciting product for us. And one of the things, one of the real things that we are really excited about is that it can really unify database operations between private and public. So in the future, we can also offer an aversion of Era, which operates on native public cloud instances and really excited about that. >> Furrier: Yeah. And you guys got that two X performance on scaling up databases and analytics. Now the big part point there, since you brought up security, I got to ask you, how are you guys talking about security? Obviously it's embedded in from the beginning. I know you guys continue to talk about that, but talk about, Rajiv, the security on, on that's on everyone's mind. Okay. It goes evolving. You seeing ransomware are continuing to happen more and more and more, and that's just the tip of the iceberg. What do you guys, how are you guys helping customers stay secure? >> Mirani: Security is something that you always have to think about as a defense in depth when it comes to security, right? There's no one product that, that's going to do everything for you. That said, what we are trying to do is to essentially go with the gamut of detection, prevention, and response with our security, and ransom ware is a great example of that, right. We've partnered with Qualys to essentially be able to do a risk assessment of your workloads, to basically be able to look into your workloads, see whether they have been bashed, whether they have any known vulnerabilities and so on. To try and prevent malware from infecting your workloads in the first place, right? So that's, that's the first line of defense. Now not systems will be perfect. Some, some, some, some malware will probably get in anyway But then you detect it, right. We have a database of all the 4,000 ransomware signatures that you can use to prevent ransomware from, uh, detecting ransom ware if it does infect the system. And if that happens, we can prevent it from doing any damage by putting your fire systems and storage into read-only mode, right. We can also prevent lateral spread of, of your ransomware through micro-segmentation. And finally, if you were, if you were to invade, all those defenses that you were actually able to encrypt data on, on, on a filer, we have immutable snapshots, they can recover from those kinds of attacks. So it's really a defense in depth approach. And in keeping with that, you know, we also have a rich ecosystem of partners while this is one of them, but older networks market sector that we work with closely to make sure that our customers have the best tooling around and the simplest way to manage security of their infrastructure. >> Furrier: Well, I got to say, I'm very impressed guys, by the announcements from the team I've been, we've been following Nutanix in the beginning, as you know, and now it's back in the next phase of the inflection point. I mean, looking at my notebook here from the announcements, the VPC virtual networking, DR Observability, zero trust security, workload governance, performance expanded availability, and AWS elastic DR. Okay, we'll get to that in a second, clusters on Azure preview cloud native ecosystem, cloud control plane. I mean, besides all the buzzword bingo, that's going on there, this is cloud, this is a cloud native story. This is distributed computing. This is virtualization, containers, cloud native, kind of all coming together around data. >> Cornely: What you see here is, I mean, it is clear that it is about modern applications, right? And this is about shifting strategy in terms of focusing on the pieces where we're going to be great at. And a lot of these are around data, giving you data services, data governance, not having giving you an invisible platform that can be running in any cloud. And then partnering, right. And this is just recognizing what's going on in the world, right? People want options, customers and options. When it comes to cloud, they want options to where they're running the reports, what options in terms of, whether it be using to build the modern applications. Right? So our big thing here is providing and being the best platform to go and actually support for Devers to come in and build and run their new and modern applications. That means that for us supporting a broad ecosystem of partners, entrepreneur platform, you know, we announced our partnership with Red Hat a couple of months ago, right? And this is going to be a big deal for us because again, we're bringing two leaders in the industry that are eminently complimentary when it comes to providing you a complete stack to go and build, run, and manage your client's applications. When you do that on premises, utilizing like the preferred ATI environment to do that. Using the Red Hat Open Shift, or, you're doing this open to public cloud and again, making it seamless and easy, to move the applications and their supporting data services around, around them that support them, whether they're running on prem in hybrid winter mechanic. So client activity is a big deal, but when it comes to client activity, the way we look at this, it's all about giving customers choice, choice of that from services and choice of infrastructure service. >> Furrier: Yeah. Let's talk to the red hat folks, Rajiv, it's you know, it's, they're an operating system thinking company. You know, you look at the internet now in the cloud and edge, and on-premise, it's essentially an operating system. you need your backup and recovery needs to disaster recovery. You need to have the HCI, you need to have all of these elements part of the system. It's, it's, it's, it's building on top of the existing Nutanix legacy, then the roots and the ecosystem with new stuff. >> Mirani: Right? I mean, it's, in fact, the Red Hat part is a great example of, you know, the perfect marriage, if you will, right? It's, it's, it's the best in class platform for running the cloud-native workloads and the best in class platform with a service offering in there. So two really great companies coming together. So, so really happy that we could get that done. You know, the, the point here is that cloud native applications still need infrastructure to run off, right? And then that infrastructure, if anything, the demands on that and growing it since it's no longer that hail of, I have some box storage, I have some filers and, you know, just don't excite them, set. People are using things like object stores, they're using databases increasingly. They're using the Kafka and Map Reduce and all kinds of data stores out there. And back haul must be great at supporting all of that. And that's where, as Thomas said, earlier, data services, data storage, those are our strengths. So that's certainly a building from platform to platform. And then from there onwards platform services, great to have right out of the pocket. >> Furrier: People still forget this, you know, still hardware and software working together behind the scenes. The old joke we have here on the cube is server less is running on a bunch of servers. So, you know, this is the way that is going. It's really the innovation. This is the infrastructure as code truly. This is what's what's happened is super exciting. Rajiv, Thomas, thank you guys for coming on. Always great to talk to you guys. Congratulations on an amazing platform. You guys are developing. Looks really strong. People are giving it rave reviews and congratulations on, on, on your keynotes. >> Cornely: Thank you for having us >> Okay. This is theCube's coverage of.next global virtual 2021 cube coverage day two keynote review. I'm John Furrier Furrier with the cube. Thanks for watching.

(upbeat music) >> Hi, today I'm going to talk about network analytics and what that means for telecommunications as we go forward, thinking about 5G, what the impact that's likely to have on network analytics and the data requirement, not just to run the network and to understand the network a little bit better, but also to inform the rest of the operation of the telecommunications business. So as we think about where we are in terms of network analytics and what that is over the last 20 years, the telecommunications industry has evolved its management infrastructure to abstract away from some of the specific technologies in the network. So what do we mean by that? Well, in the, when initial telecommunications networks were designed there were management systems that were built in. Eventually fault management systems, assurance systems, provisioning systems, and so on, were abstracted away. So it didn't matter what network technology had, whether it was a Nokia technology or Erickson technology or Huawei technology or whoever it happened to be. You could just look at your fault management system and understand where faults were happened. As we got into the last sort of 10, 15 years or so telecommunication service providers become, became more sophisticated in terms of their approach to data analytics and specifically network analytics and started asking questions about why and what if in relation to their network performance and network behavior. And so network analytics as a sort of an independent functioning was born and over time more and more data began to get loaded into the network analytics function. So today just about every carrier in the world has a network analytics function that deals with vast quantities of data in big data environments that are now being migrated to the cloud. As all telecommunications carriers are migrating as many IT workloads as possible to the cloud. So what are the things that are happening as we migrate to the cloud that drive enhancements in use cases and enhancements in scale in telecommunications network analytics? Well, 5G is the big thing, right? So 5G, it's not just another G in that sense. I mean, in some cases, in some senses it is 5G means greater bandwidth and lower latency and all those good things. So, you know, we can watch YouTube videos with less interference and, and less sluggish bandwidth and so on and so forth. But 5G is really about the enterprise and enterprise services transformation. 5G is a more secure kind of a network, but 5G is also a more pervasive network. 5G has a fundamentally different network topology than previous generations. So there's going to be more masts. And that means that you can have more pervasive connectivity. So things like IOT and edge applications, autonomous car, current smart cities, these kinds of things are all much better served because you've got more masts, that of course means that you're going to have a lot more data as well and we'll get to that. The second piece is immersive digital services. So with more masts, with more connectivity, with lower latency, with higher bandwidth with the potential is immense for services innovation. And we don't know what those services are going to be. We know that technologies like augmented reality, virtual reality, things like this have great potential, but we have yet to see where those commercial applications are going to be, but the innovation and the innovation potential for 5G is phenomenal. It certainly means that we're going to have a lot more edge devices. And that again is going to lead to an increase in the amount of data that we have available. And then the idea of pervasive connectivity when it comes to smart cities, autonomous cars, integrated traffic management systems, all of this kind of stuff, those kind of smart environments thrive where you've got this kind of pervasive connectivity, this persistent connection to the network. Again, that's going to drive more innovation. And again, because you've got these new connected devices, you're going to get even more data. So this rise, this exponential rise in data is really what's driving the change in network analytics. And there are four major vectors that are driving this increase in data in terms of both volume and in terms of speed. So the first thing is more physical elements. So we said already that 5G networks are going to have a different topology. 5G networks will have more devices, more masts. And so with more physical elements in the network, you're going to get more physical data coming off those physical networks. And so that needs to be aggregated and collected and managed and stored and analyzed and understood so that we can have a better understanding as to, you know, why things happened the way they do, why the network behaves in which they do in ways that it does and why devices that are connected to the network and ultimately of course, consumers, whether they be enterprises or retail customers behave in the way they do in relation to their interaction with the network. Edge nodes and devices. We're going to have an explosion in terms of the number of devices. We've already seen IOT devices with your different kinds of trackers and sensors that are hanging off the edge of the network, whether it's to make buildings smarter or car smart or people smarter in terms of having the measurements and the connectivity and all that sort of stuff. So the numbers of devices on the edge and beyond the edge are going to be phenomenal. One of the things that we've been trying to wrestle with as an industry over the last few years is where does a telco network end and where does the enterprise, or even the consumer network begin? It used to be very clear that, you know, the telco network ended at the router but now it's not that clear anymore because in the enterprise space, particularly with virtualized networking, which we're going to talk about in a second, you start to see end to end network services being deployed. And so are they being those services in some instances that are being managed by the service provider themselves, and in some cases by the enterprise client. Again, the line between where the telco network ends and where the enterprise or the consumer network begins is not clear. So those edge, the proliferation of devices at the edge, in terms of, you know, what those devices are, what the data yield is and what the policies are that need to govern those devices in terms of security and privacy and things like that, that's all going to be really, really important. Virtualized services, we just touched on that briefly. One of the big, big trends that's happening right now is not just the shift of IT operations onto the cloud, but the shift of the network onto the cloud, the virtualization of network infrastructure. And that has two major impacts. First of all, it means that you've got the agility and all of the scale benefits that you get from migrating workloads to the cloud, the elasticity and the growth and all that sort of stuff, but arguably more importantly for the telco it means that with a virtualized network infrastructure, you can offer entire networks to enterprise clients. So, you know, selling to a government department, for example, who's looking to stand up a system for, you know, certification of, you know, export certification, something like that. You can not just sell them the connectivity, but you can sell them the networking and the infrastructure in order to serve that entire end to end application. You could send, you could offer them in theory, an entire end-to-end communications network. And with 5G network slicing, they can even have their own little piece of the 5G bandwidth that's been allocated to gets a carrier and have a complete end to end environment. So the kinds of services that can be offered by telcos given virtualize network infrastructure are many and varied and it's an outstanding opportunity. But what it also means is that the number of network elements virtualized in this case is also exploding. And that means the amount of data that we're getting on, informing us as to how those network elements are behaving, how they're performing is going to go up as well. And then finally, AI complexity. So on the demand inside while historically network analytics, big data has been driven by returns in terms of data monetization, whether that's through cost avoidance or service assurance, or even revenue generation through data monetization and things like that. AI is transforming telecommunications and every other industry. The potential for autonomous operations is extremely attractive. And so understanding how the end-to-end telecommunication service delivery infrastructure works is essential as a training ground for AI models that can help to automate a huge amount of telecommunications operating processes. So the AI demand for data is just going through the roof. And so all of these things combined to mean that big data is getting explosive. It is absolutely going through the roof. So that's a huge thing that's happening. So as telecommunications companies around the world are looking at their network analytics infrastructure, which was initially designed for service insurance primarily and how they migrate that to the cloud. These things are impacting on those decisions because you're not just looking at migrating a workload to operate in the cloud that used to work in the data center. Now you're looking at migrating a workload but also expanding the use cases in that workload. And bear in mind many of those workloads are going to need to remain on-prem. So they'll need to be within a private cloud or at best a hybrid cloud environment in order to satisfy regulatory jurisdiction requirements. So let's talk about an example. So LG Uplus is fantastic service provider in Korea, huge growth in that business over the last, over the last 10, 15 years or so. And obviously most people would be familiar with LG, the electronics brand, maybe less so with, with LG Uplus, but they've been doing phenomenal work and were the first business in the world to launch commercial 5G in 2019. And so a huge milestone that they achieved. And at the same time they deployed the Network Real-time Analytics Platform or NRAP from a combination of Cloudera and our partner Caremark . Now, there were a number of things that were driving the requirement for the analytics platform at the time. Clearly the 5G launch was the big thing that they had in mind, but there were other things that were at play as well. So within the 5G launch, they were looking for a visibility of services and service assurance and service quality. So, you know, what services have been launched? How are they being taken up? What are the issues that are arising? Where the faults happening? Where are the problems? Because clearly when you launch a new service like that you want to understand and be on top of the issues as they arise. So that was really, really important. A second piece was and, you know, this is not a new story to any telco in the world, right? But there are silos in operation. And so it taking advantage of, or eliminating redundancies through the process of digital transformation it was really important. And so particular, the two silos between wired and the wireless sides of the business needed to come together so that there would be an integrated network management system for LG Uplus as they rolled out 5G. So eliminating redundancy and driving cost savings through the integration of the silos was really, really important. And that's a process and the people think every bit, as much as it is a systems and a data thing so another big driver. And the fourth one, you know, we've talked a little bit about some of these things, right? 5G brings huge opportunity for enterprise services innovation. So industry 4.0, digital experience, these kinds of use cases were very important in the South Korean market and in the business of LG Uplus And so looking at AI and how can you apply AI to network management? Again, there's a number of use cases, really, really exciting use cases that have gone live now in LG Uplus since we did this initial deployment and they're making fantastic strides there. Big data analytics for users across LG Uplus, right? So it's not just for, it's not just for the immediate application of 5G or the support or the 5G network, but also for other data analysts and data scientists across the LG Uplus business. Network analytics while primarily it's, it's primary use case is around network management. LG Uplus or network analytics has applications across the entire business, right? So, you know, for customer churn or next best offer for understanding customer experience and customer behavior really important there for digital advertising, for product innovation, all sorts of different use cases and departments within the business needed access to this information. So collaboration sharing across the network, the real-time network analytics platform it was very important. And then finally, as I mentioned, LG group is much bigger than just LG Uplus. It's got the electronics and other pieces, and they had launched a major group wide digital transformation program in 2019. And so being a part of that was important as well. Some of the seems that they were looking to address. So first of all, the integration of wired and wireless data sources, and so getting your assurance data sources, your network data sources and so on integration was really, really important. Scale was massive for them. You know, they're talking about billions of transactions in under a minute being processed and hundreds of terabytes per day. So, you know, phenomenal scale that needed to be, you know, available out of the box as it were. Real time indicators and alarms. And there was lots of KPIs and thresholds set that, you know, to make, made to meet certain criteria, certain standards. Customer specific real time analysis of 5G, services particularly for the launch, root cause analysis and AI based prediction on service anomalies and service issues was a core use case. As I talked about already the provision of service, of data services across the organization. And then support for 5G, served the business service impact was extremely important. So it's not just understand, well, you know, that you have an outreach in a particular network element, but what is the impact on the business of LG Uplus, but also what is the impact on the business of the customer from an outage or an anomaly or a problem on the network. So being able to answer those kinds of questions really, really important too. And as I said between Cloudera and Caremark and LG Uplus they have already, themselves an intrinsic part of the solution, this is what we ended up building. So a big, complicated architecture side. I really don't want to go into too much detail here. You can see these things for yourself, but let me skip through it really quickly. So, first of all, the key data sources. You have all of your wireless network information, other data sources, this is really important 'cause sometimes you kind of skip over this. There are other systems that are in place like the enterprise data warehouse that needed to be integrated as well. Southbound and northbound interfaces. So we get our data, yo know, from the network and so on and network management applications through both file interfaces, Kafka, NiFi are important technologies. And also the RDBMS systems that, you know, like the enterprise data warehouse that we're able to feed that into the system. And then northbound, you know, we spoke already about making network analytics services available across the enterprise. So, you know, having both a file and API interface available for other systems and other consumers across the enterprise is very important. Lots of stuff going on then in the platform itself. Two petabytes and persistent storage, Cloudera HDFS, 300 nodes for the raw data storage and then Kudu for real time storage for, you know, real-time indicator analysis around generation and other real time processes. So there was the core of the solution spark processes for ETL, key quality indicators and alarming, and also a bunch of work done around data preparation, data generation for transferal to, for party systems through the northbound interfaces. Impala API queries for real-time systems there on the right hand side and then a whole bunch of clustering classification, prediction jobs through the ML processes, the machine learning processes. Again, another key use case, and we've done a bunch of work on that, and I encourage you to have a look at the Cloudera website for more detail on some of the work that we did here. Some pretty cool stuff. And then finally, just the upstream services, some of these, there's lots more than simply these ones, but service assurance is really, really important so SQM, CEM and ACD right to the service quality management customer experience autonomous control is really, really important consumers of the real-time analytics platform and your conventional service assurance functions like faulted performance management. These things are as much consumers of the information and the network analytics platform as they are providers of data to the network analytics platform. So some of the specific use cases that have been stood up and that are delivering value to this day and there's lots of more besides, but these are just three that we pulled out. So, first of all, sort of specific monitoring and customer quality analysis care and response. So again, growing from the initial 5G launch, and then broadening into broader services, understanding where there are issues so that when people complain, when people have an issue that we can answer the concerns of the client in a substantive way. AI functions around root cause analysis understanding why things went wrong when they went wrong and also making recommendations as to how to avoid those occurrences in the future. So, you know, what preventative measures can be taken. And then finally, the collaboration function across LG Uplus extremely important and continues to be important to this day where data is shared throughout the enterprise, through the API Lira, through file interfaces and other things and through interface integrations with upstream systems. So that's kind of the real quick run through of LG Uplus. And the numbers are just staggering. You know, we've seen upwards of a billion transactions in under 40 seconds being tested. And we've gone through beyond those thresholds now already, and we're start, and this isn't just a theoretical sort of a benchmarking test or something like that. We're seeing these kinds of volumes of data and not too far down the track. So with those things that I mentioned earlier or with the proliferation of network infrastructure in the 5G context with virtualized elements, with all of these other bits and pieces are driving massive volumes of data towards the network analytics platform. So phenomenal scale. This is just one example. We work with service providers all over the world is over 80% of the top 100 telecommunication service providers run on Cloudera. They use Cloudera in the network and we're seeing those customers all migrating Legacy Cloudera platforms now onto CDP onto the Cloudera Data Platform. They're increasing the jobs that they do. So it's not just warehousing, not just ingestion of ETL and moving into things like machine learning. And also looking at new data sources from places like NW DAF the network data analytics function in 5G or the management and orchestration layer in software defined network function virtualization. So, you know, new use cases coming in all the time, new data sources coming in all the time, growth in the application scope from, as we say, from edge to AI. And so it's really exciting to see how the footprint is growing and how the applications in telecommunications are really making a difference in facilitating network transformation. And that's covering, that's me covered for today. I hope you found that helpful. By all means please reach out. There's a couple of links here. You can follow me on Twitter. You can connect to the telecommunications page. Reach out to me directly at Cloudera I'd love to answer your questions and talk to you about how big data is transforming networks and how network transformation is accelerating telcos throughout the world.

