>>Hey, welcome back everyone. Day three of eight of us Reinvent 2022. I'm John Farmer with Dave Volante, co-host the q Dave. 10 years for us, the leader in high tech coverage is our slogan. Now 10 years of reinvent day. We've been to every single one except with the original, which we would've come to if Amazon actually marketed the event, but they didn't. It's more of a customer event. This is day three. Is the machine learning ai keynote sws up there. A lot of announcements. We're gonna break this down. We got, we got Andy Thra here, vice President, prince Constellation Research. Andy, great to see you've been on the cube before one of our analysts bringing the, bringing the, the analysis, commentary to the keynote. This is your wheelhouse. Ai. What do you think about Swami up there? I mean, he's awesome. We love him. Big fan Oh yeah. Of of the Cuban we're fans of him, but he got 13 announcements. >>A lot. A lot, >>A lot. >>So, well some of them are, first of all, thanks for having me here and I'm glad to have both of you on the same show attacking me. I'm just kidding. But some of the announcement really sort of like a game changer announcements and some of them are like, meh, you know, just to plug in the holes what they have and a lot of golf claps. Yeah. Meeting today. And you could have also noticed that by, when he was making the announcements, you know, the, the, the clapping volume difference, you could say, which is better, right? But some of the announcements are, are really, really good. You know, particularly we talked about, one of that was Microsoft took that out of, you know, having the open AI in there, doing the large language models. And then they were going after that, you know, having the transformer available to them. And Amazon was a little bit weak in the area, so they couldn't, they don't have a large language model. So, you know, they, they are taking a different route saying that, you know what, I'll help you train the large language model by yourself, customized models. So I can provide the necessary instance. I can provide the instant volume, memory, the whole thing. Yeah. So you can train the model by yourself without depending on them kind >>Of thing. So Dave and Andy, I wanna get your thoughts cuz first of all, we've been following Amazon's deep bench on the, on the infrastructure pass. They've been doing a lot of machine learning and ai, a lot of data. It just seems that the sentiment is that there's other competitors doing a good job too. Like Google, Dave. And I've heard folks in the hallway, even here, ex Amazonians saying, Hey, they're train their models on Google than they bring up the SageMaker cuz it's better interface. So you got, Google's making a play for being that data cloud. Microsoft's obviously putting in a, a great kind of package to kind of make it turnkey. How do they really stand versus the competition guys? >>Good question. So they, you know, each have their own uniqueness and the we variation that take it to the field, right? So for example, if you were to look at it, Microsoft is known for as industry or later things that they are been going after, you know, industry verticals and whatnot. So that's one of the things I looked here, you know, they, they had this omic announcement, particularly towards that healthcare genomics space. That's a huge space for hpz related AIML applications. And they have put a lot of things in together in here in the SageMaker and in the, in their models saying that, you know, how do you, how do you use this transmit to do things like that? Like for example, drug discovery, for genomics analysis, for cancer treatment, the whole, right? That's a few volumes of data do. So they're going in that healthcare area. Google has taken a different route. I mean they want to make everything simple. All I have to do is I gotta call an api, give what I need and then get it done. But Amazon wants to go at a much deeper level saying that, you know what? I wanna provide everything you need. You can customize the whole thing for what you need. >>So to me, the big picture here is, and and Swami references, Hey, we are a data company. We started, he talked about books and how that informed them as to, you know, what books to place front and center. Here's the, here's the big picture. In my view, companies need to put data at the core of their business and they haven't, they've generally put humans at the core of their business and data. And now machine learning are at the, at the outside and the periphery. Amazon, Google, Microsoft, Facebook have put data at their core. So the question is how do incumbent companies, and you mentioned some Toyota Capital One, Bristol Myers Squibb, I don't know, are those data companies, you know, we'll see, but the challenge is most companies don't have the resources as you well know, Andy, to actually implement what Google and Facebook and others have. >>So how are they gonna do that? Well, they're gonna buy it, right? So are they gonna build it with tools that's kind of like you said the Amazon approach or are they gonna buy it from Microsoft and Google, I pulled some ETR data to say, okay, who are the top companies that are showing up in terms of spending? Who's spending with whom? AWS number one, Microsoft number two, Google number three, data bricks. Number four, just in terms of, you know, presence. And then it falls down DataRobot, Anaconda data icu, Oracle popped up actually cuz they're embedding a lot of AI into their products and, and of course IBM and then a lot of smaller companies. But do companies generally customers have the resources to do what it takes to implement AI into applications and into workflows? >>So a couple of things on that. One is when it comes to, I mean it's, it's no surprise that the, the top three or the hyperscalers, because they all want to bring their business to them to run the specific workloads on the next biggest workload. As you was saying, his keynote are two things. One is the A AIML workloads and the other one is the, the heavy unstructured workloads that he was talking about. 