>>Hi today, I'm going to talk about network analytics and what that means for, for telecommunications as we go forward. Um, thinking about, uh, 5g, what the impact that's likely to have on, on network analytics and the data requirement, not just to run the network and to understand the network a little bit better. Um, but also to, to inform the rest of the operation of the telecommunications business. Um, so as we think about where we are in terms of network analytics and what that is over the last 20 years, the telecommunications industry has evolved its management infrastructure, uh, to abstract away from some of the specific technologies in the network. So what do we mean by that? Well, uh, in the, in the initial, uh, telecommunications networks were designed, there were management systems that were built in, um, eventually fault management systems, uh, assurance systems, provisioning systems, and so on were abstracted away. >>So it didn't matter what network technology had, whether it was a Nokia technology or Erickson technology or Huawei technology or whatever it happened to be. You could just look at your fault management system, understand where false, what happened as we got into the last sort of 10, 15 years or so. Telecommunication service providers become became more sophisticated in terms of their approach to data analytics and specifically network analytics, and started asking questions about why and what if in relation to their network performance and network behavior. And so network analytics as a, as a bit of an independent function was born and over time, more and more data began to get loaded into the network analytics function. So today just about every carrier in the world has a network analytics function that deals with vast quantities of data in big data environments that are now being migrated to the cloud. >>As all telecommunications carriers are migrating as many it workloads as possible, um, to the cloud. So what are the things that are happening as we migrate to the cloud that drive, uh, uh, enhancements in use cases and enhancements and scale, uh, in telecommunications network analytics? Well, 5g is the big thing, right? So 5g, uh, it's not just another G in that sense. I mean, in some cases, in some senses, it is 5g means greater bandwidth, lower latency and all those good things. So, you know, we can watch YouTube videos with less interference and, and less sluggish bandwidth and so on and so forth. But 5g is really about the enterprise and enterprise services. Transformation, 5g is more secure, kind of a network, but 5g is also a more pervasive network 5g, a fundamentally different network topology than previous generations. So there's going to be more masts and that means that you can have more pervasive connectivity. >>Uh, so things like IOT and edge applications, autonomous cars, smart cities, these kinds of things, um, are all much better served because you've got more masks that of course means that you're going to have a lot more data as well. And we'll get to that. The second piece is immersive digital services. So with more masks, with more connectivity, with lower latency with higher man, the potential, uh, is, is, is, is immense for services innovation. And we don't know what those services are going to be. We know that technologies like augmented reality, virtual reality, things like this have great potential. Um, but we, we have yet to see where those commercial applications are going to be, but the innovation and the innovation potential for 5g is phenomenal. Um, it certainly means that we're going to have a lot more, uh, edge devices, um, uh, and that again is going to lead to an increase in the amount of data that we have available. >>And then the idea of pervasive connectivity when it comes to smart, smart cities, uh, autonomous, autonomous currents, um, uh, integrated traffic management systems, um, all of this kind of stuff, those of those kind of smart environments thrive where you've got this kind of pervasive connectivity, this persistent, uh, connection to the network. Um, again, that's going to drive, um, um, uh, more innovation. And again, because you've got these new connected devices, you're going to get even more data. So this rise, this exponential rise in data is really what's driving the change in, in network analytics. And there are four major vectors that are driving this increase in data in terms of both volume and in terms of speed. So the first is more physical elements. So we said already that 5g networks are going to have a different apology. 5g networks will have more devices, more and more masks. >>Um, and so with more physical elements in the network, you're going to get more physical data coming off those physical networks. And so that needs to be aggregated and collected and managed and stored and analyzed and understood when, so that we can, um, have a better understanding as to why things happened the way they do, why the network behaves in which they do in, in, in, in ways that it does and why devices that are connected to the network. And ultimately of course, consumers, whether they be enterprises or retail customers, um, behave in the way they do in relation to their interaction within our edge nodes and devices, we're going to have a, uh, an explosion in terms of the number of devices. We've already seen IOT devices with your different kinds of trackers and, uh, and, and sensors that are hanging off the edge of the network, whether it's to make buildings smarter car smarter, or people smarter, um, in, in terms of having the, the, the measurements and the connectivity and all that sort of stuff. >>So the numbers of devices on the agent beyond the age, um, are going to be phenomenal. One of the things that we've been trying to with as an industry over the last few years is where does the telco network end, and where does the enterprise, or even the consumer network begin. You used to be very clear that, you know, the telco network ended at the router. Um, but now it's not, it's not that clear anymore because in the enterprise space, particularly with virtualized networking, which we're going to talk about in a second, um, you start to see end to end network services being deployed. Um, uh, and so are they being those services in some instances are being managed by the service provider themselves, and in some cases by the enterprise client, um, again, the line between where the telco network ends and where the enterprise or the consumer network begins, uh, is not clear. >>Uh, so, so those edge, the, the, the proliferation of devices at the age, um, uh, in terms of, um, you know, what those devices are, what the data yield is and what the policies are, their need to govern those devices, um, in terms of security and privacy, things like that, um, that's all going to be really, really important virtualized services. We just touched on that briefly. One of the big, big trends that's happening right now is not just the shift of it operations onto the cloud, but the shift of the network onto the cloud, the virtualization of network infrastructure, and that has two major impacts. First of all, it means that you've got the agility and all of the scale, um, uh, benefits that you get from migrating workloads to the cloud, the elasticity and the growth and all that sort of stuff. But arguably more importantly for the telco, it means that with a virtualized network infrastructure, you can offer entire networks to enterprise clients. >>So if you're selling to a government department, for example, is looking to stand up a system for certification of, of, you know, export certification, something like that. Um, you can not just sell them the connectivity, but you can sell them the networking and the infrastructure in order to serve that entire end to end application. You could sentence, you could offer them in theory, an entire end-to-end communications network, um, and with 5g network slicing, they can even have their own little piece of the 5g bandwidth that's been allocated against the carrier, um, uh, and, and have a complete end to end environment. So the kinds of services that can be offered by telcos, um, given virtualize network infrastructure, uh, are, are many and varied. And it's a, it's a, it's a, um, uh, an outstanding opportunity. But what it also means is that the number of network elements virtualized in this case is also exploding. >>That means the amount of data that we're getting on, uh, informing us as to how those network elements are behaving, how they're performing, um, uh, is, is, is going to go up as well. And then finally, AI complexity. So on the demand side, um, while historically, uh, um, network analytics, big data, uh, has been, has been driven by, um, returns in terms of data monetization, uh, whether that's through cost avoidance, um, or service assurance, uh, or even revenue generation through data monetization and things like that. AI is transforming telecommunications and every other industry, the potential for autonomous operations, uh, is extremely attractive. And so understanding how the end-to-end telecommunication service delivering delivery infrastructure works, uh, is essential, uh, as a training ground for AI models that can help to automate a huge amount of telecommunications operating, um, processes. So the AI demand for data is just going through the roof. >>And so all of these things combined to mean big data is getting explosive. It is absolutely going through the roof. So that's a huge thing that's happening. So as telecommunications companies around the world are looking at their network analytics infrastructure, which was initially designed for service insurance primarily, um, and how they migrate that to the cloud. These things are impacting on those decisions because you're not just looking at migrating a workload to operate in the cloud that used to work in the, in the data center. Now you're looking at, um, uh, migrating a workload, but also expanding the use cases in that work and bear in mind, many of those, those are going to need to remain on prem. So they'll need to be within a private cloud or at best a hybrid cloud environment in order to satisfy a regulatory jurisdictional requirements. So let's talk about an example. >>So LGU plus is a Finastra fantastic service provider in Korea. Um, huge growth in that business over the last, uh, over the last 10, 15 years or so. Um, and obviously most people will be familiar with LG, the electronics brand, maybe less so with, uh, with LG plus, but they've been doing phenomenal work. And we're the first, uh, business in the world who launch commercial 5g in 2019. And so a huge milestone that they achieved. And at the same time they deploy the network real-time analytics platform or in rep, uh, from a combination of Cloudera and our partner calmer. Now, um, there were a number of things that were driving, uh, the requirement for it, for the, for the analytics platform at the time. Um, clearly the 5g launch was that was the big thing that they had in mind, but there were other things that re so within the 5g launch, um, uh, they were looking for, for visibility of services, um, and service assurance and service quality. >>So, you know, what services have been launched? How are they being taken up? What are the issues that are arising, where are the faults happening? Um, where are the problems? Because clearly when you launch a new service, but then you want to understand and be on top of the issues as they arise. Um, so that was really, really important. The second piece was, and, you know, this is not a new story to any telco in the world, right. But there are silos in operation. Uh, and so, um, taking advantage of, um, or eliminating redundancies through the process, um, of, of digital transformation, it was really important. And so particular, the two silos between wired and the wireless sides of the business come together so that there would be an integrated network management system, um, for, uh, for LGU plus, as they rolled out 5g. So eliminating redundancy and driving cost savings through the, the integration of the silos is really, really important. >>And that's a process and the people thing every bit, as much as it is a systems and a data thing. So, um, another big driver and the fourth one, you know, we've talked a little bit about some of these things, right? 5g brings huge opportunity for enterprise services, innovation. So industry 4.0 digital experience, these kinds of use cases, um, are very important in the south Korean marketing and in the, um, in the business of LGU plus. And so, uh, um, looking at AI and how can you apply AI to network management? Uh, again, there's a number of use cases, really, really exciting use cases that have gone live now, um, in LG plus since, uh, since we did this initial deployment and they're making fantastic strides there, um, big data analytics for users across LGU plus, right? So it's not just for, um, uh, it's not just for the immediate application of 5g or the support or the 5g network. >>Um, but also for other data analysts and data scientists across the LGU plus business network analytics, while primarily it's primary it's primary use case is around network management, um, LGU plus, or, or network analytics, um, has applications across the entire business, right? So, um, you know, for customer churn or next best offer for understanding customer experience and customer behavior really important there for digital advertising, for product innovation, all sorts of different use cases and departments within the business needed access to this information. So collaboration sharing across the network, the real-time network analytics platform, um, it was very important. And then finally, as I mentioned, LG group is much bigger than just LG plus it's because the electronics and other pieces, and they had launched a major group wide digital transformation program in 2019, and still being a part of that was, well, some of them, the problems that they were looking to address. >>Um, so first of all, the integration of wired and wireless data service data sources, and so getting your assurance data sources, your network, data sources, uh, and so on integrated with is really, really important scale was massive for them. Um, you know, they're talking about billions of transactions in under a minute, uh, being processed, um, and hundreds of terabytes per day. So, uh, you know, phenomenal scale, uh, that needed to be available out of the box as it were, um, real time indicators and alarms. And there was lots of KPIs and thresholds set that, you know, w to make, make it to meet certain criteria, certain standards, um, customer specific, real time analysis of 5g, particularly for the launch root cause analysis, an AI based prediction on service, uh, anomalies and service service issues was, was, was a core use case. Um, as I talked about already the provision of service of data services across the organization, and then support for 5g, uh, served the business service, uh, impact, uh, was extremely important. >>So it's not just understand well, you know, that you have an outage in a particular network element, but what is the impact on the business of LGU plus, but also what is the impact on the business of the customer, uh, from an outage or an anomaly or a problem on, on, on the network. So being able to answer those kinds of questions really, really important, too. And as I said, between Cloudera and Kamarck, uh, uh, and LGU plus, uh, really themselves an intrinsic part of the solution, um, uh, this is, this is what we, we ended up building. So a big complicated architecture space. I really don't want to go into too much detail here. Um, uh, you can see these things for yourself, but let me skip through it really quickly. So, first of all, the key data sources, um, you have all of your wireless network information, other data sources. >>This is really important because sometimes you kind of skip over this. There are other systems that are in place like the enterprise data warehouse that needed to be integrated as well, southbound and northbound interfaces. So we get our data from the network and so on, um, and network management applications through file interfaces. CAFCA no fire important technologies. And also the RDBMS systems that, uh, you know, like the enterprise data warehouse that we're able to feed that into the system. And then northbound, um, you know, we spoke already about me making network analytics services available across the enterprise. Um, so, uh, you know, uh, having both the file and the API interface available, um, for other systems and other consumers across the enterprise is very important. Um, lots of stuff going on then in the platform itself to petabytes and persistent storage, um, Cloudera HDFS, 300 nodes for the, the raw data storage, um, uh, and then, uh, could do for real time storage for real-time indicator analysis, alarm generation, um, uh, and other real time, um, processes. >>Uh, so there, that was the, the core of the solution, uh, spark processes for ETL key quality indicators and alarming, um, and also a bunch of work done around, um, data preparation, data generation for transferal to, to third party systems, um, through the northbound interfaces, um, uh, Impala, API queries, um, for real-time systems, uh, there on the right hand side, and then, um, a whole bunch of clustering classification, prediction jobs, um, through the, uh, the, the, the, the ML processes, the machine learning processes, uh, again, another key use case, and we've done a bunch of work on that. And, um, I encourage you to have a look at the Cloudera website for more detail on some of the work that we did here. Um, so this is some pretty cool stuff. Um, and then finally, just the upstream services, some of these there's lots more than, than, than simply these ones, but service assurance is really, really important. So SQM cm and SED grade. So the service quality management customer experience, autonomous controllers, uh, really, really important consumers of, of the, of the real-time analytics platform, uh, and your conventional service assurance, um, functions like faulted performance management. Uh, these things are as much consumers of the information and the network analytics platform as they are providers of data to the network, uh, analytics >>Platform. >>Um, so some of the specific use cases, uh, that, uh, have been, have been stood up and that are delivering value to this day and lots of more episodes, but these are just three that we pulled out. Um, so first of all, um, uh, sort of specific monitoring and customer quality analysis, Karen response. So again, growing from the initial 5g launch and then broadening into broader services, um, understanding where there are the, where there are issues so that when people complaining, when people have an issue, um, that, um, uh, that we can answer the, the concerns of the client, um, in a substantive way, um, uh, AI functions around root cause analysis or understanding why things went wrong when they went wrong. Um, uh, and also making recommendations as to how to avoid those occurrences in the future. Uh, so we know what preventative measures can be taken. Um, and then finally the, uh, the collaboration function across LGU plus extremely important and continues to be important to this day where data is shared throughout the enterprise, through the API Lira through file interfaces and other things, and through interface integrations with, uh, with upstream systems. >>So, um, that's kind of the, the, uh, real quick run through of LGU plus the numbers are just stave staggering. Um, you know, we've seen, uh, upwards of a billion transactions in under 40 seconds being, um, uh, being tested. Um, and, and we've gone beyond those thresholds now, already, um, and we're started and, and, and, and this isn't just a theoretical sort of a benchmarking test or something like that. We're seeing these kinds of volumes of data and not too far down the track. So, um, with those things that I mentioned earlier with the proliferation of, of, um, of network infrastructure, uh, in the 5g context with virtualized elements, with all of these other bits and pieces are driving massive volumes of data towards the, uh, the, the, the network analytics platform. So phenomenal scale. Um, this is just one example we work with, with service providers all over the world is over 80% of the top 100 telecommunication service providers run on Cloudera. >>They use Cloudera in the network, and we're seeing those customers, all migrating legacy cloud platforms now onto CDP onto the Cloudera data platform. Um, they're increasing the, the, the jobs that they do. So it's not just warehousing, not just ingestion ETL, and moving into things like machine learning. Um, and also looking at new data sources from places like NWTF the network data analytics function in 5g, or the management and orchestration layer in, in software defined networks, network, function, virtualization. So, you know, new use cases coming in all the time, new data sources coming in all the time growth in, in, in, in the application scope from, as we say, from edge to AI. Um, and so it's, it's really exciting to see how the, the, the, the footprint is growing and how, uh, the applications in telecommunications are really making a difference in, in facilitating, um, network transformation. And that's covering that. That's me covered for today. I hope you found that helpful, um, by all means, please reach out, uh, there's a couple of links here. You can follow me on Twitter. You can connect to the telecommunications page, reach out to me directly at Cloudera. I'd love to answer your questions, um, uh, and, uh, and talk to you about how big data is transforming networks, uh, and how network transformation is, is accelerating telcos, uh, throughout >>Jamie Sharath with Liga data, I'm primarily on the delivery side of the house, but I also support our new business teams. I'd like to spend a minute really just kind of telling you about the legal data, where basically a Silicon valley startup, uh, started in 2014, and, uh, our lead iron, our executive team, basically where the data officers at Yahoo before this, uh, we provide managed data services, and we provide products that are focused on telcos. So we have some experience in non telco industry, but our focus for the last seven years or so is specifically on telco. So again, something over 200 employees, we have a global presence in north America, middle east Africa, Asia, and Europe. And we have folks in all of those places, uh, I'd like to call your attention to the, uh, the middle really of the screen there. So here is where we have done some partnership with Cloudera. >>So if you look at that and you can see we're in Holland and Jamaica, and then a lot to throughout Africa as well. Now, the data fabric is the product that we're talking about. And the data fabric is basically a big data type of data warehouse with a lot of additional functionality involved. The data fabric is comprised of, uh, some something called a flare, which we'll talk about in a minute below there, and then the Cloudera data platform underneath. So this is how we're partnering together. We, uh, we, we have this tool and it's, uh, it's functioning and delivering in something over 10 up. So flare now, flare is a piece of that legal data IP. The rest is there. And what flare does is that basically pulls in data, integrates it to an event streaming platform. It's, uh, it is the engine behind the data fabric. >>Uh, it's also a decisioning platform. So in real time, we're able to pull in data. We're able to run analytics on it, and we're able to alert are, do whatever is needed in a real-time basis. Of course, a lot of clients at this point are still sending data in batch. So it handles that as well, but we call that a CA picture Sanchez. Now Sacho is a very interesting app. It's an AI analytics app for executives. What it is is it runs on your mobile phone. It ties into your data. Now this could be the data fabric, but it couldn't be a standalone product. And basically it allows you to ask, you know, human type questions to say, how are my gross ads last week? How are they comparing against same time last week before that? And even the same time 60 days ago. So as an executive or as an analyst, I can pull it up and I can look at it instantly in a meeting or anywhere else without having to think about queries or anything like that. >>So that's pretty much for us at legal data, not really to set the context of where we are. So this is a traditional telco environments. So you see the systems of record, you see the cloud, you see OSS and BSS data. So one of the things that the next step above which calls we call the system of intelligence of the data fabric does, is it mergers that BSS and OSS data. So the longer we have any silos or anything that's separated, it's all coming into one area to allow business, to go in or allow data scientists go in and do that. So if you look at the bottom line, excuse me, of the, uh, of the system of intelligence, you can see that flare is the tools that pulls in the data. So it provides even streaming capabilities. It preserves entity states, so that you can go back and look at it state at any time. >>It does stream analytics that is as the data is coming in, it can perform analytics on it. And it also allows real-time decisioning. So that's something that, uh, that's something that business users can go in and create a system of, uh, if them's, it looks very much like the graph database, where you can create a product that will allow the user to be notified if a certain condition happens. So for instance, a bundle, so a real-time offer or user is succinct to run out of is ongoing, and an offer can be sent to him right on the fly. And that's set up by the business user as opposed to programmers, uh, data infrastructure. So the fabric has really three areas. That data is persistent, obviously there's the data lake. So the data lake stores that level of granularity that is very deep years and years of history, data, scientists like that, uh, and, uh, you know, for a historical record keeping and requirements from the government, that data would be stored there. >>Then there's also something we call the business semantics layer and the business semantics layer contains something over 650 specific telco KPIs. These are initially from PM forum, but they also are included in, uh, various, uh, uh, mobile operators that we've delivered at. And we've, we've grown that. So that's there for business data lake is there for data scientists, analytical stores, uh, they can be used for many different reasons. There are a lot of times RDBMS is, are still there. So these, this, this basically platform, this cloud they're a platform can tie into analytical data stores as well via flair access and reporting. So graphic visualizations, API APIs are a very key part of it. A third-party query tools, any kind of grid tools can be used. And those are the, of course, the, uh, the ones that are highly optimized and allow, you know, search of billions of records. >>And then if you look at the top, it's the systems of engagement, then you might vote this use cases. So teleco reporting, hundreds of KPIs that are, that are generated for users, segmentation, basically micro to macro segmentation, segmentation will play a key role in a use case. We talked about in a minute monetization. So this helps teleco providers monetize their specific data, but monetize it in. Okay, how to, how do they make money off of it, but also how might you leverage this data to engage with another client? So for instance, in some where it's allowed a DPI is used, and the fabric tracks exactly where each person goes each, uh, we call it a subscriber, goes within his, uh, um, uh, internet browsing on the, on the four or 5g. And, uh, the, all that data is stored. Uh, whereas you can tell a lot of things where the segment, the profile that's being used and, you know, what are they propensity to buy? Do they spend a lot of time on the Coca-Cola page? There are buyers out there that find that information very valuable, and then there's signs of, and we spoke briefly about Sanchez before that sits on top of the fabric or it's it's alone. >>So, so the story really that we want to tell is, is one, this is, this is one case out of it. This is a CVM type of case. So there was a mobile operator out there that was really offering, you know, packages, whether it's a bundle or whether it's a particular tool to subscribers, they, they were offering kind of an abroad approach that it was not very focused. It was not depending on the segments that were created around the profiling earlier, uh, the subscriber usage was somewhat dated and this was causing a lot of those. A lot of those offers to be just basically not taken and, and not, not, uh, audited. Uh, there was limited segmentation capabilities really before the, uh, before the, uh, fabric came in. Now, one of the key things about the fabric is when you start building segments, you can build that history. >>So all of that data stored in the data lake can be used in terms of segmentation. So what did we do about that? The, the, the envy and, oh, the challenge this, uh, we basically put the data fabric in and the data fabric was running Cloudera data platform and that, uh, and that's how we team up. Uh, we facilitated the ability to personalize campaign. So what that means is, uh, the segments that were built and that user fell within that segment, we knew exactly what his behavior most likely was. So those recommendations, those offers could be created then, and we enable this in real time. So real-time ability to even go out to the CRM system and gather further information about that. All of these tools, again, we're running on top of the Cloudera data platform, uh, what was the outcome? Willie, uh, outcome was that there was a much more precise offer given to the client that is, that was accepted, no increase in cross sell and upsell subscriber retention. >>Uh, our clients came back to us and pointed out that, uh, it was 183% year on year revenue increase. Uh, so this is a, this is probably one of the key use cases. Now, one thing to really mention is there are hundreds and hundreds of use cases running on the fabric. And I would even say thousands. A lot of those have been migrated. So when the fabric is deployed, when they bring the Cloudera and the legal data solution in there's generally a legacy system that has many use cases. So many of those were, were migrated virtually all of them in pen, on put on the cloud. Uh, another issue is that new use cases are enabled again. So when you get this level of granularity and when you have campaigns that can now base their offers on years of history, as opposed to 30 days of history, the campaigns campaign management response systems, uh, are, are, uh, are enabled quite a bit to do all, uh, to be precise in their offers. Okay. >>Okay. So this is a technical slide. Uh, one of the things that we normally do when we're, when we're out there talking to folks, is we talk and give an overview and that last little while, and then we give a deep technical dive on all aspects of it. So sometimes that deep dive can go a couple of hours. I'm going to do this slide and a couple of minutes. So if you look at it, you can see over on the left, this is the, uh, the sources of the data. And they go through this tool called flare that runs on the cloud. They're a data platform, uh, that can either be via cues or real-time cues, or it can be via a landing zone, or it can be a data extraction. You can take a look at the data quality that's there. So those are built in one of the things that flare does is it has out of the box ability to ingest data sources and to apply the data quality and validation for telco type sources. >>But one of the reasons this is fast to market is because throughout those 10 or 12, uh, opcos that we've done with Cloudera, where we have already built models, so models for CCN, for air for, for most mediation systems. So there's not going to be a type of, uh, input that we haven't already seen are very rarely. So that actually speeds up deployment very quickly. Then a player does the transformations, the, uh, the metrics, continuous learning, we call it continuous decisioning, uh, API access. Uh, we, uh, you know, for, for faster response, we use distributed cash. I'm not going to go too deeply in there, but the layer in the business semantics layer again, are, are sitting on top of the Cloudera data platform. You see the Kafka CLU, uh, Q1, the right as well. >>And all of that, we're calling the fabric. So the fabric is Cloudera data platform and the cloud and flair and all of this runs together. And, and by the way, there've been many, many, many, many hundreds of hours testing flare with Cloudera and, uh, and the whole process, the results, what are the results? Well, uh, there are, there are four I'm going to talk about, uh, we saw the one for the, it was called my pocket pocket, but it's a CDM type, a use case. Uh, the subscribers of that mobile operator were 14 million plus there was a use case for 24 million plus that a year on year revenue was 130%, uh, 32 million plus for 38%. These are, um, these are different CVM pipe, uh, use cases, as well as network use cases. And then there were 44%, uh, telco with 76 million subscribers. So I think that there are a lot more use cases that we could talk about, but, but in this case, this is the ones we're looking at, uh, again, 183%. This is something that we find consistently. And these figures come from our, uh, our actual end client. How do we unlock the full potential of this? Well, I think to start is to arrange a meeting and, uh, it would be great to, to, uh, for you to reach out to me or to Anthony. Uh, we're working at the junction on this, and we can set up a, uh, we can set up a meeting and we can go through this initial meeting. And, uh, I think that's the very beginning. Uh, again, you can get additional information from Cloudera website and from the league of data website, Anthony, that's the story. Thank you. >>No, that's great. Jeremy, thank you so much. It's a, it's, it's wonderful to go deep. And I know that there are hundreds of use cases being deployed in MTN, um, but great to go deep on one. And like you said, it can, once you get that sort of architecture in place, you can do so many different things. The power of data is tremendous, but it's great to be able to see how you can, how you can track it end to end from collecting the data, processing it, understanding it, and then applying it in a commercial context and bringing actual revenue back into the business. So there is your ROI straight away. Now you've got a platform that you can transform your business on. That's, that's, it's a tremendous story, Jamie, and thank you for your part. Sure. Um, that's a, that's, that's our story for today. Like Jamie says, um, please do flee, uh, feel free to reach out to us. Um, the, the website addresses are there and our contact details, and we'd be delighted to talk to you a little bit more about some of the other use cases, perhaps, um, and maybe about your own business and, uh, and how we might be able to make it, make it perform a little better. So thank you.

(bright upbeat melody) >> Welcome back to Dell Technology World 2021. Dell Tech World, the virtual edition. My name is Dave Vellante. We're going to talk about the Edge. I'm very excited to invite Pierluca Chiodelli, who's the Vice President of Product Management for the Edge portfolio at Dell. And Gil Shneorson, who's the Senior Vice President, Edge portfolio also at Dell Technologies. Gentlemen, great to see you welcome to theCUBE. >> Thank you, Dave. >> Thank you, great to see you. >> Yeah, great to see you guys too. Wish we were face to face but maybe in '22. Gil, let's start with you. The Edge is very exciting, it's not really defined. It's very fragmented, but it's there. It's kind of, you know it, when you see it. What do you get excited about when you think about the Edge? >> I think of there's two elements. The first one, is that we all live at the Edge. In other words, the areas we deal with are around us everyday. When we shop, when we consume, when we drive. So it's a very physical type of activity, we know it's there. What's really exciting mostly to me is that, and you started with talking about fragmentation right off the bat. It is a great opportunity for Dell Technologies to add value. Because it's so fragmented, because it's so new, because it has developed and evolved the way it is. We see an amazing opportunity for us to add much more value than we do today and solve problems that have yet to be solved in the industry. >> And Pierluca, it's an exciting, it's almost like an infinite playground for a technologist. I mean. >> Yeah, Dave, I think that's exactly what we find out. The Edge is very exciting, there is a lot of motion especially due to the pandemic and other things. Big factor that is accelerating the innovation at the Edge but this is an inorganic acceleration and what it cause for most of our customers is also confusion, right? They need to apply multiple solutions but not very organized. So you try to solve the outcome like having the right production on your line because demand is surging. But you don't have an organic things to do that and solve the problem. So you see a lot of silos coming in for each one of the solution, and that's what Gil was referring. That's a great opportunity for us as Dell with the breadth of the portfolio we have and what our team that is a new team is focusing doing is to bring that idea to be able to consolidate multiple things at the Edge and process things at the Edge. >> We did an event. CUBE had an event called the CUBE on Cloud and Q1, we had John Rose on and the title of the segment was something like gaining the technology Edge. And we were kind of geeking out on the tech at the Edge. And my takeaway there was... We were trying to like what is Edge? It's like, well, it's the place where it makes most sense to process the data. And so that brings up a lot of challenges. There are technical challenges and there are business challenges. I wonder if we could sort of dig into those a little bit. How do you guys look at that? Maybe Gil, you want to start maybe. Maybe on the business side and then we can dig into that. >> Sure. The way things evolved. If you think about it, at the Edge is very verticalized. And because of that, they're very use case driven. And so in every industry possible, you start with some business person making a decision whether they have a need or they want to grow their business. And so for example, they will buy an applying to do fraud protection in retail or detection retail. Or they will apply an application to merit robotics and the factory need would come with its own gateway, implant, compute, and a cloud portal. And then you do it again and again, and again, every time you have a business opportunity. All of the sudden you have this proliferation of IT type equipment. At the end where it's the worst place to have it really because you don't have the right IT resources and you are in the need to protect it in a much more... In a different way that you can do in a data center. And so all of that, bring us to a point that we see an opportunity to simplify. And so not only simplification. And this is, you know, simplification or simplicity is the most important driver for any IP purchase. Things that are simple or the easiest and the most economical to operate. The next demand that we see from a customer is security. Because things are at the edge, they have a much more extended attack surface. They need to be connected to networks. They need to be connected without IT staff. So if you can simplify insecure, you can really unlock amazing value by processing data close to where it's created. Without it, we're seeing this opportunity as businesses but we can't really get to it because there are those two hurdles in front of us. >> So Pierluca. We need to you thank you for that, Gill. When you hear a lot about AI inferencing at the Edge. And if you think about AI today, much of the work is modeling. It's done in the cloud. But you're not going to be doing AI inferencing in real time in the cloud. Take the autonomous vehicle example. So that brings some technical challenges. There's obviously data challenges. I'm curious as to how you think about that. I mean, we always talk about how much data is going to be persisted. I think Tesla persists like five minutes of data, right? But some of it is going to go back. That's true. But a lot of it is going to be processed real time. And that's just really different than the way we typically think about IT. >> Yeah, absolutely. So at the Edge, especially in manufacturing, we see right now, or in other use case, it's very important to get the outcome very quickly. Now, you don't use that a deep learning model for that. You need to just understand. For example, in the computer vision use case where you take image of your production line. To your point, Dave, you not keep those image, you keep the image where you have the defect. But you need to process that AIML needs to be intelligent enough to understand that you have a defect, and send that image then to the club. So the search of the data at the Edge is a very important factor. And why you need to process data at the Edge, because as your point, you can't wait to send to the cloud and then wait, right? Tesla is a clear example of that. All the autonomous car where you need to react instantaneously to a change. But in manufacturing, for example, that is our focus for now, is for example, the robots. That if you need to optimize the robot, you need to have a immediate understanding of where the pieces are and when they need to put. And the tolerance need to be act immediately. Otherwise, you come out with the thousand of pieces that they are not in the right tolerance. So, and at the end of the day, what we see is not only the search of the need of processing AIML to the Edge, but also the need to have a new type of compute at the Edge. So in the past, was just gateway and you'd get the gateway and you send the data to the cloud. Now, it's a form of a new compute that has also GPU capability and other things to process this data. So very important. And I think that Dell, especially, we are very focused on that because is really where the customer need to extract the value. >> Thank you. And Gil, I want to get Gil to the unique value proposition to Dell and what makes you distinct. If I infer from your comments, your strategy, you said it's to simplify. And so I see two vectors there. One is to simplify at the Edge. The other is where needed connect that Edge, whether it's on prem, a public cloud, cross-cloud, that kind of simplification layer that abstracts the complex the underlying complexity. Maybe you could talk about your strategy and what makes you guys different? >> Sure. We've been talking to our... Well, we always talk to our customers. And we've been doing business at the Edge for many, many years. Let's call it coincidence that we're a very large company. We have reached, we serve our customers. So when they decide to buy something for their Edge, you know, environments, they come to us as well as other vendors. When the percentage of the time based on our market share. But when we decided to take another look at how can we be even more relevant, we started talking to a lot of them great depth. And what we discovered was the problem I talked about before. The problem of complexity, the problem of security and the problem of choice. And so our focus is to do what we do best. At the end of the day, we're an IT company and our customers for the most part are IT people. And we see them dragged more and more into Edge projects because customers need to connect Edge to the network. And they need to security, and that's how it starts. And so those worlds of IT and OT are coming together and they're coming together, applying IT best practices, which is exactly what we know how to do. And so, because of that, we think that they need to think about architecture versus unique silence solutions. Architecture that can support multiple use cases that can grow with time, consolidate more and more use cases as they grow, simplify what they do by applying tried and true or tried and true IT best practices in a secure manner. So the dealer approach would be doing that, taking a more architectural approach to the adverse as a use case. And then just like you predicted, meet the customers where they are from an application standpoint. And so we know that a lot of applications are growing and be developing on a hyper scale or public clouds. We would like to connect to those. We would like to allow them to keep working as they have, except, when they run it at the Edge. Think about environments, if can consolidate multiple workloads and not solve it for each one at the same time. And so that will be our overall approach. That's what we're working on. >> Yeah. So, okay. So in that horizontal layer, if you will, to serve many, many use cases, not just... You're not going to go a mile deep into one and be the expert at some narrow use case. You want to be that horizontal platform. Here, look, I wonder, does that call for more programmability over time of the products to really allow people to kind of design in that flexibility, if you will, build my own. Is that something that we can expect? >> Yeah, absolutely. So we spoke a little bit about this before the interview. And the things that is very important is composability, starting from a very small form factor to the cluster, and then expand to the cloud is the fundamental things. And the trend that we see. The fact that you can compose the infrastructure, starting from a small gateway that is changing in this market right up to the cloud, and be able to use the same layer that allow you to run the same application is a fundamental things. And we are working on that. We are working on this vision and our strategy is really to be able to be transparent but provide the right building block to do all the use cases that they are required. Where the data. So we, again, not only meeting the customer but meeting where the data are, what the customer wants out of those data. So that's a fundamental things. And we have project Apex. So obviously we are plugging in the project Apex. From a Edge point of view, will allow the customer to have these unique experience to go in Apex and also deploy the Edge infrastructure that is needed. We're starting right now with that. So we will touch later, but that's the first building block of that journey. >> Excellent. Let's touch now. You've got some news around Apex and what are you announcing? >> So we are very excited because as I said our team it's pretty new and it's a very important investment that Dell makes. Not only in us as a team, but as a motion. So we are announcing a reference architecture with PTC. PTC is one of the biggest company for... Actually based here in Boston for manufacturing. And reference architecture will be run on base on Apex private cloud. So the customer can go to the portal, order Apex private cloud and deploy PTC on top of that. So very important things is the first step in this journey. But it's very important steps so we want to thank you also PTC to allow us to work with them. We have other stuff as well that we are announcing. I don't know if you are familiar but we have a very unique streaming data platform. Streaming data platform that can stream multiple data collected from gateway, from every place. And that it's a need. Obviously, when you need to process data in real time, whatever is streaming. What are we doing with the new streaming data platform approach is the ability to deploy single node. So it can be very appealing for the Edge and up to three nodes. >>Awesome. That's great. So a couple of comments on that. So it was funny. We did the LiveWorx show in theCUBE a couple of years ago. PTC is a big event and it was the Edge. And I remember looking around and saying "Where's all the IT vendors?". And so that's great to see you guys leaning in like that. Pierluca, the streaming platform. Tell me more about that. What's the tech behind it? >> So the streaming data platform is a project, that we start couple of year ago, is actually start from open source Pravega. It's a very interesting technology where you can stream multiple data. It's not a traditional storage. Use a technology that can really collect thousands of different streams. And that's very important when you need to mind the data. Bring the data, the structured data in efficient that you can process them at the real time. It's very important. So there are very cool use case of that. But now, that we look at the Edge, this is make more and more tangible sense because we have a lot of partners that they're working with us, especially to extend. When you have all these sensor, you bring the data to the gateways and from the gateways, then you can use data streaming platform to collect all these streams. And then you can easily process them. So it's a very fundamental technology. We are very proud of that. As I said, our enterprise version, it's getting more and more. And now we can land these on different architecture. So it can be backed up by an unison. It can be also on different storage type now. And as I said, we looking now to bring from a what was it data center kind of structure, down to the Edge because now we can put it in a single node up to three nodes. >> It makes a lot of sense. Is this like a Kafka based thing or open source or is it something you guys built or a combination? >> It's a combination. The project is an open source project but we did that. We start this many years ago. And it works with Kafka but it's not Kafka. So it has plugging that can work with Kafka and all the other things. And it's very easy to deploy. So it's a very, very important. And the other things is the scalability of this platform. >> Yeah, so I mean, that sounds like the kind of thing you had in the labs. And you said, "Okay, this is going to be important". And then boom, all of a sudden, the market comes to you. As if you pop it right in. And then of course, the Accenture relationship. Deep, deep industry expertise. So that makes a of sense. 5G's happening. A different world the next 10 years than the last 10 years. Isn't it? >> Yep. >> What is it about manufacturing? Why did you start there? >> I can take this. We looked at where the opportunity was from two perspective. One is whether what are the opportunities to sell, Dave. And the other one obviously comes with it because there's an opportunity to have. And manufacturing today at the Edge is about 30% of the opportunity in sales. According to IDC. But more so, it's been around for the longer time. And so it's maturing, it's the most demanding. And you know, it's got very long horizons of investment. And what we did was, we figured that if we can solve problems for industry, we can then extend that and solve it for everybody else. Because this would be the toughest one to solve. And we like challenge. And then, so we decided to focus and go deep. And you said it before, well, our approach is definitely horizontal approach. We cannot take an horizontal approach without verticalizing and understanding specific needs. So nobody can avoid doing both at the same time. You need to understand. But you also want to solve it in a way that doesn't proliferate the silos. So that's our role. We will understand, but we will make it more generic. So other people can never (indistinct). >> Yeah. And David, if I can add, I think the manufacturing is also very exciting for us as a technologist, right? And Dell technology, as in the name, the technology. So it's very exciting because if I look at manufacturing, we are really in the middle of a industrial transformation. I mean, it's a new era. If you think about, nobody care in the past to connect their machinery with... That they have PLC to the network. All of these is changing because the life where we live right now, with the pandemic, with the remote working, with the fact that you need to have a much more control and be able to have predictive matter. So you're not stopping your manufacturing. Is pushing the entire manufacturing institute industry to connect these machines. And with the connectivity of these machinery, you get a lot of data. You get also a lot of challenge. For example, security. So now, that's the place where connectivity brings the IT aspect in. And the OT guys, now they starting to speak (indistinct) because now it's a more complex things, right? It's not any more computerized only to one machinery. Specifically, is the entire floor. So it's a very interesting dynamics. >> Is the connection between that programmable logic controller and the Dell solution, you mentioned to secure, better security. And I presume it's also to connect back to whatever the core or the cloud, et cetera. Is it also to do something locally? Does it improve? Is there value add that you can provide locally? And what is that value add? >> Yeah, absolutely. So the value add, as I said, if you think right in the past, right? You have a machine that probably stay in the manufacturing for 20, 25 years then you have an hardware attached to that machine that they used the POC about 11 year. The guy that he knows better about that machine, is actually not the software component of it. But he's the guy that he's been working on that machine for 15 years. Now, how you translate that knowledge to a learning algorithm that actually can do that for thousand of machine. And that's really the key, right? You need to centralize information, process those information, but not in the cloud, not in a central data center, but on the manufacturing floor. And you need to have a way to represent these things in a very simple way. So the plant manager can take action, or the guy that is responsible for the entire line, can take action immediately. And that's where the change is. It's not anymore to... Is trying to extend that knowledge to multiple machine, multiple floor, and try to get these change immediately. So that's very important. >> So the PLC doesn't become a general purpose computer, or even necessarily an Uber computer. It connects to that capability because that enables data sharing across clouds. >> That's enable the entire things. You can't do a model just with one source. You need to have multiple sources. And also think about the manufacturing is changing not only for the machinery, but people that they build new manufacturing, right? They need to be smart building. They need to have a technology for being more green, solar energy consumption. So the manufacturing itself is mean five or six different things that you need to solve. It's not just the machine. So this idea of this silos environment is starting to collapse in one. And that's why it's important for us to start from a vertical, but also in the manufacturer, you already see this will expand to multiple things. Also, smart building another thing because they need it. >> Yeah. The red guilt to your point of view. Manufacturing is like the Big Apple. If you can make it there, you can make it anywhere. And you've got adjacencies that you can take the learnings, and manufacturing, and apply them to those adjacent industries. Gil, give us the last word. >> No, usually when we talk at Dell technologies world, we talk to an ideal audience. And we're thinking this year that the way to talk about Edge, at least with the people who traditionally buy from us is expose them to the fact that they are more and more going to be responsible for every projects. And so our advice would be, our hope that they would partner with us to think ahead. Just like they do with data center with our cloud strategy. Thinking ahead as they think about their Edge and try to set up some architectural guidelines. So when they do get the request, they're ready for it. And think about what they know, think about the IP best practices that they applied. All of that is coming to them. They need to be prepared as well. And so we would like to partner with all of our customers to make them ready. And obviously help them simplify, secure, consolidated as they grow. >> Well, guys, thank you. I learned a lot today. We've made a lot of progress. You know, this is the hallmark of Dell, right? It's a very high, let me make sure I get this right. Very high do to say ratio, right? As you guys talked about doing this, a couple of couple of years ago. And you've made a lot of progress and I really appreciate you coming on theCUBE to explain this strategy. It makes a lot of sense. And so congratulations and good luck in the future. >> Thank you. >> Thank you, Dave. >> All right. And thank you for watching everybody. This is Dave Vellante for theCUBE's ongoing coverage of Dell Tech World 2021, the virtual edition. Keep it right there, I'll be right back. (closing music)