80%, 90% of the data that's coming off is unstructured. So how do you analyze that? Such as the geospatial data. He was talking about the volumes of data you need to analyze the, the neural deep neural net drug you ought to use, only hyperscale can do it, right? So that's no wonder all of them on top for the data, one of the things they announced, which not many people paid attention, there was a zero eight L that that they talked about. >>What that does is a little bit of a game changing moment in a sense that you don't have to, for example, if you were to train the data, data, if the data is distributed everywhere, if you have to bring them all together to integrate it, to do that, it's a lot of work to doing the dl. So by taking Amazon, Aurora, and then Rich combine them as zero or no ETL and then have Apaches Apaches Spark applications run on top of analytical applications, ML workloads. That's huge. So you don't have to move around the data, use the data where it is, >>I, I think you said it, they're basically filling holes, right? Yeah. They created this, you know, suite of tools, let's call it. You might say it's a mess. It's not a mess because it's, they're really powerful but they're not well integrated and now they're starting to take the seams as I say. >>Well yeah, it's a great point. And I would double down and say, look it, I think that boring is good. You know, we had that phase in Kubernetes hype cycle where it got boring and that was kind of like, boring is good. Boring means we're getting better, we're invisible. That's infrastructure that's in the weeds, that's in between the toes details. It's the stuff that, you know, people we have to get done. So, you know, you look at their 40 new data sources with data Wrangler 50, new app flow connectors, Redshift Auto Cog, this is boring. Good important shit Dave. The governance, you gotta get it and the governance is gonna be key. So, so to me, this may not jump off the page. Adam's keynote also felt a little bit of, we gotta get these gaps done in a good way. So I think that's a very positive sign. >>Now going back to the bigger picture, I think the real question is can there be another independent cloud data cloud? And that's the, to me, what I try to get at my story and you're breaking analysis kind of hit a home run on this, is there's interesting opportunity for an independent data cloud. Meaning something that isn't aws, that isn't, Google isn't one of the big three that could sit in. And so let me give you an example. I had a conversation last night with a bunch of ex Amazonian engineering teams that left the conversation was interesting, Dave. They were like talking, well data bricks and Snowflake are basically batch, okay, not transactional. And you look at Aerospike, I can see their booth here. Transactional data bases are hot right now. Streaming data is different. Confluence different than data bricks. Is data bricks good at hosting? >>No, Amazon's better. So you start to see these kinds of questions come up where, you know, data bricks is great, but maybe not good for this, that and the other thing. So you start to see the formation of swim lanes or visibility into where people might sit in the ecosystem, but what came out was transactional. Yep. And batch the relationship there and streaming real time and versus you know, the transactional data. So you're starting to see these new things emerge. Andy, what do you, what's your take on this? You're following this closely. This seems to be the alpha nerd conversation and it all points to who's gonna have the best data cloud, say data, super clouds, I call it. What's your take? >>Yes, data cloud is important as well. But also the computational that goes on top of it too, right? Because when, when the data is like unstructured data, it's that much of a huge data, it's going to be hard to do that with a low model, you know, compute power. But going back to your data point, the training of the AIML models required the batch data, right? That's when you need all the, the historical data to train your models. And then after that, when you do inference of it, that's where you need the streaming real time data that's available to you too. You can make an inference. One of the things, what, what they also announced, which is somewhat interesting, is you saw that they have like 700 different instances geared towards every single workload. And there are some of them very specifically run on the Amazon's new chip. The, the inference in two and theran tr one chips that basically not only has a specific instances but also is run on a high powered chip. And then if you have that data to support that, both the training as well as towards the inference, the efficiency, again, those numbers have to be proven. They claim that it could be anywhere between 40 to 60% faster. >>Well, so a couple things. You're definitely right. I mean Snowflake started out as a data warehouse that was simpler and it's not architected, you know, in and it's first wave to do real time inference, which is not now how, how could they, the other second point is snowflake's two or three years ahead when it comes to governance, data sharing. I mean, Amazon's