>>Yeah, yeah, >>Hello. We're back with Today's the last session in the creating engaging analytics experiences for all track breaking down data silos. A conversation with Snowflake on Western Union Earlier today, we did a few deep dives into the thought spot product with sessions on thoughts about one. Thoughts were everywhere on spot. Take you to close out this track. We're joined by industry leading experts Christian Kleinerman s VP of product at Snowflake and Tom Matzzie, Pharaoh, chief data officer at Western Union, for a thought provoking conversation on data transformation on how to avoid the pitfalls of traditional analytics. They'll be discussing in key challenges faced by organizations, why user engagement matters and looking towards the future of the industry. No Joining Thomas and Christian in conversation is Angela Cooper, vice president of customer success at Thought spot. Thank you all for being here today. We're so excited for what is what this conversation has in store. Handing it over now to Christian to kick things off. >>Hi. So, a few years ago, when when someone asked about Snowflake, the most common answer, it was like, what is snowflake and what do you do? Hopefully in the last couple off months, things have changed and and here I am showing a couple of momentum data points on, uh, where we have accomplished here it Snowflake. So we we have received Ah, a lot of attention and buzz. Recently, we were listed in the New York Stock Exchange And we even though we still think of ourselves as a small start up company, we have crossed the 2000 employees mark. More important, we count with 3 3000 plus amazing customers. And something that we obsess about is the a satisfaction of our customers. We really are working hard. The laboring technology that having a platform for better decisions, better analytics and then the promoters course off 71 depicted here is a testament of that. And last, but certainly not least about snowflake. It's very important that we know that we succeed with our partners. We know that we don't go to market by ourselves. We actually have Ah, fantastic set of partners and of course, thoughts. But it is one of our most important partners. >>Good morning. Good afternoon. Eso Amman Thomas affair on the chief kid officer here at Western Union. It's gonna be a background of a Western union and what we, uh, what we do and how we service our customers. So today we are in over 200 countries and territories worldwide. We have a 550,000 retail Asian network to service all of our customers, uh, needs from what he transfer and picking up in a depositing cash. We also have our digital transformation underway, where we now have educate abilities up and running and over 35 countries with paled options to accounts in over 120 countries. We think about our overall business and how support are over our customers and our services. It really has transformed over the past 12 months with Cove it and it's part of that We have to be able to really accelerate our transformation on a digital front to help to enable in the super those customers going forward. Eso as part of that, You know, a big, big help in a big supporter of that transformation has been snowflake and has been thought spot as part of that transformation. If you go the next to the next slide are our current, uh B I in our illegal tools right to date, uh, have been very useful up until the last one or two years. As data explodes and as as our customer needs transform and as our solutions and our time to act in our time to react in the overall market becomes faster and faster, we need to be able to basically look across our entire company, our entire organization and cross functionally to visit to leverage data leverage our insights to really basically pivot our overall business and our overall model to support our customers and our and to enable those services and products going forward. So as part of that, snowflakes been a huge part of that journey, right, allowing us to consolidate over our 30 plus data stores across the company on able to really leverage that overall data and insights to drive, uh, quick reaction right with the pivot, our business offered to enable new services and improve customer experiences going forward and then being able to use a snowflake and then being put the applications on top of that like thought spot, which allows, uh, users that are both technical and nontechnical to the go in and just, um, ask the question as if the searching on Google or Yahoo or being they can just ask any question they want and then get the results back in real time, made that business call and then really go forward through these is this larger ecosystem as a whole. It's really enabled us to really transform our business and supporter customers going forward. >>Wonderful. Thank you, Tom. Thank you, Christian, for the overview of both snowflake and Western Union. Both have big presence in Denver, which is where Tom and I are tonight. Um, I'm here. I'm the vice president of customer success for Thought spot, and I wanted to ask both of you some questions about the industry and specific things that you're facing within Western Union. So first I was hoping Christian that you could talk to me a little bit about Snowflake has thousands of customers at this point, servicing essentially located data sets. But what are you seeing? Has been the top challenges that businesses air facing and how it snowflake uniquely positioned to help. Yeah, >>so certainly the think the challenges air made. I would say that the macro challenge above everything is how to turn data into a competitive differentiator, their study after study that says companies that embrace data and insights and analytics they are outperforming their competitors. So that would be my macro challenge. Once you go into the next level, maybe I can think of three elements. The first one Tom already perfectly teed up the topic of of silence and the reality For most organizations, data is fragmented across different database systems. Even filed systems in some instances transactional databases, analytical data bases and what customers expect is to have, ah, unified experience like I am dealing with company extra company. Why? And I really don't care if behind the scenes there's 10 different teams or 100 different systems. I just want a unified experience. And the Congress is true. The opportunity to deliver personalized custom experiences is reliant on a single view of the day. The other topic that comes to mind this is the one of data governance, Um, as data becomes more important than a reorganization, understanding the constraints and security and privacy also become critical to not only advanced data capability but do it doing so responsibly and within the norms off regulation and the last one which is something court to tow our vision. We are pioneering the concept of the data cloud and the challenge that that we're addressing there is the problem around access to data, right. You can no longer as an organization think of making decisions just on your own data. But there's lots of data collaboration, data enrichment. Maybe I wanna put my data in context. And that's what we're trying to simplify and democratize access and simplify connecting to the data that improves decisions on all three fronts. Obviously, we're obsessed. That's no bling on on tearing down the silos on delivering a solution that is very focused on data governance. And for sure, the data cloud simplifies access to data. >>Wonderful. Now, I know we we really focused on those data silos is a business challenge. But Tom, going through your digital transformation journey are there specific challenges that you faced with Western Union That thought spot and snowflake have helped you overcome? >>Yeah. So? So first off fully agree what Christian just said, right? Those are absolutely, you know, problems that we faced. And we've had overcome, um, service, any company right being able to the transforming to modernize the cloud. Um, for us, one of the biggest things is being able to not just access our information, but have it in a way that it can be consumed, right? Have it in a way that it could be understood, right? Have it in a way that we can then drive business business decision points and and be able to use that information to either fix a problem that we see or better service our customers or offer a product that we're seeing right now is a miss in the marketplace to service in a underserved community or underserved, um, customer base. Also, from our standpoint, being able toe look, um um, uh and predict in forecast what's going to happen and be able to use that information and use our insights to then be proactive and thio in either, You know, be thoughtful about how do we shift our focus, or how do we then change our strategy to take advantage of that for that forecast in that position that we're seeing into the future? >>Wonderful. I've heard from many customers you could not have predicted what was going to happen to our businesses in the year 2020 with the traditional models and especially with what did you say? 30 plus different data silos. Being able to do that type of prediction across those systems must have been very, very difficult. You also mentioned going through a digital transformation at Western Union. So can you talk to me, Tom? A little bit about kind of present day? And why? Why is it important to enable your frontline knowledge workers with the right data at the right time with the right technology? >>Yeah, so? So you're spot on, by the way. But, uh, no one predicted that that we would have a pandemic that would literally consume the entire globe right And change how consumers, um uh, use and buy services and products, or how economies would either shut down or at the reopening shut down again. And then how different interests to be impacted by this? Right. So, uh, what we learned and what we were able to pivot was being able to do exactly what you just said, right. Being able to understand what's happening the date of the right time, right then being able to with the right technology with the right capabilities, understand? what's happening. I understand. Then what should our pivot be? And how should we then go focus on that pivot to go into go and transform? I think it's e. It's more than just just the front lines. Also, our executives. It's also are back office operations, right, because as you think through this, right as customers were having issues right, go into retail locations that were closed. It end of Q one Earlier, Q two. We obviously had a a large surplus right of phone calls coming into our call centers, asking for help, asking for How can we transact better? Where can we go? Right? How do we handle the operationally? Right? As we had a massive surge onto our digital platform where we were, we had 100% increase year over year in Q one and Q two. How do we make sure that our platform the technology can scale right and still provide the right S L A's and and and and the right, um uh, support to our internal customers as well as our extra customers in the future? Eso so really interesting, though, you know, on on on the front line side, our sales staff, right? And even our front line associates with our agent locations A to retail side, you know, for us, is really around. How do we best support them? So how do we partner with them to understand? You know, when a certain certain governments or certain, uh, regions were going toe lock down, how do we support them to keep them open, right. How do we make them a essential service going forward? How do we enable them? Right, the Wright systems or technology to do things a bit differently than they have in the past to adopt right with the changing times. But, you know, I'll tell you the amount of transformation in the basement we've done this year, I think you know, has a massive and actually on Lee, you know, created a larger wave for us to actually ride into the future as we can, to base to innovate, you know, in partnership with both thought spot and with the snowflake into the future. >>Absolutely. I've seen many, many a industry analyst reports talking about how companies now in 2020 have accelerated that digital transformation movement because of current day. In current time, Christian What are you seeing with the rest of the industry and other global companies about enabling data across the globe at the right time? >>Yeah, so I can't agree more with with with with what? Tom said. And he gave some very, um, compelling and very riel use cases where the timeliness of data and and and and and at the right time concept make a big difference. Right? They aske part of our data marketplace with snowflake with deliver, for example, um, up to date low ladies information on, uh, covert 19 data sets where we're infection spiking. And what were the trends? And the use case was very, very riel. Every single company was trying to make sense of the numbers. Uh, all machine learning models were sort of like, out of whack, because no trends and no patterns may make sense anymore. And it was They need to be able to join my data and my activity with this health data set and make decisions at the right time. Imagine if if the cycle to makes all these decisions waas Ah, monthlong. You would never catch up, right? And he speaks to tow a concept that it that is, um, dear, it wasa snowflake and is the lifetime value data right? The notion of ableto act on a piece of data on an event at the right time and obviously with the slow laden see it's possible, makes a big difference. And and there is no end of example. Stomach gives her all again very compelling ones. Um, there's many others, but if you're running a marketing campaign and would you want to know five minutes later that it's not working out, you're burning your daughters? Or would you want to know the next day? Or if someone is going to give you you have a subscription based business and you're going toe, for example, have a model that predicts the turn of your customer? How useful is if you find out Hey, your customer is gonna turn, but you found out two months later. Once probably you are really toe action and change the outcome. Eyes different and and and this order to manage that I'm talking about days or months are not uncommon. Many organizations today, and that's where the topic of right technology matters. Um, I love asking questions about Do you know, an organization and customers. Do you run data, transformations and ingests at two and three in the morning? And the most common answer is yes. And then you start asking why. And usually the answer is some flavor off technology made me do it and a big part of what we're trying to do, like what we're pioneering is. How about ingesting data, transforming data enriching data when the business needs it at the right time with the right timeliness? Not when the technology had cycles. So they were Scipio available, so the importance can't be overstated. There is value in in in analyzing understanding data on time, and we provide technology and platform to any of this. >>That's such a good point. Christian. We ended up on Lee doing processes and loading in the middle of the night because that's what the technology at that time would allow. You couldn't have the concurrency. You couldn't have, um, data happening all at the same time. And so wonderful point that stuff like enables. I think another piece that's interesting that you guys a hit on is that it's important to have the same user experiencing user interface at the right time. And so what I found talking to customers. And Tom what? You and I have discussed this. When you have 30 different data sets and you have a interface that's different, you have a legacy reports system. Maybe you have excel on top of another. You have thought spot on one. You have your dashboard of choice on another, those different sources in different ways. To view that data, it can all be so disjointed. And the combination of thought spot with snowflake and all the data in one place with a centralized, unified user experience just helps users take advantage off the insights that they need right at that right moment. So kind of finishing up for our last question for today I'm interested to hear about Christian will go back to you quickly about what do you see from snowflakes? Perspective is ahead. Future facing for data and analytics. >>One of the topics you just alluded toe Angela, which is the fact that many data sets are gonna be part of the processes by which we make decisions and that that's where were the experience with thoughts but a single unified search experience for a single unified. Um automatic insects, which is what's para que does That is the future, right? I I don't think that x many years from now on, and I think that that X is a small number. Organizations are going to say I had some business activity. I collected some data. I did some analysis and I have conclusions because it always has to be okay, put it in context or look at industry trends and look at other activity that can help him make more sense about my data. The example of tracking they covert are breaking is ah, timely one. But you can always say go on, put it in context with, I don't know, maybe the GDP of the country or the adoption of a platform and things like that. So I think that's ah big trend on having multiple data sets. Contributing towards better decisions towards better product experience is for better services. And, of course, Snowflake is trying to do its part, is doing its part with vision and simplify answers today and the answer on hot spot simplifying blending the interface so that would be super useful. The other big piece, of course, is, um, Predictive Analytics people Talk machine Learning and AI, which is a little bit to buzz worthy. But it is true that we have the technology to drive predictions and and do a better job of understanding behaviors off what's supposed to happen based on understanding the best and the last one. If if if I'm allowed one. Exco What's ahead for data industry, which sounds obvious, but But we're not all the way. There is both cloud the adoption and moving to the cloud as well as the topic of multi Cloud. Increasingly, I think we we finally shifted conversations from Should I go to the cloud or not? Now it's How fast do I do it? And increasingly what we hear is I may want to take the best of the different clouds and how doe I go in and and and embrace a multi cloud reality without having to learn 100 plus different services and nuances of services on on every car and this work technologies like snowflake and thoughts about that can can support a different multiple deployment are being well received by different customs, nerve fault, >>Tom industry trends, or one thing I know. Western Union is really leading in the digital transformation and in your space, What's next for Western Union? >>Yeah, so just add on Requip Thio Christian before I dive into a Western Union use case just to your point. Christian, I really see a convergence happening between how people today work or or manage their personal life, where the applications, the user experiences and the responses are at your fingertips. Easy to use don't need to learn different tools. It's just all there, right, whether you're an android user or an apple user rights, although your fingertips I ask you the same innovation and transmission happening now on the work side, where I see to your point right a convergence happening where not just that the technology teams but even the business teams. They wanna have that same feature, that same functionality, where all their insights their entire way to interact with the business with the business teams with their data with their systems with their products for their services are at their fingertips right where they can go and they can make a change on an iPad or an iPhone and instant effect. They can go change a rule. They could go and modify Uh uh, an algorithm. They can go and look at expanding their product base, and it's just there. It's instant now. This would take time, right? Because this is going to be a transformational journey right across many different industries, but it's part of that. I really see that type of instant gratification, uh, satisfaction, that type of being able to instantly get those insights. Be able thio to really, you know, do what you do on your personal life in your work life every single day. That trend is absolutely it's actually happening. And it's kind of like tag team that into what we're doing at Western Union is exactly that we are actually transforming how our business teams, uh, in our technology teams are able to interact with our customers, interact with our products, interact with our services, interact with our data and our systems instantly. Right? Perfect example that it's that spot where they could go on typing any question they want. And they instigate an answer like that that that was unheard of a year ago, at least for our business. Right being able to to to go and put in in a new rule and and have it flow through the rules engine and have an instant customer impact that's coming right. Being able to instantly change or configure a new product or service with new fee structure and launch in 15 minutes. That's coming, right? All these new transformations about how do we actually better, uh, leverage our capabilities, our products and our services to meet those customer demands instantly. That's where I see the industry going the next couple of years. >>Wonderful. Um, excited to have both of you on the panel this afternoon. So thank you so much for joining us, Christian and Tom as just a quick wrap up. I, you know, learned quite a bit about industry trends and the problems facing companies today. And from the macro view with snowflake and thousands of customers and thought spots, customers and Western Union. The underlying theme is data unity, right? No more fragmented silos, no more fragmented user experiences, but truly bringing everything together in a governed safe way for users. Toe have trust in the data to have trust in what to answer and what insight is being put in front of them. And all of this pulled together so that businesses can make those better decisions more informed and more personalized. Consumer like experiences for your customers in modern technology stacks. So again, thank you both today for joining us, and we look forward to many more conversations in the future. Thank you >>for having me very happy to be here. >>Thank you so much. >>Thanks. >>Thank you, Angela. And thank you, Tom and Christian for sharing your stories. It was really interesting to hear how the events of this year have prompted Western Union to accelerate their digital transformation with snowflake and thought spot and just reflecting on alot sessions in this track, I love seeing how we're making the search experience even easier and even more consumer like in that first session and then moving on to the second session with our customer Hayes. It was really impressive to see how quickly they'd embedded thought spot into their own MD audit product. And then, of course, we heard about Spot Ike, which is making it easier for everybody to get to the Y faster with automated insights. So I'm afraid that wraps up the sessions in this track. We've come to an end, But remember to join us for the exciting product roadmap session coming right up. And then after that, put your questions to the speakers that you've heard in Track two in I'll meet the Experts Roundtable, creating engaging analytics experiences for all. Now all that remains is for me to say thank you for joining us. We really appreciate you taking the time. I hope it's been interesting and valuable. And if it has, we'd love to pick up with you for a 1 to 1 conversation Bye for now.

>> Announcer: From around the globe, it's theCUBE! With digital coverage of AnsibleFest 2020. Brought to you by Red Hat. >> Hello, everyone. Welcome back to theCUBE's coverage of AnsibleFest 2020. I'm your host with theCUBE John Furrier. And we've got two great guests. A CUBE alumni, Robyn Bergeron, senior manager, Ansible community team. Welcome back, she's with Ansible and Red Hat. Good to see you. And Matt Jones, chief architect for the Ansible Automation Platform. Again, both with Red Hat, Ansible was acquired by Red Hat. Robyn used to work for Red Hat, then went to Ansible. Ansible got bought by Red Hat. Robyn, great to see you, Matt, great to see you. >> Yep, thanks for having me back again. It's good to see you. >> We're not in person. It's the virtual event. Thanks for coming on remotely to our CUBE virtual, really appreciate it. I want to talk about the, and I brought that Red Hat kind of journey Robyn. We talked about it last year, but it really is an important point. The roots of Ansible and kind of where it's come from and what it's turned into and where it is today, is an interesting journey because the mission is still the same. I would like to get your perspectives because you know, Red Hat was acquired by IBM, Ansible's under Red Hat, all part of one big happy family. A lot's going on around the platform, Matt, you're the chief architect, Robyn you're on the community team. Collections, collections, collections, is the message, content, content, content, community, a lot going on. So take a minute, both of you explain the Ansible roots, where it is today, and the mission. >> Right, so beginning of Ansible was really, there was a small team of folks and they'd actually been through an iteration before that didn't use SSH called Funk, but you know, it was, let's make a piece of software that is open source that allows people to automate other things. And we knew at the time that, you know, based on a piece of research that we had seen out of Harvard that having a piece of software be architected in a modular fashion wasn't just great for the software, but it was also great for developing pathways and connections for the community to actually contribute stuff. If you have a car, this is always my analogy. If you have a car, you don't have to know how the engine works in order to swap out the windshield wipers or embed new windshield wipers, things like that. The nice thing about modular architectures is that it doesn't just mean that things can plug in. It means you can actually separate them into different spots to enable them to be plugged in. And that's sort of where we are today with collections, right? We've always had this sense of modules, but everything except for a couple of points in time, all of the modules, the ways that you connect Ansible to the vast array of technologies that you can use it with. All of those have always been in the full Ansible repository. Now we've separated out most of, you know, nearly everything that is not absolutely essential to having in a, you know, a very minimal Ansible installation, broken them out into separate repositories, that are usually grouped by function, right? So there's probably like a VMware something and a cloud something, and a IBM, z/OS something, things like that, right? Each in their own individual groups. So now, not only can contributors find what they want to contribute to in much smaller spots that are not a sea of 5,000 plus folks doing work. But now you can also choose to use your Ansible collections, update them, run them independently of just the singular release of Ansible, where you got everything, all the batteries included in one spot. >> Matt, this brings up the point about she's bringing in more advanced functionality, she's talking about collections. This has been kind of the Ansible formula from the beginning in its startup days, ease of use, easy, fast automation. Talk about the, you know, back in 2013 it was a startup. Now it's part of Red Hat. The game is still the same. Can you just share kind of what's the current guiding principles around Ansible this year? Because lots going on, like I said, faster, bigger, a lot going on, share your perspective. You've been there. >> Yeah, you know, what we're working on now is we're taking this great tool that has changed the way that automation works for a lot of people and we want to make it faster and bigger and better. We want it to scale better. We want it to automate more and be easier to automate, automate all the things that people want to do. And so we're really focusing on that scalability and flexibility. Robyn talked about content and collections, right? And what we want to enable is people to bring the content collections, the collections, the roles, the models, and use them in the way that they feel works best for them, leaving aside some of the things that they maybe aren't quite as interested in and put it together in a way that scales for them and scales for a global automation, automation everywhere. >> Yeah, I want to dig into the collections later, Robyn, for sure. And Matt, so let's, we'll put that on pause for a minute. I want to get into the event, the virtual event. Obviously we're not face to face, this year's virtual. You guys are both keynoting. Matt, we'll start with you. If you can each give 60 seconds, kind of a rundown of your keynote talk, give us the quick summary this year on the keynotes, Matt, we'll start with you. >> Yeah. That's, 60 seconds is- >> If you need a minute and a half, we'll give you 90 seconds, Robyn, that's going to be tough. Matt, we'll start with you. >> I'll try. So this year, and I mentioned the focus on scalability and flexibility, we on the product and on the platform, on the Ansible Automation Platform, the goal here is to bring content and flexibility of that content into the platform for you. We focused a lot on how you execute, how you run automation, how you manage your automation, and so bringing that content management automation into the system for you. It's really important to us. But what we're also noticing is that we, people are managing automation at a much larger scale. So we are updating the Ansible Tower, Ansible AWX, the automation platform, we're updating it to be more flexible in how it runs content, and where it can run content. We're making it so that execution of automation doesn't just have to happen in your data center, in one data center, we recognize that automation occurs globally, and we want to expand that automation execution capability to be able to run globally and all report back into your central business. We're also expanding over the next six months, a year, how well Ansible integrates with OpenShift and Kubernetes. This is a huge focus for us. We want that experience for automation to feel the same, whether you're automating at the edge, in devices and virtual machines and data centers, as well as clusters and Kubernetes clusters anywhere in the world. >> That's awesome. That's why I brought that up earlier. I wanted to get that out there because it's worth calling out that the Ansible mission from the beginning was similar scope, easy to do and simplify, but now it's larger scale. Again, it's everywhere, harder to do, hence complexity being extracted away. So thank you for sharing. We'll dig into that in a second. Okay, Robyn, 60 seconds or more, if you need it, your keynote this year at AnsibleFest, give us the quick rundown. >> All right. Well, I think we probably know at this point, one of the main themes this year is called automate to connect and, you know, the purpose of the community keynote is really to highlight the achievements of the community. So, you know, we are talking about, well, we are talking about collections, you know, going through some of the very broad highlights of that, and also how that has contributed, or, not contributed, how that is included as part of the recent release of Ansible 2.10, which was really the first release where we've got it very easy for people to actually start using collections and getting familiar with what that brings to them. A good portion of the keynote is also just about innovation, right? Like how we do things in open source and why we do things in certain ways in open source to accelerate us. And how that compares with the Red Hat, traditional product model, which is, we kind of, we do a lot of innovation upstream. We move quickly so that if something is maybe not the right idea, we can move on. And then in our products, that's sort of the thing that we give to our customers that is tried, tested and true. All of that kind of jazz. We also talk about, or I guess I also talk about the, all of our initiatives that we're doing around diversity and inclusiveness, including some of the code changes that we've made for better, more inclusive language in our projects and our downstream products, our diversity and inclusion working group that we have in the community land, which is, you know, just looking to embrace more and more people. It's a lot about connectivity, right? To one of Matt's points about all the things that we're trying to achieve and how it's similar to the original principles, the third one was, it's always, we need to have it to be easy to contribute to. It doesn't necessarily just mean in our community, right? Like we see in all of these workplaces, which is one of the reasons why we brought in Automation Hub, that folks inside large organizations, companies, government, whatever it is, are using Ansible and there's more and more, and, you know, there's one person, they tell their friend, they tell another friend, and next thing you know, it's the whole department. And then you find people in other departments and then you've got a ton of people doing stuff. And we all know that you can do a bunch of stuff by yourself, but you can accomplish a lot more together. And so, making it easy to contribute inside your organization is not much different than being able to contribute inside the community. So this is just a further recognition, I think, of what we see as just a natural extension of open source. >> I think the community angle is super important 'cause you have the community in terms of people contributing, but you also have multiple vendors now, multiple clouds, multiple integrations, the stakeholders of collaboration have increased. It was just like, "Oh, here's the upstream and et cetera, we're done, and have meetings, do all that stuff." And Matt, that brings me to my next question. Can you talk about some of the recent releases that have changed the content experience for the Ansible users in the upstream and within the automation platform? >> Well, so last year we released collections, and we've really been moving towards that over the 2.9, 2.10 timeframe. And now I think you're starting to see sort of the realization of that, right? This year we've released Automation Hub on cloud.redhat.com so that we can concentrate that vendor and partner content that Red Hat supports and certifies. In AnsibleFest you'll hear us talk about Private Automation Hub. This is bringing that content experience to the customer, to the user of this content, sort of helping you curate and manage that content yourself, like Robyn said, like we want to build communities around the content that you've developed. That's the whole reason that we've done this with collections is we don't want to bind it to Ansible core releases. We don't want to block content releases, all of this great functionality that the community is building. This is what collections mean. You should be free to use the collections that you want when you want it, regardless of when Ansible core itself has released. >> Can you just take a minute real quick and just explain what is collections, for folks out there who are rich? 'Cause that's the big theme here, collections, collections, collections. That's what I'm hearing resonate throughout the virtual hallways, if you will. Twitter and beyond. >> That's a good question. Like what is a collection itself? So we've talked a lot in the past about reusable content for Ansible. We talk a lot about roles and modules and we sort of put those off to the side a little bit and say, "These are your reusable components." You can put 'em anywhere you want. You can put 'em in source control, distribute them through email, it doesn't matter. And then your playbooks, that's what you write. And that's your sort of blessed content. Collections are really about taking the modules and roles and plugins, the things that make automation possible, and bundling those up together in groups of content, groups of modules and roles, or standing by themselves so that you can decide how that's distributed and how you consume that, right? Like you might have the Azure, VMware or Red Hat satellite collection that you're using. And you're happy with that. But you want a new version of Ansible. You're not bound to using one and the same. You can stick with the content that matters to you, the roles, the modules, the plugins that work for you. And you decide when to update those and you know, what the actual modules and plugins you're using are. >> So I got to ask the content question, you know, I'm a content producer. We do videos as content, blog posts content. When you talk about content, it's code, clarify that role for us because you got, you're enabling developers with content and helping them find experts. This is a concept. Robyn, talk about this. And Matt, you can weigh in, too, define what does content mean? It means different things. (indistinct) again, content could be. >> It is one of those words, it's right up there with developers, you know, so many different things that that can mean, especially- >> Explain content and the importance of the semantics of that. Explain it, it's important that people understand the semantics of the word "content" with respect to what's going on with Ansible. >> Yeah, and Matt and I actually had a conversation about the murkiness of this word, I believe that was yesterday. So what I think about our content, you know, and I try to put myself in the mind, my first job was a CIS admin. So I try to put myself in the mind of someone who might be using this content that I'm about to attempt to explain. Like Matt just explained, we've always had these modules, which were included in Ansible. People have pieces of code that show very basic things, right? If I get one of the AWS modules, it would, I am able to do things like "I would like to create a new user." So you might make a role that actually describes the steps in Ansible, that you would have to create a new user that is able to access AWS services at your company. There may be a number of administrators who want to use that piece of stuff, that piece of code over and over and over again, because hopefully most companies are getting bigger and not smaller, right? They want to have more people accessing all sorts of pieces of technology. So making some of these chunks accessible to lots of folks is really important, right? Because what good is automation, if, sure we've taken care of half of it, but if you still have to come up with your own bits of code from scratch every time you want to invoke it, you're still not really leveraging the full power of collaboration. So when we talk about content, to me, it really is things that are constantly reusable, that are accessible, that you tie together with modules that you're getting from collections. And I think it's that bundle, you can keep those pits of reusable content in the collections or keep them separate. But, you know, it's stuff that is baked for you, or that maybe somebody inside your organization bakes, but they only have to bake it once. They don't have to bake it in 25 silos over and over and over again. >> Matt, the reason why we're talking about this is interesting, 'cause you know what this points out, in my opinion, it's my opinion. This points out that we're talking about content as a word means that you guys were on the cutting edge of new paradigms, which is content, it's essentially code, but it's addressable, community it's being shared. Someone wrote the code and it's a whole 'nother level of thinking. This is kind of a platform automation. I get it. So give us your thoughts because this is a critical component because the origination of the content, the code, I mean, I love it. Content is, I've always said content, our content should be code. It's all data, but this is interesting. This is the cutting edge concept. Could you explain what it means from your perspective? >> This is about building communities around that content, right? Like it's that sharing that didn't exist before, like Robyn mentioned, like, you know, you shouldn't have to build the same thing a dozen times or 100 times, you should be able to leverage the capabilities of experts and people who understand that section of automation the best, like I might be an expert in one field or Robyn's an expert in another field, we're automating in the same space. We should be able to bring our own expertise and resources together. And so this is what that content is. Like, I'm an expert in one, you're an expert in another, let's bring them together as part of our automation community and share them so that we can use them iterate on them and build on them and just constantly make them better. >> And the concepts are consumption, there's consumption of the content. There's the collaboration of the content. There's the sharing, all this, and there's reputation, there's expertise. I mean, it's a multi sided marketplace here, isn't it? >> Yeah. I read a article, I don't know, a year or two ago that said, we've always evolved in the technology industry around, if you have access to this, first it was the mainframes. Then it was, whatever, personal computers, the cloud, now it's containers, all of this, but, once everybody buys that mainframe or once everybody levels up their skills to whatever the next thing is that you can just buy, there's not much left that actually can help you to differentiate from your competitors, other than your ability to actually leverage all of those tools. And if you can actually have better collaboration, I think than other folks, then that is one of those points that actually will get you ahead in your digital transformation curve. >> I've been harping on this for a while. I think that cloud native finally has gone, when I say "mainstream" I mean like on everyone's mind, you look at the container uptake, you're looking at containers. We had IDC on, five to 10% of the enterprises are containerizing. That's huge growth opportunity. The IPO of, say, Snowflake's on Amazon. I mean, how does this happen? That's a company that's went public, It's the most valuable IPO in the history of IPOs on Wall Street. And it's built on Amazon, it has its own cloud. So it's like, I mean, this points to the new value that's being created on top of these new cloud native architectures. So I really think you guys are onto something big here. And I think you're starting to see this, new notions of how things are being rethought and reimagined. So let's keep it, while I've got you guys here real quick, Ansible 2.1 community release. Tell us more about the updates there. >> Oh, 2.10, because, yeah. Oh, that's fine. I know I too have had, I'm like, "Why do we do that?" But it's semantic versioning. So I am more accustomed to this now, it's a slightly different world from when I worked on Fedora. You know, I think the big highlight there is really collections. I mean, it's collections, collections, collections. That is all the work that we did, it's under the hood, over the hood, and really, how we went from being all in one repo to breaking things out. It's a big line for, we're advancing both the tool and also advancing the community's ability to actually collaborate together. And, you know, as folks start to actually use it, it's a big change for them potentially in how they can actually work together in their organizations using Ansible. One of the big things we did focus on was ensuring that their ease of use, that their experience did not change. So if they have existing Ansible stuff that they're running, playbooks, mod roles, et cetera, they should be able to use 2.10 and not see any discernible change. That's all the under the hood. That was a lot of surgery, wasn't it, Matt? Serious amounts of work. >> So Matt, 2.10, does that impact the release piece of it for the developers and the customers out there? What does it change? >> It's a good point. Like at least for the longer term, this means that we can focus on the Ansible core experience. And this is the part that we didn't touch on much before now with the collections pieces that now when we're fixing bugs, when we're iterating and making Ansible as an engine of automation better, we can do that without negatively impacting the automation that people actually use. We could focus on the core experience of actually automating itself. >> Execution environments, let's talk about that. What are they, are they being used in the community today? What do you guys react to that? >> We're actually, we're sort of in the middle of building this right now. Like one of the things that we've struggled with is when you, you need to automate, you need this content that we've talked about before. But beyond that, you have the system that sits underneath the version of Linux, the kernel that you're using, going even further, you need Python dependencies, you need library dependencies. These are hard and complicated things, like in the Ansible Tower space, we have virtual environments, which lets you install those things right alongside the Ansible Tower control plane. This can cause a lot of problems. So execution environments, they take those dependencies, the unit that is the environment that you need to run your automation in, and we're going to containerize it. You were just talking about this from the containerization perspective, right? We're going to build more easily isolated, easy to use distinct units of environments that will let you run your automation. This is great. This lets you, the person who's building the content for your organization, he can develop it and test it and send it through the CI process all the way up through production, it's the exact same environment. You could feel confident that the automation that you're running against the libraries and the models, the version of Ansible that you're using, is the same when you're developing the content as when you're running it in production for your business, for your users, for your customers. >> And that's the Nirvana. This is really where you talk about pushing it to new limits. Real quick, just to kind of end it out here for Ansible 2020, AnsibleFest 2020. Obviously we're now virtual, people aren't there in person, which is really an intimate event. Last year was awesome. Had theCUBE set right there, great event, people were intimate. What's going on for what you guys have for people that obviously we got the videos and got the media content. What's the main theme, Robyn and Matt, and what's going on for resources that might be available for folks who want to learn more, what's going on in the community, can you just take a minute each to talk about some of the exciting things that are going on at the event that they should pay attention to, and obviously, it's asynchronous so they can go anywhere anytime they want, it's the internet. Where can they go to hang out? Is there a hang space? Just give the quick two second commercial, Robyn, we'll start with you. >> All right. Well of course you can catch the keynotes early in the morning. I look forward to everybody's super exciting, highly polite comments. 'Cause I hear there's a couple people coming to this event, at least a few. I know within the event platform itself, there are chat rooms for each track. I myself will be probably hanging out in some of the diversity and inclusion spaces, honestly, and I, this is part of my keynote. You know, one of the great things about AnsibleFest is for me, and I was at the original AnsibleFest that had like 20 people in Boston in 2013. And it happened directly across the street from Red Hat Summit, which is why I was able to just ditch my job and go across the street to my future job, so to speak. We were... Well, I just lost my whole train of thought and ruined everything. Jeez. >> We got that you're going to be in the chat rooms for the diversity and community piece, off platform, is there a Slack? Is there like a site? Anything else? 'Cause you know, when the event's over, they're going to come back and consume on demand, but also the community, is there a Discord? I mean, all kinds of stuff's going on, popping up with these virtual spaces. >> One thing I should highlight is we do have the Ansible Contributor Summit that goes on the day before AnsibleFest and the day after AnsibleFest. Now, normally this is a pretty intimate event with the large outreach that we've gotten with this Fest, which is much bigger than the original one, much, much, much bigger, we've, and signing up for the contributor summit is part of the registration process for AnsibleFest. So we've actually geared our first day of that event to be towards new or aspiring contributors rather than the traditional format that we've had, which is where we have a lot of engineers, and can you remember sit down physically or in a virtual room and really talk about all of the things going on under the hood, which is, you know, can be intimidating for new people. Like "I just wanted to learn about how to contribute, not how to do surgery." So the first day is really geared towards making everything accessible to new people because turns out there's a lot of new people who are very excited about Ansible and we want to make sure that we're giving them the content that they need. >> Think about architects. I mean, SREs are jumping in, Matt, you talked about large scale. You're the chief architect, new blood's coming in. But give us an update on your perspective, what people should pay attention to at the event, after the event, communities they could be involved in, certainly people want to tap into you are an expert and find out what's going on. What's your comment? >> Yeah, you know, we have a whole new session track this year on architects, specifically for SREs and automation architects. We really want to highlight that. We want to give that sort of empowerment to the personas of people who, you know, maybe you're not a developer, maybe you're not, operations or a VP of your company. You're looking at the architecture of automation, how you can make our automation better for you and your organization. Everybody's suffered a lot and struggled with the COVID-19. We're no different, right? We want to show how automation can empower you, empower your organization and your company, just like we've struggled also. And we're excited about the things that we want to deliver in the next six months to a year. We want you to hear about those. We want you to hear about content and collections. We want you to hear about scalability, execution environments, we're really excited about what we're doing. You know, use the tools that we've provided in the AnsibleFest event experience to communicate with us, to talk to us. You can always find us on IRC via email, GitHub. We want people to continue to engage with us, our community, our open source community, to engage with us in the same ways that they have. And now we just want to share the things that we're working on, so that we can all collaborate on it and automate better. >> I'm really glad you said that. I mean, again, people are impacted by COVID-19. I got, it sounds like all channels are open. I got to say of all the communities that are having to work from home and are impacted by digital, developers probably are less impacted. They got more time to gain, they don't have to travel, they could hang out, they're used to some of these tools. So I think I guess the strategy is turn on all the channels and engage in new ways. And that seems to be the message, right? >> Yeah, exactly. >> Alright, Robyn Bergeron, great to see you again, Matt Jones, great to chat with you, chief architect for Ansible Automation Platform and of course, Robyn senior manager for the community team. Thanks so much for joining me today. I appreciate it. >> Thank you so much. >> Okay. It's theCUBE's coverage. I'm John Furrier, your host. We're here in the studio in Palo Alto. We're virtual. This is theCUBE virtual with AnsibleFest virtual. We're not face to face. Thank you for watching. (calm music)

>>from around the globe. It's the Cube covering HP Discover virtual experience brought to you by HP. I'm stew minimum. And this is the Cube's coverage of HP Discover virtual experience rather than all getting together in one place. Life box, Vegas. We're getting people around the globe where they are digging into some of the partner discussions here. Happy to welcome to the program. Ricardo de Blasio. He is the chief revenue officer from Con Vault. Ricardo, Thanks so much for joining us. Great to see you. >>We lost you. Great to be here. >>Excellent. So, you know, obviously HP discover Conn Volt and B when you give us the latest on on the partner. >>Absolutely. Well, first of all, I would like to thank you H p e deal team, not only for this invitation, but for the great partnership that we have. Ah, since actually, many, many years. Well, things are going really, really well with HB. Ah, we're very happy with very proud. I mean, if I'm a chief revenue officer also, my idol said it all. You know, if I look at the performance is off our alliances in the last 18 months, um, as being a double digit row and in some quarter even a triple digit growth. So ah, our relationship engagement into the field are growing up on a weekly basis. And the amount of opportunity that we have in our forecasting in our pipeline with HB are growing more and more and more. And, um, I believe we found still a good thing. And young between ah Cos us being the leader off data protection in the market and in conjunction with one of the largest server infrastructure, um, service vendor, service provider like HP. You know, if you think about one of the the the the highest success that we had experience right now is is humble. True green A as a backup as a service, right? So so many angle our chip. It's working and we just feel we are crashing, really, that the people, the iceberg and the best is yet to come so super excited to be here, super excited for what's what's ahead of us. >>Alright, Ricardo, we'll last year and we've had the Cube at Kahn Volt go on for a couple of years. Ah, lot of discussion about the various consumption models, especially out you know, Cloud is fitting into things, whether it be a public cloud and backing up data or are, you know, SAS models. You know, obviously, Alex was the, you know, star of the show at combo go. Last year you mentioned the Green Lake offerings that you're doing with HP to give back up as a service. So bring us inside. You know what you're hearing from your customers? How they're managing these various cloud offering. >>Totally stupid. Well, um I mean, as you know, I mean the the adoption into cloud native APS or moving waters into, ah, cloud models. It is something that has been around for the last 10 years. Obviously, what the current situation is producing is a triggering event to really moving to a light speed, um, transformation and adoption off any type of cloud motors. And we believe that's up backup and data protection provider. Um, we are in the middle of it, experiencing a lot of benefits. I mean, at the end of the day, you know, if you look at our metallic offering, one of our blockbuster is backing up office 3 65 which is a cloud native app after that we got Salesforce or we've got service now and so on. Right before moving to more traditional and point out of management like that or ah, um, like mobile phone. But even if I look at, you know, from from an angle of our partnership with HP, But I see most off the grow and opportunity is being on the Green Lake platform. Um, a lot of the opportunity that we have in our pipe that have been built in the last six months, but I see a lot of potential to do business together. Um, 80% of them is with Green Lake. This always great legs in the middle. >>Excellent. Yeah. What? What do you hearing so much from customers, You know, with your you're the chief revenue officer. So is it Move from cap ex to op X. You know, bring us inside a little bit. The finance side. What you're hearing from customers is how they get ready. Obviously, with the global pandemic even more of a highlight on the cloud models, if I've done things right, I should be able to either, you know, scale up if needed, or if I need to dial things down for a little while. Hopefully, I haven't locked myself in tow some environment. So I love to hear a little bit more color on that piece of it. >>Absolutely. I believe you nail. It's do I mean ah, there's definitely an operational driver behind which he's Can I scale or down my data center without having the possibility to have people on the ground? And so how can I move into a virtual data center? Uh, what? I have computing storage networking that can follow my beach off. Uh, I o according to my business need and this d'etre angle in the current crisis, um, company often are not run, but CFO becomes more important. And, um, there's a huge ah, attention to us and, uh, and everything that can be moved from a perpetual into a credible and therefore cloud has a better fit for that. And, um, and then last but not least, is also, you know, the better integration that a lot of cloud models provide. You were the cloud native. That's right. I mean, um, and you run salesforce on Prem? Not really. Right. So how can you have Ah, a dashboard of different business application and operational applications that are better integrated with the cloud native. That's so the more you can offer your client I eat relates or metallic proponent Delta Cloud Native Services that he's a, um, naturally integrated with the parent cloud native app. So the more you're going to make their life easier. >>Excellent. Ricardo. You know, Con Volt works with many partners. What makes the HP partnership special? >>So I think you know what I said earlier. Definitely. I would say the first thing. These, um a market segmentation, overs and the price. We are experiencing a lot of success with our enterprise clients. You know, if I look at the joint pipeline that we built together, I would say 90% of the lines are global 2000 customers logo. And so that is everyone. Number number two. You know how much work with it collectively in integrating our product line and platform together. So if you look into the humble complete solution and very soon also mentality, but even that big that has been done a lot of effort on that side, they are natively integrated with open a P I so that our clients really will not feel the difference off having two different salad silos solution and and then last but not least, the same strategic goal and view off pushing our cloud based Motorola radical modeler Green Lake for H p e and metallic and a big for mobile. >>Excellent. So you mentioned you know, some of the shifting models to some some of the newer solutions. You know, obviously, you know, integrations partnership a little bit of time, but give us a little bit. What should we be expecting from, you know, calm bolt in the partnership with HP through the rest of 2020. >>Absolutely. Still. Well, um, definitely an acceleration. You know, we put the decision a combo too. Ah, focus on fewer partners are very relevant to us. We're very happy to say that Hve is one of them. And, um, we want to do more from a product integration perspective. So the next one in line with the metallic and how the metallic play and integration will play into green legs and into a lot of HB product. Um, but also, we want to do more with our field engagement. Right? So now we have weekly or monthly orderly. Ah, weekly engagement with our with our two fields organization. Ah, just in order to better serve our clients and often do business with the same channel partners that we have in our ecosystem. >>Excellent. Well, Ricardo, thank you so much for joining us. We really pleasure. >>Thank you. Thank you, Stew. And thank you, HP, for the great partnership opportunity. >>All right, Lots more coverage from the cube. HP discover virtual experience. I'm Stew Minimum. And thank you for watching the Cube. Yeah, yeah, yeah, yeah, yeah.