doing what always does. It's copying, you know, it's customer driven. Cuz they probably walk into an account and they say, Hey look, what's Snowflake's doing for us? This stuff's kicking ass. And they go, oh, that's a good idea, let's do that too. You saw that with separating compute from storage, which is their tiering. You saw it today with extending data, sharing Redshift, data sharing. So how does Snowflake and data bricks approach this? They deal with ecosystem. They bring in ecosystem partners, they bring in open source tooling and that's how they compete. I think there's unquestionably an opportunity for a data cloud. >>Yeah, I think, I think the super cloud conversation and then, you know, sky Cloud with Berkeley Paper and other folks talking about this kind of pre, multi-cloud era. I mean that's what I would call us right now. We are, we're kind of in the pre era of multi-cloud, which by the way is not even yet defined. I think people use that term, Dave, to say, you know, some sort of magical thing that's happening. Yeah. People have multiple clouds. They got, they, they end up by default, not by design as Dell likes to say. Right? And they gotta deal with it. So it's more of they're inheriting multiple cloud environments. It's not necessarily what they want in the situation. So to me that is a big, big issue. >>Yeah, I mean, again, going back to your snowflake and data breaks announcements, they're a data company. So they, that's how they made their mark in the market saying that, you know, I do all those things, therefore you have, I had to have your data because it's a seamless data. And, and Amazon is catching up with that with a lot of that announcements they made, how far it's gonna get traction, you know, to change when I to say, >>Yeah, I mean to me, to me there's no doubt about Dave. I think, I think what Swamee is doing, if Amazon can get corner the market on out of the box ML and AI capabilities so that people can make it easier, that's gonna be the end of the day tell sign can they fill in the gaps. Again, boring is good competition. I don't know mean, mean I'm not following the competition. Andy, this is a real question mark for me. I don't know where they stand. Are they more comprehensive? Are they more deeper? Are they have deeper services? I mean, obviously shows to all the, the different, you know, capabilities. Where, where, where does Amazon stand? What's the process? >>So what, particularly when it comes to the models. So they're going at, at a different angle that, you know, I will help you create the models we talked about the zero and the whole data. We'll get the data sources in, we'll create the model. We'll move the, the whole model. We are talking about the ML ops teams here, right? And they have the whole functionality that, that they built ind over the year. So essentially they want to become the platform that I, when you come in, I'm the only platform you would use from the model training to deployment to inference, to model versioning to management, the old s and that's angle they're trying to take. So it's, it's a one source platform. >>What about this idea of technical debt? Adrian Carro was on yesterday. John, I know you talked to him as well. He said, look, Amazon's Legos, you wanna buy a toy for Christmas, you can go out and buy a toy or do you wanna build a, to, if you buy a toy in a couple years, you could break and what are you gonna do? You're gonna throw it out. But if you, if you, if part of your Lego needs to be extended, you extend it. So, you know, George Gilbert was saying, well, there's a lot of technical debt. Adrian was countering that. Does Amazon have technical debt or is that Lego blocks analogy the right one? >>Well, I talked to him about the debt and one of the things we talked about was what do you optimize for E two APIs or Kubernetes APIs? It depends on what team you're on. If you're on the runtime gene, you're gonna optimize for Kubernetes, but E two is the resources you want to use. So I think the idea of the 15 years of technical debt, I, I don't believe that. I think the APIs are still hardened. The issue that he brings up that I think is relevant is it's an end situation, not an or. You can have the bag of Legos, which is the primitives and build a durable application platform, monitor it, customize it, work with it, build it. It's harder, but the outcome is durability and sustainability. Building a toy, having a toy with those Legos glued together for you, you can get the play with, but it'll break over time. Then you gotta replace it. So there's gonna be a toy business and there's gonna be a Legos business. Make your own. >>So who, who are the toys in ai? >>Well, out of >>The box and who's outta Legos? >>The, so you asking about what what toys Amazon building >>Or, yeah, I mean Amazon clearly is Lego blocks. >>If people gonna have out the box, >>What about Google? What about Microsoft? Are they basically more, more building toys, more solutions? >>So Google is more of, you know, building solutions angle like, you know, I give you an API kind of thing. But, but if it comes to vertical industry solutions, Microsoft is, is is ahead, right? Because they have, they have had years of indu industry experience. I mean there are other smaller cloud are trying to do that too. IBM being an example, but you know, the, now they are starting to go after the specific industry use cases. They think that through, for example, you know the medical one we talked about, right? So they want to build the, the health lake, security health lake that they're trying to build, which will HIPPA and it'll provide all the, the European regulations, the whole line yard, and it'll help you, you know, personalize things as you need as well. For example, you know, if you go for a certain treatment, it could analyze you based on your genome profile saying that, you know, the treatment for this particular person has to be individualized this way, but doing that requires a anomalous power, right? So if you do applications like that, you could bring in a lot of the, whether healthcare, finance or what have you, and then easy for them to use. >>What's the biggest mistake customers make when it comes to machine intelligence, ai, machine learning, >>So many things, right? I could start out with even the, the model. Basically when you build a model, you, you should be able to figure out how long that model is effective. Because as good as creating a model and, and going to the business and doing things the right way, there are people that they leave the model much longer than it's needed. It's hurting your business more than it is, you know, it could be things like that. Or you are, you are not building a responsibly or later things. You are, you are having a bias and you model and are so many issues. I, I don't know if I can pinpoint one, but there are many, many issues. Responsible ai, ethical ai. All >>Right, well, we'll leave it there. You're watching the cube, the leader in high tech coverage here at J three at reinvent. I'm Jeff, Dave Ante. Andy joining us here for the critical analysis and breaking down the commentary. We'll be right back with more coverage after this short break.

>>The Cube presents UI Path Forward five. Brought to you by UI Path. >>We're back the cube's coverage of UI path forward. Five. And we're live. Dave Velante with Dave Nicholson. AJ Gupta is here. He's the Chief Digital Transformation Officer at the Motor Vehicles of California dmv. Welcome Jay. Good to see you. >>Thank you. >>Good to see you. Wow, you, you have an interesting job. I would just say, you know, I've been to going to conferences for a long time. I remember early last decade, Frank Sluman put up a slide. People ho hanging out, waiting outside the California dmv. You were the butt of many jokes, but we have a happy customer here, so we're gonna get it to your taste >>Of it. Yeah, very happy >>Customer, obviously transform the organization. I think it's pretty clear from our conversations that that automation has played a role in that. But first of all, tell us about yourself, your role and what's going on at the dmv. >>Sure. Myself, a j Gupta, I am the Chief Digital Transformation Officer at the dmv. Somewhat of i, one would say a made up title, but Governor's office asked me, Okay, we need help. And that's what >>Your title though? >>Yeah, yeah. So I'm like, well we are doing business and technology transformation. So that's, that's what I've been doing for the last three years at the dmv. Before that I was in private sector for 25 years, decided first time to give back cuz I was mostly doing public sector consulting. So here I am. >>Okay. So you knew the industry and that's cool that you wanted to give back because I mean obviously you just, in talking off camera, you're smart, you're very cogent and you know, a lot of times people in the private sector, they don't want to go work in the, in the public sector unless they're, unless they're power crazy, you know? Anyway, so speaking with David Nicholson, the experience has gone from really crappy to really great. I mean, take >>It from here. Yeah. Well, am I gonna be, I'm, because I'm from California, I was just, I was just, you know, we >>Got a dual case study >>Eloquently about, about the, the, the change that's happened just in, just in terms of simple things like a registration renewal. It used to be go online and pray and weed through things and now it's very simple, very, very fast. Tell us more about, about some of the things that you've done in the area of automation that have increased the percentage of things that could be done online without visiting a field office. Just as an >>Example. Yeah, what's the story? >>Yeah, so first of all, thank you for saying nice things about dmv, you as a customer. It means a lot because we have been very deliberately working towards solving all customer po pain points, whether it's in person experiences, online call centers, kiosks, so all across the channels. So we started our journey, myself and director Steve Gordon about three years ago, almost at the same time with the goal of making Department of Mo no motor vehicles in California as the best retail experience in the nation across industries. So that's our goal, right? Not there yet, but we are working towards it. So for, for our in person channels, which is what you may be familiar with, first of all, we wanna make sure brick and click and call all the customer journeys can be done across the channels. You can decide to start journey at one place, finish at another place. >>All that is very deliberate. We are also trying to make sure you don't have to come to field office at all. We would welcome you to come, we love you, but we don't want you to be there. You have better things to do for the economy. We want you to do that instead of showing up in the field office, being in the weight line. So that's number one. Creating more digital channels has been the key. We have created virtual field office. That's something that you would become familiar with if you are not as a DMV customer. During Covid, the goal was we provide almost all the services. We connect our technicians to the customer who are in need of a live conversation or a email or a text or a, or a SMS