>> Commentator: From around the globe, it's theCUBE, covering HPE Discover Virtual Experience, brought to you by HPE. >> Hi, I'm Stu Miniman, and this is theCUBE's coverage of HPE Discover Virtual Experience. Rather than all getting together in one place like Las Vegas, we're getting to talk to people around the globe where they are, digging into some of the partner discussions here. Happy to welcome to the program Riccardo De Blasio. He is the Chief Revenue Officer from Commvault. Riccardo, thanks so much for joining us, great to see you. >> Hello Stu, great to be here. >> Excellent. So, you know, obviously HPE Discover, Commvault, and HPE, why don't you give us the latest on, on the partner up? >> Absolutely. Well, first of all, I would like to thank you, HPE, the whole team, not only for this invitation, but for the great partnership that we have, since actually many, many years. Well, things are going really, really well with HPE. We're very happy, we're very proud. I mean, I'm a chief revenue officer, so my title says it all. And if I look at the performances of our alliances in the last, 18 months. It's been a double digit grow and in some quarter, even a triple digit grow. Our relationship and engagement into the field are growing on a weekly basis and the amount of opportunity that we have in focusing in our pipeline with HPE are growing more and more and more. I believe we found, Steve, a good ying and yang, between the two companies, us being the leader of data protection in the market, and in conjunction with one of the largest server infrastructure service provider like HPE, you know, think about it one of the highest success that we are experience right now is Commvault through GreenLake as a backup, as a service, right? So many angles, our partnership, it's working and we just feel we are scratching really the tip of the iceberg. And the best is yet to come. So super excited to be here. Super excited for what's ahead of us. >> All right, Riccardo, well, last year, and we've had theCUBE at Commvault GO for a couple of years, a lot of discussion about the various consumption models, especially how cloud is fitting into things, whether it be a public cloud and backing up data, or SaaS models. Obviously Metallic was the, you know, star of the show at Commvault GO last year, You mentioned, the GreenLake offerings that you're doing with HPE to give back up as a service. So, bring us inside what you're hearing from your customers, how they are managing these various cloud offerings. >> Totally Stu. The adoption into cloud native apps, or moving workloads into cloud models, it is something that has been around for the last 10 years. Obviously, what the current situation is producing is a triggering event to really move in to a light speed transformation and adoption of any type of cloud models. And we believe as a backup and data protection provider, we are in the middle of it, experiencing a lot of benefits. I mean, at the end of the day, if you look at our Metallic offering a one of our blockbuster is backing up office 365, which is a cloud native app. After that, we got Salesforce, or we got ServiceNow, and so on, right? Before we move into more traditional endpoint data management like laptop, or MDM like mobile phone, but even if I look, you know, from an angle of our partnership with HPE, what I see most of the grow and opportunity has been on the GreenLakes platform. A lot of the opportunities that we have in our pipe that have been built in the last six months, where I see a lot of potential to do business together, 80% of them is with GreenLakes, there's always GreenLakes in the middle. >> Excellent. What are you hearing so much from customers? You know, you're the chief revenue officer, so is it the move from CapEx to OpEx? You know, bring us inside a little bit, the finance side and what you're hearing from customers as to how ready. Obviously, with the global pandemic, even puts more of a highlight on those cloud models. If I've done things right I should be able to either, scale up, if needed, or if I need to dial things down for a little while, hopefully I haven't locked myself into some environment. So I would love to hear a little bit more color from you on that piece of it. >> Absolutely, I believe you nailed it, Stu. There's definitely an operational driver behind, which is I can I scale up or down my data center without having the possibility to have people on the ground. And so how can I move into a virtual data center where I have computing, storage, networking, that can follow my peak of IO, according to my business need. And there's the other angle in the current crisis. Company often are not run, but CFO becomes more important, and there's a huge attention to cost, and everything that can be moved from perpetual into a ratable cost. And therefore, cloud has a better fit for that. And, and then last but not least, is also, you know, the better integration that a lot of cloud models provide you with the cloud native apps, right? I mean, can you run Salesforce on-prem? Not really, right. So how can you have a dashboard of different business application and operational application that are better integrated with the cloud native apps. So the more you can offer your client, I.e. GreenLakes, or Metallic, or Commvault, are cloud native services that is naturally integrated with the current cloud native apps, the more you're going to make their life easier. >> One of the big items when you talk about cloud are, you know, compliance and security. Bring us inside what that means when you're dealing with HPE and Commvault together? Sure, I mean, our two organization are traditionally extremely well positioned in the enterprise markets. And when I say enterprise market, I'm talking also about the public sector, vertical, so often or financial service, often vertical industry which are heavily regulated, I think, this is one of the better benefits our joint solution offer to our clients. We know how to deal with this type of environment since the last three decades. And very often we've been at the center of that complexity. So we got the playbook that has been written a number of years ago and re-polished every year. So we feel very strong that every time we win together, one of the competitive advantage we have against our competition is precisely that ability to navigate through a heavily regulated, heavily compliance, enterprise environment that often some of our competitor, they don't have the structure, the culture, the skill set and the tools in order to make our customers life easier on that perspective. >> Excellent. Riccardo, Commvault works with many partners, what makes the HPE partnership special? >> So I think you know what I said earlier, definitely, I would say the first thing is a market segmentation over enterprise. We are experiencing a lot of success with our enterprise client. I look at the joint pipeline that we built together, I would say 90% of the clients are Global 2000 customers. And so that is definitely one number. Number two is, you know how much work we did collectively in integrating our product line and platform together. So if you look, you know, the Commvault Complete solution, and very soon also Metallic, but even Advik, that has been done a lot of effort on that side, there are natively integrated with open API so that our client really will not feel the difference of having two different silos solution. And and then last but not least, the same strategic goal and view of pushing cloud based model, a ratable model, GreenLake for HPE, and Metallic and Advik for Commvault. >> Excellent. You mentioned some of the shifting models to some of the newer solutions. Integration partnership take a little bit of time, but, what should we be expecting from, Commvault in the partnership with HPE for the rest of 2020? >> Absolutely, Steve. Definitely an acceleration. We took the decision at Commvault to focus on fewer partners but very relevant to us. We're very happy to say that HPE is one of them. And we want to do more from a product integration perspective. So the next one in line would be Metallic, and how the Metallic play and integration will play into GreenLakes and into a lot of HPE products. But also we want to do more with our field engagement. So now we have weekly or monthly, quarterly, weekly engagement with our, with our two fields organization just in order to better serve our clients and often do business with the same channel partners that we have in our ecosystem. >> Excellent. Riccardo, thank you so much for joining us. We're really pleasured to talk. >> Thank you, thank you Stu, And thank you HPE for the great partnership and opportunity. >> All right, lots more coverage from theCUBE HPE Discover Virtual Experience. I'm Stu Miniman, and thank you for watching theCUBE (upbeat mellow music)

>> From around the globe its theCUBE with digital coverage of Red Hat Summit 2020, brought to you by Red Hat. >> Hi I'm Stu Miniman and this is theCUBE's coverage of a Red Hat Summit 2020. Of course this year the event is virtual. We're bringing all the people on theCUBE from where they are and really happy to bring back to the program, one of our CUBE alumni, Paul Cormier, who is the president and CEO of Red Hat. Of course the keynote and you and I spoke ahead of the show. Paul great to see you and thanks so much for joining us. >> My pleasure, always great to see you Stu. My pleasure. >> All right, so Paul lots have changed since last time we got together for summit. One things stayed the same though. So, you know, the big theme, I heard in your keynote, you talked about open hybrid cloud of course. We've been talking about cloud for years when you ran the product theme, you know, making Red Hat go everywhere is something that we've watched, you know, that move. Is anything different when you're talking to customers, when you're talking to your, the product themes, you think about the times were in, why is open hybrid cloud not a buzzword but hugely important in the times were facing? >> Because the big premise to open hybrid cloud is that customers, cloud has become part of people's infrastructure. I've seen very few if any true enterprise customers that are moving everything, every app to one cloud. And so I think what people really realized once they started implementing clouds, part of their infrastructure was that you going to always have applications that are running bare metal. Some are virtual machine maybe on top of VMware it might been a private cloud, and not many people saying you know what the public clouds are all so different from each other I might want to run one application for whatever reason in one in a different one or another I think they started to realize the actual operational cost to that, the security cost of that and even more mobility the development cost of that from the application perspective and now having five silos up there now how that's so costly so now our whole premise since the beginning of open hybrid cloud has been to give you that level playing field to have those things all the same no matter where the application wants whether experimental virtual machine private multiple public cloud and so in the long run as customers start to start to really go to cloud first application development and they can still manage that under one platform in a common way but at the same time managed develop secure it but at the same time they can manage develop and secure their legacy applications that are also on linux as well in the same way so I think in the long run it really brings it together and saves money and efficiency in those areas. >> Yeah it's I always loved I look over time we have certain words that we think we know what they mean and then they mature over time let's just say we'll start with the first piece of what you're talking about open we live through those of us that have been through that the really ascendancy of open-source is in the early days open was free and we joke it was free like puppies >> Yeah. but today open source of course is very prevalent we see it all over the place but give from an open hybrid cloud why open is important today and what customers should think, how do customers think about that today? >> There's probably two most misunderstood things with open so first thing is that open source is a development model, first of all. I always say it's a verb not a noun, I even say well think internally and externally. We're not an open source company, we're an enterprise software company with an open source development model. So you think about that, that's what that's really important. Why is the open source development model so important? It's important because everyone has the same opportunity in terms of the features of within the code everyone has the same opportunity to contribute. The best technology wins that's how it works in the upstream community is it's not a technology driven by one company that may have a one company agenda. It's really a development process that allows the best technology to win and I think that's one of the main things and one of the main reasons why you see all the innovation frankly in the last five years around infrastructure and development, associated pieces and tools around that of being in and around Linux because Linux was available, it was powerful, it was open when people wanted to develop for when people wanted to develop kubernetes for example, they had to make changes to the Linux kernel in order to do that it did work because they could and so those are the things that make it really important as a development model and I think those are the things that get confused a lot. I think the other things that get confuses a lot of people think that, "hey if I have this great technology and I just open-source is that it'll all just work, everyone will come, now that's not the case. The things that really, the projects that really succeed of an open-source perspective are the problems that are common and horizontal across a big group of people so they're trying to solve similar problems and that's one of the things that we found as you go further up the stack the length typically the less community is involved it's the horizontal layers where you need whether you're in banking or retail or telco or whatever they're all the same, those are the pieces where open-source really fits well. >> Alright so the second piece you talk about hybrid I think back to the early days Paul when cloud was first defined and we talked about public and private cloud we had discussions of hybrid cloud and multi clouds and the concern that I have is it was very much an infrastructure discussion and it was pieces and the vision that we always have is, were customers to actually get value is, the total solution needs to be more valuable than the sum of its parts. So it's really about hybrid applications about where my data lives, so do you agree with some of those things I'm saying how does Red Hat look at it and from your team i do get lots of the application and app dev discussion which I always find even more meaningful than arguing over ontologies of how you build your cloud. >> Everything you said is all about the application if you look at just where we started with linux just along what did Linux bring to the enterprise when we first started rally me you and I talked about this earlier that was the thing that really opened things up. The enterprise's started buying Linux they right they started buying Linux for Linux for $29.95 at the book stores but when I first came on board we talked to some of the banking customers in there, they said well we love this technology but every time you guys change a release on my applications breaker when I get new hardware it doesn't work etc. So it's all about the application Linux is better about that all the time from the beginning of time what hybrid it really means here, is that I can run that seamlessly across wherever that footprint is going to live and so I think that's also one of the things that gets confused a bit. When the cloud first started, the cloud vendors were telling people that every application was going to move to one cloud tomorrow right? We knew that was not practical, that's the other thing from open-source developers, we look at a practical perspective, we look back in 2007 I just looked at just to prepare for the note I just put up to the company. Back in 2007 at the summit I talked about any application anywhere anytime. That's really the essence of what hybrid is here, so what we found here is what every application is impractical for every application to move to one cloud and so cloud is powerful but it's become part of people's development and operations and security environment so now as we stitch that in may make that common for those three things for the operation security in development more application development world that's where the power is. So I see the day where application developers and application users won't know or care what platform the back-end day is coming from for whatever applications they're writing, they shouldn't care that should just happen seamlessly under the covers but having said that, that complicates thing and that's why management needs to be retooled with it as well. Sorry on that but I could talk about that for three days right? >> Yeah so as an industry we kind of argue about these and everybody feels that they understand the way the future should look. So Paul for a number of years it was, "we're going to build this stack "and let's have the exact same stack here and there." There were some of the big iron companies that did that a few years ago now you see some of your public cloud partners saying, "we can give you that same experience "that same hardware all the way "down to the chip level things are going to be the same." When I look at software companies, there's two that come to mind to live across dispersed environments. One is very much from a virtualization standpoint they design themselves to live on any hardware out there. Red Hat has a slightly different way of looking at things, so what's your take on kind of the stack and why is hybrid in that hybrid cloud model that you're building probably looks and sounds and feels different then I think almost anybody else out there? >> Well the cloud guys, they all have similar technologies underneath I mean most of it not all of its based on Linux but they're all different I mean remember the UNIX days I'm old enough to remember the UNIX day. That was the goal back then but like each hardware vendor did each cloud vendor is now taking that Linux or the Associated pieces with it and they have to make their changes to adapt to their environment and some of those changes don't allow for applications to be portable outside that environment, that's exactly like the OEM world of the past and so I hope some people hate it when I say this to make this a comparison but I really look at the cloud guys as a mainframe and certainly mainframe as and still does bring a ton of value to certain customer base and so if you're going to keep your application in that one place, a mainframe will all on you mainframe mentality will always stitch it to bet together better but that's not the reality of what customers are trying to do out there. So I really think you have to look at it that way it's not that much different in concept anyways to the OEM days whether from when they started running Linux and the thing that Red Hat's done that some of the others haven't for VMware for example, VMware they have no pieces that touch the application I mean they have some now they had photon, they had some of the other pieces that sort of tried to touch the application but at the end of the day we always concentrated in Linux and especially from a Red Hat perspective of keeping the environment the same, both from an application perspective and from a hardware perspective. Certainly when an application runs in the cloud, we don't have to worry about the hardware anymore but we still have to worry about the application and businesses are all about the application and so we always took that tack from both sides of that. I think that's one of VMware's weaknesses frankly is that applications don't run on hypervisors, they run on operating systems including when I say operating systems I mean containers because that is a Linux operating system. >> Yeah Paul a lot of good points you brought up there and it's interesting the mainframe analogy in the early days of cloud there were some that would throw stones and saying right you're rebuilding the mainframe and you're going to be locked in, this is going to be an environment so I'd love to get your thought you think about what's happening in application development, the rise of is you talked about containers and kubernetes serverless is out there there's that, "we want to enable the application developers but we don't want to get locked into some platform there. Talk about red-hat's role how your products are helping the ship, help customers make sure that they can take advantage of some of these new ways of building, maintaining and changing without being stuck on any specific platform or technology >> Well the first place, I believe I'm sure I will be corrected on this but we really are the only company that I can think of at this moment that is a hundred percent open source. Everything we do when our products go is open source based goes back upstream to the community for everyone to take advantage of so that's the first thing. I mean the second thing we do is one of the big fallacies is, open source has become so popular that people are confusing upstream projects with downstream products and so for us I'll use us as an example, I'll use Linux and I'll use kubernetes as an example, the Linux kernel we all built from the Linux kernel us, Susa, Ubuntu we all build from the Linux kernel but at the end of the day we all make choices when we bring that upstream work down to become a product. In our case we go upstream to rel, we go from fedora to sent us to rel. We all make choices, which file systems were going to package, what development environment we're going to to package, what packages werre gonna package and so when we get down to what's get deployed in the enterprise, those choices in what makes the difference of why by rel is slightly different than SUSE Linux which is slightly different than Canonical's upon - but they're all come from the same heritage, the same as the case with kubernetes is this sort of fallacy that kubernetes is the last time I checked it was 127 different kubernetes vendors out there. They're all just going to magically work together yes they all come from the same place but we have to touch the users face, we have to touch the kernel and so there how do you line that up in the life cycle of what the customers get is going to be different. We might be able to take different pieces from different from those 127, make it work at one point but the first time any of us makes a change, it's not coordinated with the other side, it's probably going to break. Anyone our life cycles go out 10 plus years and so engineering that altogether is something that makes it all work together as you upgrade whether it be hardware or your applications and so some people confuse that with not being old till 100 percent open. When we find a bug in rel, rel that's been out there for five years maybe we give that fix back to the upstream community that's open it's out there and so I think that's the part that this doesn't become so accepted now and so much part of the mainstream now that we very much confused projects with products and so that's one of the biggest confusion points out there. >> Yeah really good points there Paul. So when I think about some of the things we've heard over the years is in the original days it was, "Oh well public hug Paul? I'm not going to need rel anymore they've got Linux then kubernetes has come along and Red Hat's had a really strong position but you look at it and you say, "Okay well if I'm most customers, "if I'm doing Amazon, "if I'm doing Google, "if I'm doing Microsoft, "I'm probably going to end up using some of their native services that they've got built-in. Talk about how the role of Red Hat kind of continues to change and you live in this multi cloud environment and i think it's kind of that intersection that you were talking about, open and compatibility as opposed to. You're not saying that Red Hat's going to conquer the world and take down all the other options >> Well cloud providers bring a ton of value. I mean the users have to be smart on how and when they use that value. If you truly are going to be a hundred percent of your applications in one public cloud, then you probably will get the best solution from that one public cloud. Serverless is a great example if you're an Amazon and you spin up via services serverless that container that gets spun up is never going to run outside that Club, if that's okay with you that's okay with you. (Voice scrambles) The we've gone about this is as I said to give you that seamless environment all the way across. If you want to run just containers, (voice scrambles) on one particular cloud vendor and you want it under their kubernetes and it's never going to run in any other place, that's okay too but if you're going to have an environment with applications that are in multiple cloud vendors infrastructure you're even on your own, you're now going to have to spin up these different silos of that technology even though the technology as the same heritage. So that's a huge operational and development cost as you grow bigger and able to order to do that and so our set a strategy is very simple, it's give the developers operations and security people that common environment to work across and over time (voice scrambles) they shouldn't care where the services are coming from. It should just all work and that's why you seen things like automation being so important now. I mean our nation is our biggest growing business with ansible right now and part of the reason is as people spread out to a container based environment applications that may now spread across those different footprints maybe you want to have your front (voice scrambles) we have one of the rel customers in Europe that has the front facing customer side of their ticket, their ticketing system up in the public cloud and they've got the backend financial transaction database pieces that click credit cards behind their firewall, that's really one application spread across containers, if you have do you want to have to manage the front end of that with one kubernetes and the backend of that were the different kubernetes? Probably not and so that's really what we bring to the table as we've really grown in with this new technology. >> Alright, so final question I have for you Paul I'm actually going to get away a little bit from your background on the product piece you have to talk a little bit about just red hat going forward. So you talked about, we know for many years red hat has been much more than the Linux piece you talk about automation I've got some great interviews this week talking about some of the the latest in application development, lots of open source projects and so many open source projects (laughing) nobody can keep them all straight there. So as customers look at strategic partnerships, what is the role of red hat and with now being under IBM Jim white her steps over to become president there Arvind of course had a long relationship and it was the architect behind the Red Hat acquisition what's the same and what's different as we think about Red Hat 2020 under your leadership? >> I think it's a lot of the same I mean I think the the difference becomes in the world we're in right now is sort of how we can help our customers come out of back and back into re-entry right and so how that's going to to be different than the past (voice scrambles) we're working through that with many of our customers and we think we can be a big help here because we run their business and today where they run their business over the platforms on their business and that's not going to go away for them and in fact if anything that's going to get even more critical for them because they've got to get more automation to get just more efficiency out of it so in terms of what we do and as a company that's not going to change at all I mean we've been on this path that we're on for a long time. I stand up in front of our sales kickoffs every year is hearing and virtual as well and I say, "we'll to talk to you about the strategy." Guess what? It hasn't changed much from last year and that's a good thing because these technology rollouts are multi-year rollouts, so we're going to continue on that I mean the other thing too is, our customers are seeing moving many more of their work close to the Linux environment and so I think we can help them expand that as well and I think from an IBM perspective (voice scrambles) one of the big premises here from our perspective is to help us scale because they're in the process of helping their customers move to this next generation architectures and at the same time be able support the current architectures and that's what we do well and so they can just help us get to places that we just wouldn't have had the time and the resources maybe to get there get on our own so we can expand that footprint even more quickly with IBM. So that's the focus right now is to really help our customers move to the next phase of this in terms of re-entry >> Yeah as I've heard you and many other Red Hatters say Red Hat is still Red Hat and definitely it's something that we can see loud and clear at Red Hat summit 2020. Thank you so much Paul. >> Thank you Stu nice to see you again. >> All right lots of coverage from Red Hat summit 2020 be sure to check out the cube net for the whole back catalogue that we have of Paul their customers, there their partners and thank you for so watching the queue [Music]

>> Joy: Welcome to the Virtual Big Data Conference. Vertica is so excited to host this event. I'm Joy King, and I'll be your host for today's Big Data Conference Keynote Session. It's my honor and my genuine pleasure to lead Vertica's product and go-to-market strategy. And I'm so lucky to have a passionate and committed team who turned our Vertica BDC event, into a virtual event in a very short amount of time. I want to thank the thousands of people, and yes, that's our true number who have registered to attend this virtual event. We were determined to balance your health, safety and your peace of mind with the excitement of the Vertica BDC. This is a very unique event. Because as I hope you all know, we focus on engineering and architecture, best practice sharing and customer stories that will educate and inspire everyone. I also want to thank our top sponsors for the virtual BDC, Arrow, and Pure Storage. Our partnerships are so important to us and to everyone in the audience. Because together, we get things done faster and better. Now for today's keynote, you'll hear from three very important and energizing speakers. First, Colin Mahony, our SVP and General Manager for Vertica, will talk about the market trends that Vertica is betting on to win for our customers. And he'll share the exciting news about our Vertica 10 announcement and how this will benefit our customers. Then you'll hear from Amy Fowler, VP of strategy and solutions for FlashBlade at Pure Storage. Our partnership with Pure Storage is truly unique in the industry, because together modern infrastructure from Pure powers modern analytics from Vertica. And then you'll hear from John Yovanovich, Director of IT at AT&T, who will tell you about the Pure Vertica Symphony that plays live every day at AT&T. Here we go, Colin, over to you. >> Colin: Well, thanks a lot joy. And, I want to echo Joy's thanks to our sponsors, and so many of you who have helped make this happen. This is not an easy time for anyone. We were certainly looking forward to getting together in person in Boston during the Vertica Big Data Conference and Winning with Data. But I think all of you and our team have done a great job, scrambling and putting together a terrific virtual event. So really appreciate your time. I also want to remind people that we will make both the slides and the full recording available after this. So for any of those who weren't able to join live, that is still going to be available. Well, things have been pretty exciting here. And in the analytic space in general, certainly for Vertica, there's a lot happening. There are a lot of problems to solve, a lot of opportunities to make things better, and a lot of data that can really make every business stronger, more efficient, and frankly, more differentiated. For Vertica, though, we know that focusing on the challenges that we can directly address with our platform, and our people, and where we can actually make the biggest difference is where we ought to be putting our energy and our resources. I think one of the things that has made Vertica so strong over the years is our ability to focus on those areas where we can make a great difference. So for us as we look at the market, and we look at where we play, there are really three recent and some not so recent, but certainly picking up a lot of the market trends that have become critical for every industry that wants to Win Big With Data. We've heard this loud and clear from our customers and from the analysts that cover the market. If I were to summarize these three areas, this really is the core focus for us right now. We know that there's massive data growth. And if we can unify the data silos so that people can really take advantage of that data, we can make a huge difference. We know that public clouds offer tremendous advantages, but we also know that balance and flexibility is critical. And we all need the benefit that machine learning for all the types up to the end data science. We all need the benefits that they can bring to every single use case, but only if it can really be operationalized at scale, accurate and in real time. And the power of Vertica is, of course, how we're able to bring so many of these things together. Let me talk a little bit more about some of these trends. So one of the first industry trends that we've all been following probably now for over the last decade, is Hadoop and specifically HDFS. So many companies have invested, time, money, more importantly, people in leveraging the opportunity that HDFS brought to the market. HDFS is really part of a much broader storage disruption that we'll talk a little bit more about, more broadly than HDFS. But HDFS itself was really designed for petabytes of data, leveraging low cost commodity hardware and the ability to capture a wide variety of data formats, from a wide variety of data sources and applications. And I think what people really wanted, was to store that data before having to define exactly what structures they should go into. So over the last decade or so, the focus for most organizations is figuring out how to capture, store and frankly manage that data. And as a platform to do that, I think, Hadoop was pretty good. It certainly changed the way that a lot of enterprises think about their data and where it's locked up. In parallel with Hadoop, particularly over the last five years, Cloud Object Storage has also given every organization another option for collecting, storing and managing even more data. That has led to a huge growth in data storage, obviously, up on public clouds like Amazon and their S3, Google Cloud Storage and Azure Blob Storage just to name a few. And then when you consider regional and local object storage offered by cloud vendors all over the world, the explosion of that data, in leveraging this type of object storage is very real. And I think, as I mentioned, it's just part of this broader storage disruption that's been going on. But with all this growth in the data, in all these new places to put this data, every organization we talk to is facing even more challenges now around the data silo. Sure the data silos certainly getting bigger. And hopefully they're getting cheaper per bit. But as I said, the focus has really been on collecting, storing and managing the data. But between the new data lakes and many different cloud object storage combined with all sorts of data types from the complexity of managing all this, getting that business value has been very limited. This actually takes me to big bet number one for Team Vertica, which is to unify the data. Our goal, and some of the announcements we have made today plus roadmap announcements I'll share with you throughout this presentation. Our goal is to ensure that all the time, money and effort that has gone into storing that data, all the data turns into business value. So how are we going to do that? With a unified analytics platform that analyzes the data wherever it is HDFS, Cloud Object Storage, External tables in an any format ORC, Parquet, JSON, and of course, our own Native Roth Vertica format. Analyze the data in the right place in the right format, using a single unified tool. This is something that Vertica has always been committed to, and you'll see in some of our announcements today, we're just doubling down on that commitment. Let's talk a little bit more about the public cloud. This is certainly the second trend. It's the second wave maybe of data disruption with object storage. And there's a lot of advantages when it comes to public cloud. There's no question that the public clouds give rapid access to compute storage with the added benefit of eliminating data center maintenance that so many companies, want to get out of themselves. But maybe the biggest advantage that I see is the architectural innovation. The public clouds have introduced so many methodologies around how to provision quickly, separating compute and storage and really dialing-in the exact needs on demand, as you change workloads. When public clouds began, it made a lot of sense for the cloud providers and their customers to charge and pay for compute and storage in the ratio that each use case demanded. And I think you're seeing that trend, proliferate all over the place, not just up in public cloud. That architecture itself is really becoming the next generation architecture for on-premise data centers, as well. But there are a lot of concerns. I think we're all aware of them. They're out there many times for different workloads, there are higher costs. Especially if some of the workloads that are being run through analytics, which tend to run all the time. Just like some of the silo challenges that companies are facing with HDFS, data lakes and cloud storage, the public clouds have similar types of siloed challenges as well. Initially, there was a belief that they were cheaper than data centers, and when you added in all the costs, it looked that way. And again, for certain elastic workloads, that is the case. I don't think that's true across the board overall. Even to the point where a lot of the cloud vendors aren't just charging lower costs anymore. We hear from a lot of customers that they don't really want to tether themselves to any one cloud because of some of those uncertainties. Of course, security and privacy are a concern. We hear a lot of concerns with regards to cloud and even some SaaS vendors around shared data catalogs, across all the customers and not enough separation. But security concerns are out there, you can read about them. I'm not going to jump into that bandwagon. But we hear about them. And then, of course, I think one of the things we hear the most from our customers, is that each cloud stack is starting to feel even a lot more locked in than the traditional data warehouse appliance. And as everybody knows, the industry has been running away from appliances as fast as it can. And so they're not eager to get locked into another, quote, unquote, virtual appliance, if you will, up in the cloud. They really want to make sure they have flexibility in which clouds, they're going to today, tomorrow and in the future. And frankly, we hear from a lot of our customers that they're very interested in eventually mixing and matching, compute from one cloud with, say storage from another cloud, which I think is something that we'll hear a lot more about. And so for us, that's why we've got our big bet number two. we love the cloud. We love the public cloud. We love the private clouds on-premise, and other hosting providers. But our passion and commitment is for Vertica to be able to run in any of the clouds that our customers choose, and make it portable across those clouds. We have supported on-premises and all public clouds for years. And today, we have announced even more support for Vertica in Eon Mode, the deployment option that leverages the separation of compute from storage, with even more deployment choices, which I'm going to also touch more on as we go. So super excited about our big bet number two. And finally as I mentioned, for all the hype that there is around machine learning, I actually think that most importantly, this third trend that team Vertica is determined to address is the need to bring business critical, analytics, machine learning, data science projects into production. For so many years, there just wasn't enough data available to justify the investment in machine learning. Also, processing power was expensive, and storage was prohibitively expensive. But to train and score and evaluate all the different models to unlock the full power of predictive analytics was tough. Today you have those massive data volumes. You have the relatively cheap processing power and storage to make that dream a reality. And if you think about this, I mean with all the data that's available to every company, the real need is to operationalize the speed and the scale of machine learning so that these organizations can actually take advantage of it where they need to. I mean, we've seen this for years with Vertica, going back to some of the most advanced gaming companies in the early days, they were incorporating this with live data directly into their gaming experiences. Well, every organization wants to do that now. And the accuracy for clickability and real time actions are all key to separating the leaders from the rest of the pack in every industry when it comes to machine learning. But if you look at a lot of these projects, the reality is that there's a ton of buzz, there's a ton of hype spanning every acronym that you can imagine. But most companies are struggling, do the separate teams, different tools, silos and the limitation that many platforms are facing, driving, down sampling to get a small subset of the data, to try to create a model that then doesn't apply, or compromising accuracy and making it virtually impossible to replicate models, and understand decisions. And if there's one thing that we've learned when it comes to data, prescriptive data at the atomic level, being able to show end of one as we refer to it, meaning individually tailored data. No matter what it is healthcare, entertainment experiences, like gaming or other, being able to get at the granular data and make these decisions, make that scoring applies to machine learning just as much as it applies to giving somebody a next-best-offer. But the opportunity has never been greater. The need to integrate this end-to-end workflow and support the right tools without compromising on that accuracy. Think about it as no downsampling, using all the data, it really is key to machine learning success. Which should be no surprise then why the third big bet from Vertica is one that we've actually been working on for years. And we're so proud to be where we are today, helping the data disruptors across the world operationalize machine learning. This big bet has the potential to truly unlock, really the potential of machine learning. And today, we're announcing some very important new capabilities specifically focused on unifying the work being done by the data science community, with their preferred tools and platforms, and the volume of data and performance at scale, available in Vertica. Our strategy has been very consistent over the last several years. As I said in the beginning, we haven't deviated from our strategy. Of course, there's always things that we add. Most of the time, it's customer driven, it's based on what our customers are asking us to do. But I think we've also done a great job, not trying to be all things to all people. Especially as these hype cycles flare up around us, we absolutely love participating in these different areas without getting completely distracted. I mean, there's a variety of query tools and data warehouses and analytics platforms in the market. We all know that. There are tools and platforms that are offered by the public cloud vendors, by other vendors that support one or two specific clouds. There are appliance vendors, who I was referring to earlier who can deliver package data warehouse offerings for private data centers. And there's a ton of popular machine learning tools, languages and other kits. But Vertica is the only advanced analytic platform that can do all this, that can bring it together. We can analyze the data wherever it is, in HDFS, S3 Object Storage, or Vertica itself. Natively we support multiple clouds on-premise deployments, And maybe most importantly, we offer that choice of deployment modes to allow our customers to choose the architecture that works for them right now. It still also gives them the option to change move, evolve over time. And Vertica is the only analytics database with end-to-end machine learning that can truly operationalize ML at scale. And I know it's a mouthful. But it is not easy to do all these things. It is one of the things that highly differentiates Vertica from the rest of the pack. It is also why our customers, all of you continue to bet on us and see the value that we are delivering and we will continue to deliver. Here's a couple of examples of some of our customers who are powered by Vertica. It's the scale of data. It's the millisecond response times. Performance and scale have always been a huge part of what we have been about, not the only thing. I think the functionality all the capabilities that we add to the platform, the ease of use, the flexibility, obviously with the deployment. But if you look at some of the numbers they are under these customers on this slide. And I've shared a lot of different stories about these customers. Which, by the way, it still amaze me every time I talk to one and I get the updates, you can see the power and the difference that Vertica is making. Equally important, if you look at a lot of these customers, they are the epitome of being able to deploy Vertica in a lot of different environments. Many of the customers on this slide are not using Vertica just on-premise or just in the cloud. They're using it in a hybrid way. They're using it in multiple different clouds. And again, we've been with them on that journey throughout, which is what has made this product and frankly, our roadmap and our vision exactly what it is. It's been quite a journey. And that journey continues now with the Vertica 10 release. The Vertica 10 release is obviously a massive release for us. But if you look back, you can see that building on that native columnar architecture that started a long time ago, obviously, with the C-Store paper. We built it to leverage that commodity hardware, because it was an architecture that was never tightly integrated with any specific underlying infrastructure. I still remember hearing the initial pitch from Mike Stonebreaker, about the vision of Vertica as a software only solution and the importance of separating the company from hardware innovation. And at the time, Mike basically said to me, "there's so much R&D in innovation that's going to happen in hardware, we shouldn't bake hardware into our solution. We should do it in software, and we'll be able to take advantage of that hardware." And that is exactly what has happened. But one of the most recent innovations that we embraced with hardware is certainly that separation of compute and storage. As I said previously, the public cloud providers offered this next generation architecture, really to ensure that they can provide the customers exactly what they needed, more compute or more storage and charge for each, respectively. The separation of compute and storage, compute from storage is a major milestone in data center architectures. If you think about it, it's really not only a public cloud innovation, though. It fundamentally redefines the next generation data architecture for on-premise and for pretty much every way people are thinking about computing today. And that goes for software too. Object storage is an example of the cost effective means for storing data. And even more importantly, separating compute from storage for analytic workloads has a lot of advantages. Including the opportunity to manage much more dynamic, flexible workloads. And more importantly, truly isolate those workloads from others. And by the way, once you start having something that can truly isolate workloads, then you can have the conversations around autonomic computing, around setting up some nodes, some compute resources on the data that won't affect any of the other data to do some things on their own, maybe some self analytics, by the system, etc. A lot of things that many of you know we've already been exploring in terms of our own system data in the product. But it was May 2018, believe it or not, it seems like a long time ago where we first announced Eon Mode and I want to make something very clear, actually about Eon mode. It's a mode, it's a deployment option for Vertica customers. And I think this is another huge benefit that we don't talk about enough. But unlike a lot of vendors in the market who will dig you and charge you for every single add-on like hit-buy, you name it. You get this with the Vertica product. If you continue to pay support and maintenance, this comes with the upgrade. This comes as part of the new release. So any customer who owns or buys Vertica has the ability to set up either an Enterprise Mode or Eon Mode, which is a question I know that comes up sometimes. Our first announcement of Eon was obviously AWS customers, including the trade desk, AT&T. Most of whom will be speaking here later at the Virtual Big Data Conference. They saw a huge opportunity. Eon Mode, not only allowed Vertica to scale elastically with that specific compute and storage that was needed, but it really dramatically simplified database operations including things like workload balancing, node recovery, compute provisioning, etc. So one of the most popular functions is that ability to isolate the workloads and really allocate those resources without negatively affecting others. And even though traditional data warehouses, including Vertica Enterprise Mode have been able to do lots of different workload isolation, it's never been as strong as Eon Mode. Well, it certainly didn't take long for our customers to see that value across the board with Eon Mode. Not just up in the cloud, in partnership with one of our most valued partners and a platinum sponsor here. Joy mentioned at the beginning. We announced Vertica Eon Mode for Pure Storage FlashBlade in September 2019. And again, just to be clear, this is not a new product, it's one Vertica with yet more deployment options. With Pure Storage, Vertica in Eon mode is not limited in any way by variable cloud, network latency. The performance is actually amazing when you take the benefits of separate and compute from storage and you run it with a Pure environment on-premise. Vertica in Eon Mode has a super smart cache layer that we call the depot. It's a big part of our secret sauce around Eon mode. And combined with the power and performance of Pure's FlashBlade, Vertica became the industry's first advanced analytics platform that actually separates compute and storage for on-premises data centers. Something that a lot of our customers are already benefiting from, and we're super excited about it. But as I said, this is a journey. We don't stop, we're not going to stop. Our customers need the flexibility of multiple public clouds. So today with Vertica 10, we're super proud and excited to announce support for Vertica in Eon Mode on Google Cloud. This gives our customers the ability to use their Vertica licenses on Amazon AWS, on-premise with Pure Storage and on Google Cloud. Now, we were talking about HDFS and a lot of our customers who have invested quite a bit in HDFS as a place, especially to store data have been pushing us to support Eon Mode with HDFS. So as part of Vertica 10, we are also announcing support for Vertica in Eon Mode using HDFS as the communal storage. Vertica's own Roth format data can be stored in HDFS, and actually the full functionality of Vertica is complete analytics, geospatial pattern matching, time series, machine learning, everything that we have in there can be applied to this data. And on the same HDFS nodes, Vertica can actually also analyze data in ORC or Parquet format, using External tables. We can also execute joins between the Roth data the External table holds, which powers a much more comprehensive view. So again, it's that flexibility to be able to support our customers, wherever they need us to support them on whatever platform, they have. Vertica 10 gives us a lot more ways that we can deploy Eon Mode in various environments for our customers. It allows them to take advantage of Vertica in Eon Mode and the power that it brings with that separation, with that workload isolation, to whichever platform they are most comfortable with. Now, there's a lot that has come in Vertica 10. I'm definitely not going to be able to cover everything. But we also introduced complex types as an example. And complex data types fit very well into Eon as well in this separation. They significantly reduce the data pipeline, the cost of moving data between those, a much better support for unstructured data, which a lot of our customers have mixed with structured data, of course, and they leverage a lot of columnar execution that Vertica provides. So you get complex data types in Vertica now, a lot more data, stronger performance. It goes great with the announcement that we made with the broader Eon Mode. Let's talk a little bit more about machine learning. We've been actually doing work in and around machine learning with various extra regressions and a whole bunch of other algorithms for several years. We saw the huge advantage that MPP offered, not just as a sequel engine as a database, but for ML as well. Didn't take as long to realize that there's a lot more to operationalizing machine learning than just those algorithms. It's data preparation, it's that model trade training. It's the scoring, the shaping, the evaluation. That is so much of what machine learning and frankly, data science is about. You do know, everybody always wants to jump to the sexy algorithm and we handle those tasks very, very well. It makes Vertica a terrific platform to do that. A lot of work in data science and machine learning is done in other tools. I had mentioned that there's just so many tools out there. We want people to be able to take advantage of all that. We never believed we were going to be the best algorithm company or come up with the best models for people to use. So with Vertica 10, we support PMML. We can import now and export PMML models. It's a huge step for us around that operationalizing machine learning projects for our customers. Allowing the models to get built outside of Vertica yet be imported in and then applying to that full scale of data with all the performance that you would expect from Vertica. We also are more tightly integrating with Python. As many of you know, we've been doing a lot of open source projects with the community driven by many of our customers, like Uber. And so now with Python we've integrated with TensorFlow, allowing data scientists to build models in their preferred language, to take advantage of TensorFlow. But again, to store and deploy those models at scale with Vertica. I think both these announcements are proof of our big bet number three, and really our commitment to supporting innovation throughout the community by operationalizing ML with that accuracy, performance and scale of Vertica for our customers. Again, there's a lot of steps when it comes to the workflow of machine learning. These are some of them that you can see on the slide, and it's definitely not linear either. We see this as a circle. And companies that do it, well just continue to learn, they continue to rescore, they continue to redeploy and they want to operationalize all that within a single platform that can take advantage of all those capabilities. And that is the platform, with a very robust ecosystem that Vertica has always been committed to as an organization and will continue to be. This graphic, many of you have seen it evolve over the years. Frankly, if we put everything and everyone on here wouldn't fit on a slide. But it will absolutely continue to evolve and grow as we support our customers, where they need the support most. So, again, being able to deploy everywhere, being able to take advantage of Vertica, not just as a business analyst or a business user, but as a data scientists or as an operational or BI person. We want Vertica to be leveraged and used by the broader organization. So I think it's fair to say and I encourage everybody to learn more about Vertica 10, because I'm just highlighting some of the bigger aspects of it. But we talked about those three market trends. The need to unify the silos, the need for hybrid multiple cloud deployment options, the need to operationalize business critical machine learning projects. Vertica 10 has absolutely delivered on those. But again, we are not going to stop. It is our job not to, and this is how Team Vertica thrives. I always joke that the next release is the best release. And, of course, even after Vertica 10, that is also true, although Vertica 10 is pretty awesome. But, you know, from the first line of code, we've always been focused on performance and scale, right. And like any really strong data platform, the execution engine, the optimizer and the execution engine are the two core pieces of that. Beyond Vertica 10, some of the big things that we're already working on, next generation execution engine. We're already actually seeing incredible early performance from this. And this is just one example, of how important it is for an organization like Vertica to constantly go back and re-innovate. Every single release, we do the sit ups and crunches, our performance and scale. How do we improve? And there's so many parts of the core server, there's so many parts of our broader ecosystem. We are constantly looking at coverages of how we can go back to all the code lines that we have, and make them better in the current environment. And it's not an easy thing to do when you're doing that, and you're also expanding in the environment that we are expanding into to take advantage of the different deployments, which is a great segue to this slide. Because if you think about today, we're obviously already available with Eon Mode and Amazon, AWS and Pure and actually MinIO as well. As I talked about in Vertica 10 we're adding Google and HDFS. And coming next, obviously, Microsoft Azure, Alibaba cloud. So being able to expand into more of these environments is really important for the Vertica team and how we go forward. And it's not just running in these clouds, for us, we want it to be a SaaS like experience in all these clouds. We want you to be able to deploy Vertica in 15 minutes or less on these clouds. You can also consume Vertica, in a lot of different ways, on these clouds. As an example, in Amazon Vertica by the Hour. So for us, it's not just about running, it's about taking advantage of the ecosystems that all these cloud providers offer, and really optimizing the Vertica experience as part of them. Optimization, around automation, around self service capabilities, extending our management console, we now have products that like the Vertica Advisor Tool that our Customer Success Team has created to actually use our own smarts in Vertica. To take data from customers that give it to us and help them tune automatically their environment. You can imagine that we're taking that to the next level, in a lot of different endeavors that we're doing around how Vertica as a product can actually be smarter because we all know that simplicity is key. There just aren't enough people in the world who are good at managing data and taking it to the next level. And of course, other things that we all hear about, whether it's Kubernetes and containerization. You can imagine that that probably works very well with the Eon Mode and separating compute and storage. But innovation happens everywhere. We innovate around our community documentation. Many of you have taken advantage of the Vertica Academy. The numbers there are through the roof in terms of the number of people coming in and certifying on it. So there's a lot of things that are within the core products. There's a lot of activity and action beyond the core products that we're taking advantage of. And let's not forget why we're here, right? It's easy to talk about a platform, a data platform, it's easy to jump into all the functionality, the analytics, the flexibility, how we can offer it. But at the end of the day, somebody, a person, she's got to take advantage of this data, she's got to be able to take this data and use this information to make a critical business decision. And that doesn't happen unless we explore lots of different and frankly, new ways to get that predictive analytics UI and interface beyond just the standard BI tools in front of her at the right time. And so there's a lot of activity, I'll tease you with that going on in this organization right now about how we can do that and deliver that for our customers. We're in a great position to be able to see exactly how this data is consumed and used and start with this core platform that we have to go out. Look, I know, the plan wasn't to do this as a virtual BDC. But I really appreciate you tuning in. Really appreciate your support. I think if there's any silver lining to us, maybe not being able to do this in person, it's the fact that the reach has actually gone significantly higher than what we would have been able to do in person in Boston. We're certainly looking forward to doing a Big Data Conference in the future. But if I could leave you with anything, know this, since that first release for Vertica, and our very first customers, we have been very consistent. We respect all the innovation around us, whether it's open source or not. We understand the market trends. We embrace those new ideas and technologies and for us true north, and the most important thing is what does our customer need to do? What problem are they trying to solve? And how do we use the advantages that we have without disrupting our customers? But knowing that you depend on us to deliver that unified analytics strategy, it will deliver that performance of scale, not only today, but tomorrow and for years to come. We've added a lot of great features to Vertica. I think we've said no to a lot of things, frankly, that we just knew we wouldn't be the best company to deliver. When we say we're going to do things we do them. Vertica 10 is a perfect example of so many of those things that we from you, our customers have heard loud and clear, and we have delivered. I am incredibly proud of this team across the board. I think the culture of Vertica, a customer first culture, jumping in to help our customers win no matter what is also something that sets us massively apart. I hear horror stories about support experiences with other organizations. And people always seem to be amazed at Team Vertica's willingness to jump in or their aptitude for certain technical capabilities or understanding the business. And I think sometimes we take that for granted. But that is the team that we have as Team Vertica. We are incredibly excited about Vertica 10. I think you're going to love the Virtual Big Data Conference this year. I encourage you to tune in. Maybe one other benefit is I know some people were worried about not being able to see different sessions because they were going to overlap with each other well now, even if you can't do it live, you'll be able to do those sessions on demand. Please enjoy the Vertica Big Data Conference here in 2020. Please you and your families and your co-workers be safe during these times. I know we will get through it. And analytics is probably going to help with a lot of that and we already know it is helping in many different ways. So believe in the data, believe in data's ability to change the world for the better. And thank you for your time. And with that, I am delighted to now introduce Micro Focus CEO Stephen Murdoch to the Vertica Big Data Virtual Conference. Thank you Stephen. >> Stephen: Hi, everyone, my name is Stephen Murdoch. I have the pleasure and privilege of being the Chief Executive Officer here at Micro Focus. Please let me add my welcome to the Big Data Conference. And also my thanks for your support, as we've had to pivot to this being virtual rather than a physical conference. Its amazing how quickly we all reset to a new normal. I certainly didn't expect to be addressing you from my study. Vertica is an incredibly important part of Micro Focus family. Is key to our goal of trying to enable and help customers become much more data driven across all of their IT operations. Vertica 10 is a huge step forward, we believe. It allows for multi-cloud innovation, genuinely hybrid deployments, begin to leverage machine learning properly in the enterprise, and also allows the opportunity to unify currently siloed lakes of information. We operate in a very noisy, very competitive market, and there are people, who are in that market who can do some of those things. The reason we are so excited about Vertica is we genuinely believe that we are the best at doing all of those things. And that's why we've announced publicly, you're under executing internally, incremental investment into Vertica. That investments targeted at accelerating the roadmaps that already exist. And getting that innovation into your hands faster. This idea is speed is key. It's not a question of if companies have to become data driven organizations, it's a question of when. So that speed now is really important. And that's why we believe that the Big Data Conference gives a great opportunity for you to accelerate your own plans. You will have the opportunity to talk to some of our best architects, some of the best development brains that we have. But more importantly, you'll also get to hear from some of our phenomenal Roth Data customers. You'll hear from Uber, from the Trade Desk, from Philips, and from AT&T, as well as many many others. And just hearing how those customers are using the power of Vertica to accelerate their own, I think is the highlight. And I encourage you to use this opportunity to its full. Let me close by, again saying thank you, we genuinely hope that you get as much from this virtual conference as you could have from a physical conference. And we look forward to your engagement, and we look forward to hearing your feedback. With that, thank you very much. >> Joy: Thank you so much, Stephen, for joining us for the Vertica Big Data Conference. Your support and enthusiasm for Vertica is so clear, and it makes a big difference. Now, I'm delighted to introduce Amy Fowler, the VP of Strategy and Solutions for FlashBlade at Pure Storage, who was one of our BDC Platinum Sponsors, and one of our most valued partners. It was a proud moment for me, when we announced Vertica in Eon mode for Pure Storage FlashBlade and we became the first analytics data warehouse that separates compute from storage for on-premise data centers. Thank you so much, Amy, for joining us. Let's get started. >> Amy: Well, thank you, Joy so much for having us. And thank you all for joining us today, virtually, as we may all be. So, as we just heard from Colin Mahony, there are some really interesting trends that are happening right now in the big data analytics market. From the end of the Hadoop hype cycle, to the new cloud reality, and even the opportunity to help the many data science and machine learning projects move from labs to production. So let's talk about these trends in the context of infrastructure. And in particular, look at why a modern storage platform is relevant as organizations take on the challenges and opportunities associated with these trends. The answer is the Hadoop hype cycles left a lot of data in HDFS data lakes, or reservoirs or swamps depending upon the level of the data hygiene. But without the ability to get the value that was promised from Hadoop as a platform rather than a distributed file store. And when we combine that data with the massive volume of data in Cloud Object Storage, we find ourselves with a lot of data and a lot of silos, but without a way to unify that data and find value in it. Now when you look at the infrastructure data lakes are traditionally built on, it is often direct attached storage or data. The approach that Hadoop took when it entered the market was primarily bound by the limits of networking and storage technologies. One gig ethernet and slower spinning disk. But today, those barriers do not exist. And all FlashStorage has fundamentally transformed how data is accessed, managed and leveraged. The need for local data storage for significant volumes of data has been largely mitigated by the performance increases afforded by all Flash. At the same time, organizations can achieve superior economies of scale with that segregation of compute and storage. With compute and storage, you don't always scale in lockstep. Would you want to add an engine to the train every time you add another boxcar? Probably not. But from a Pure Storage perspective, FlashBlade is uniquely architected to allow customers to achieve better resource utilization for compute and storage, while at the same time, reducing complexity that has arisen from the siloed nature of the original big data solutions. The second and equally important recent trend we see is something I'll call cloud reality. The public clouds made a lot of promises and some of those promises were delivered. But cloud economics, especially usage based and elastic scaling, without the control that many companies need to manage the financial impact is causing a lot of issues. In addition, the risk of vendor lock-in from data egress, charges, to integrated software stacks that can't be moved or deployed on-premise is causing a lot of organizations to back off the all the way non-cloud strategy, and move toward hybrid deployments. Which is kind of funny in a way because it wasn't that long ago that there was a lot of talk about no more data centers. And for example, one large retailer, I won't name them, but I'll admit they are my favorites. They several years ago told us they were completely done with on-prem storage infrastructure, because they were going 100% to the cloud. But they just deployed FlashBlade for their data pipelines, because they need predictable performance at scale. And the all cloud TCO just didn't add up. Now, that being said, well, there are certainly challenges with the public cloud. It has also brought some things to the table that we see most organizations wanting. First of all, in a lot of cases applications have been built to leverage object storage platforms like S3. So they need that object protocol, but they may also need it to be fast. And the said object may be oxymoron only a few years ago, and this is an area of the market where Pure and FlashBlade have really taken a leadership position. Second, regardless of where the data is physically stored, organizations want the best elements of a cloud experience. And for us, that means two main things. Number one is simplicity and ease of use. If you need a bunch of storage experts to run the system, that should be considered a bug. The other big one is the consumption model. The ability to pay for what you need when you need it, and seamlessly grow your environment over time totally nondestructively. This is actually pretty huge and something that a lot of vendors try to solve for with finance programs. But no finance program can address the pain of a forklift upgrade, when you need to move to next gen hardware. To scale nondestructively over long periods of time, five to 10 years plus is a crucial architectural decisions need to be made at the outset. Plus, you need the ability to pay as you use it. And we offer something for FlashBlade called Pure as a Service, which delivers exactly that. The third cloud characteristic that many organizations want is the option for hybrid. Even if that is just a DR site in the cloud. In our case, that means supporting appplication of S3, at the AWS. And the final trend, which to me represents the biggest opportunity for all of us, is the need to help the many data science and machine learning projects move from labs to production. This means bringing all the machine learning functions and model training to the data, rather than moving samples or segments of data to separate platforms. As we all know, machine learning needs a ton of data for accuracy. And there is just too much data to retrieve from the cloud for every training job. At the same time, predictive analytics without accuracy is not going to deliver the business advantage that everyone is seeking. You can kind of visualize data analytics as it is traditionally deployed as being on a continuum. With that thing, we've been doing the longest, data warehousing on one end, and AI on the other end. But the way this manifests in most environments is a series of silos that get built up. So data is duplicated across all kinds of bespoke analytics and AI, environments and infrastructure. This creates an expensive and complex environment. So historically, there was no other way to do it because some level of performance is always table stakes. And each of these parts of the data pipeline has a different workload profile. A single platform to deliver on the multi dimensional performances, diverse set of applications required, that didn't exist three years ago. And that's why the application vendors pointed you towards bespoke things like DAS environments that we talked about earlier. And the fact that better options exists today is why we're seeing them move towards supporting this disaggregation of compute and storage. And when it comes to a platform that is a better option, one with a modern architecture that can address the diverse performance requirements of this continuum, and allow organizations to bring a model to the data instead of creating separate silos. That's exactly what FlashBlade is built for. Small files, large files, high throughput, low latency and scale to petabytes in a single namespace. And this is importantly a single rapid space is what we're focused on delivering for our customers. At Pure, we talk about it in the context of modern data experience because at the end of the day, that's what it's really all about. The experience for your teams in your organization. And together Pure Storage and Vertica have delivered that experience to a wide range of customers. From a SaaS analytics company, which uses Vertica on FlashBlade to authenticate the quality of digital media in real time, to a multinational car company, which uses Vertica on FlashBlade to make thousands of decisions per second for autonomous cars, or a healthcare organization, which uses Vertica on FlashBlade to enable healthcare providers to make real time decisions that impact lives. And I'm sure you're all looking forward to hearing from John Yavanovich from AT&T. To hear how he's been doing this with Vertica and FlashBlade as well. He's coming up soon. We have been really excited to build this partnership with Vertica. And we're proud to provide the only on-premise storage platform validated with Vertica Eon Mode. And deliver this modern data experience to our customers together. Thank you all so much for joining us today. >> Joy: Amy, thank you so much for your time and your insights. Modern infrastructure is key to modern analytics, especially as organizations leverage next generation data center architectures, and object storage for their on-premise data centers. Now, I'm delighted to introduce our last speaker in our Vertica Big Data Conference Keynote, John Yovanovich, Director of IT for AT&T. Vertica is so proud to serve AT&T, and especially proud of the harmonious impact we are having in partnership with Pure Storage. John, welcome to the Virtual Vertica BDC. >> John: Thank you joy. It's a pleasure to be here. And I'm excited to go through this presentation today. And in a unique fashion today 'cause as I was thinking through how I wanted to present the partnership that we have formed together between Pure Storage, Vertica and AT&T, I want to emphasize how well we all work together and how these three components have really driven home, my desire for a harmonious to use your word relationship. So, I'm going to move forward here and with. So here, what I'm going to do the theme of today's presentation is the Pure Vertica Symphony live at AT&T. And if anybody is a Westworld fan, you can appreciate the sheet music on the right hand side. What we're going to what I'm going to highlight here is in a musical fashion, is how we at AT&T leverage these technologies to save money to deliver a more efficient platform, and to actually just to make our customers happier overall. So as we look back, and back as early as just maybe a few years ago here at AT&T, I realized that we had many musicians to help the company. Or maybe you might want to call them data scientists, or data analysts. For the theme we'll stay with musicians. None of them were singing or playing from the same hymn book or sheet music. And so what we had was many organizations chasing a similar dream, but not exactly the same dream. And, best way to describe that is and I think with a lot of people this might resonate in your organizations. How many organizations are chasing a customer 360 view in your company? Well, I can tell you that I have at least four in my company. And I'm sure there are many that I don't know of. That is our problem because what we see is a repetitive sourcing of data. We see a repetitive copying of data. And there's just so much money to be spent. This is where I asked Pure Storage and Vertica to help me solve that problem with their technologies. What I also noticed was that there was no coordination between these departments. In fact, if you look here, nobody really wants to play with finance. Sales, marketing and care, sure that you all copied each other's data. But they actually didn't communicate with each other as they were copying the data. So the data became replicated and out of sync. This is a challenge throughout, not just my company, but all companies across the world. And that is, the more we replicate the data, the more problems we have at chasing or conquering the goal of single version of truth. In fact, I kid that I think that AT&T, we actually have adopted the multiple versions of truth, techno theory, which is not where we want to be, but this is where we are. But we are conquering that with the synergies between Pure Storage and Vertica. This is what it leaves us with. And this is where we are challenged and that if each one of our siloed business units had their own stories, their own dedicated stories, and some of them had more money than others so they bought more storage. Some of them anticipating storing more data, and then they really did. Others are running out of space, but can't put anymore because their bodies aren't been replenished. So if you look at it from this side view here, we have a limited amount of compute or fixed compute dedicated to each one of these silos. And that's because of the, wanting to own your own. And the other part is that you are limited or wasting space, depending on where you are in the organization. So there were the synergies aren't just about the data, but actually the compute and the storage. And I wanted to tackle that challenge as well. So I was tackling the data. I was tackling the storage, and I was tackling the compute all at the same time. So my ask across the company was can we just please play together okay. And to do that, I knew that I wasn't going to tackle this by getting everybody in the same room and getting them to agree that we needed one account table, because they will argue about whose account table is the best account table. But I knew that if I brought the account tables together, they would soon see that they had so much redundancy that I can now start retiring data sources. I also knew that if I brought all the compute together, that they would all be happy. But I didn't want them to tackle across tackle each other. And in fact that was one of the things that all business units really enjoy. Is they enjoy the silo of having their own compute, and more or less being able to control their own destiny. Well, Vertica's subclustering allows just that. And this is exactly what I was hoping for, and I'm glad they've brought through. And finally, how did I solve the problem of the single account table? Well when you don't have dedicated storage, and you can separate compute and storage as Vertica in Eon Mode does. And we store the data on FlashBlades, which you see on the left and right hand side, of our container, which I can describe in a moment. Okay, so what we have here, is we have a container full of compute with all the Vertica nodes sitting in the middle. Two loader, we'll call them loader subclusters, sitting on the sides, which are dedicated to just putting data onto the FlashBlades, which is sitting on both ends of the container. Now today, I have two dedicated storage or common dedicated might not be the right word, but two storage racks one on the left one on the right. And I treat them as separate storage racks. They could be one, but i created them separately for disaster recovery purposes, lashing work in case that rack were to go down. But that being said, there's no reason why I'm probably going to add a couple of them here in the future. So I can just have a, say five to 10, petabyte storage, setup, and I'll have my DR in another 'cause the DR shouldn't be in the same container. Okay, but I'll DR outside of this container. So I got them all together, I leveraged subclustering, I leveraged separate and compute. I was able to convince many of my clients that they didn't need their own account table, that they were better off having one. I eliminated, I reduced latency, I reduced our ticketing I reduce our data quality issues AKA ticketing okay. I was able to expand. What is this? As work. I was able to leverage elasticity within this cluster. As you can see, there are racks and racks of compute. We set up what we'll call the fixed capacity that each of the business units needed. And then I'm able to ramp up and release the compute that's necessary for each one of my clients based on their workloads throughout the day. And so while they compute to the right before you see that the instruments have already like, more or less, dedicated themselves towards all those are free for anybody to use. So in essence, what I have, is I have a concert hall with a lot of seats available. So if I want to run a 10 chair Symphony or 80, chairs, Symphony, I'm able to do that. And all the while, I can also do the same with my loader nodes. I can expand my loader nodes, to actually have their own Symphony or write all to themselves and not compete with any other workloads of the other clusters. What does that change for our organization? Well, it really changes the way our database administrators actually do their jobs. This has been a big transformation for them. They have actually become data conductors. Maybe you might even call them composers, which is interesting, because what I've asked them to do is morph into less technology and more workload analysis. And in doing so we're able to write auto-detect scripts, that watch the queues, watch the workloads so that we can help ramp up and trim down the cluster and subclusters as necessary. There has been an exciting transformation for our DBAs, who I need to now classify as something maybe like DCAs. I don't know, I have to work with HR on that. But I think it's an exciting future for their careers. And if we bring it all together, If we bring it all together, and then our clusters, start looking like this. Where everything is moving in harmonious, we have lots of seats open for extra musicians. And we are able to emulate a cloud experience on-prem. And so, I want you to sit back and enjoy the Pure Vertica Symphony live at AT&T. (soft music) >> Joy: Thank you so much, John, for an informative and very creative look at the benefits that AT&T is getting from its Pure Vertica symphony. I do really like the idea of engaging HR to change the title to Data Conductor. That's fantastic. I've always believed that music brings people together. And now it's clear that analytics at AT&T is part of that musical advantage. So, now it's time for a short break. And we'll be back for our breakout sessions, beginning at 12 pm Eastern Daylight Time. We have some really exciting sessions planned later today. And then again, as you can see on Wednesday. Now because all of you are already logged in and listening to this keynote, you already know the steps to continue to participate in the sessions that are listed here and on the previous slide. In addition, everyone received an email yesterday, today, and you'll get another one tomorrow, outlining the simple steps to register, login and choose your session. If you have any questions, check out the emails or go to www.vertica.com/bdc2020 for the logistics information. There are a lot of choices and that's always a good thing. Don't worry if you want to attend one or more or can't listen to these live sessions due to your timezone. All the sessions, including the Q&A sections will be available on demand and everyone will have access to the recordings as well as even more pre-recorded sessions that we'll post to the BDC website. Now I do want to leave you with two other important sites. First, our Vertica Academy. Vertica Academy is available to everyone. And there's a variety of very technical, self-paced, on-demand training, virtual instructor-led workshops, and Vertica Essentials Certification. And it's all free. Because we believe that Vertica expertise, helps everyone accelerate their Vertica projects and the advantage that those projects deliver. Now, if you have questions or want to engage with our Vertica engineering team now, we're waiting for you on the Vertica forum. We'll answer any questions or discuss any ideas that you might have. Thank you again for joining the Vertica Big Data Conference Keynote Session. Enjoy the rest of the BDC because there's a lot more to come