conversation or chat conversation in multiple languages or a video call, right? >>So we were able to accomplish that while Covid was going on, while the riots were going on. Those of your, you know about that, we, our offices were shut down. We created this channel, which we are continuing because it's a great disaster recovery business continuity channel, but also it can help keep people away from field office during peak hours. So that's been very deliberate. We have also added additional online services using bots. So we have created these web and process bots that actually let you do the intake, right? You, we could set up a new service in less than four weeks, a brand new service online. We have set up a brand new IVR service on call centers in less than a month for our seniors who didn't want to come to the field office and they were required certain pieces of information and we were able to provide that for our customers by creating this channel in less than less than four. >>And the pandemic was an accelerant to this was, was it the catalyst really? And then you guys compressed it? Or were, had you already started on the >>Well, we were >>Ready. I mean you, but you came on right? Just about just before the pandemic. >>Yeah. Yeah. So I came on in 2019, pandemic started in 2020 early. So we got lucky a little bit because we had a head start at, I was already working with u UI paths and we had come up with design patterns that we gonna take this journey for all DMV channels with using UiPath. So it was about timing that when it happened, it accelerated the need and it accelerated the actual work. I was thinking, I'll have a one year plan. I executed all of the one year plan items in less than two months out of necessity. So it accelerated definitely the execution of my plan. >>So when you talk about the chat channel, is that bots, is that humans or a combination? Yeah, >>It's a, it's a combination of it. I would say more AI than bots. Bots to the service fulfillment. So there is the user interaction where you have, you're saying something, the, the chat answers those questions, but then if you want something, hey, I want my, my registration renewed, right? It would take you to the right channel. And this is something we do today on our IVR channel. If you call in the DMV number in California, you'll see that your registration renewal is all automatic. You also have a AI listening to it. But also when you are saying, Yep, I wanna do it, then bot triggers certain aspects of the service fulfillment because our legacy is still sitting about 60 years old and we are able to still provide this modern facade for our customers with no gap and as quickly as possible within a month's time. How >>Many DMVs are in the state? >>Okay, so we have 230 different field locations out of which 180 are available for general public services. >>Okay. So and then you're, you're creating a digital overlay that's right >>To all of >>That, right? >>Yeah, it's digital and virtual overlay, right? Digital is fully self-service. Bots can do all your processing automation, can do all the processing. AI can do all the processing, but then you have virtual channels where you have customer interacting with the technicians or technicians virtually. But once a technician is done solving the problem, they click a button and bot does rest of the work for the technician. So that's where we are able to get some back office efficiency and transaction reduction. >>When was the last time you walked into a bank? >>Oh man. >>I mean, is that where we're going here where you just don't have to >>Go into the branch and that is the goal. In fact, we already have a starting point. I mean, just like you have ATM machines, we have kiosks already that do some of this automation work for us today. The goal is to not have to have to, unless you really want to, We actually set up these personas. One of them was high touch Henry. He likes to go to the field office and talk to people. We are there for them. But for the millennials, for the people who are like, I don't have time. I wanna like quickly finish this work off hours 24 by seven, which is where bots come in. They do not have weekends, HR complaint, they don't have overtime. They're able to solve these problems for me, 24 >>By seven. And what's the scope of your, like how many automations, how many bots? Can you give us a sense? >>Sure. So right now we are sitting at 36 different use cases. We have collected six point of eight point, well, we have saved 8.8 million just using the bots overall savings. If you were to look at virtual field office, which bots are part of, we have collected 388 million so far in that particular channel bots. I've also saved paper. I've saved a million sheets of paper through the bot, which I'm trying to remember how many trees it equates to, but it's a whole lot of trees that I've saved. And >>How many bots are we talking about? >>So it's 36 different use cases. So 36 >>Bots? >>Well, no, there's more bots I wanna say. So we are running at 85% efficiency, 50 bots. Oh wow. Yeah. >>Wow. Okay. So you, you asked the question about, you know, when was the last time someone was in a bank? The last time I was in a bank it was to deposit, you know, more than $10,000 in cash because of a cash transaction. Someone bought a car from me. It was more of a nuisance. I felt like I was being treated like a criminal. I was very clear what I was doing. I had just paid off a loan with that bank and I was giving them the cash for that transaction as opposed to the DMV transaction transferring title. That was easy. The DMV part was easier than the bank. And you're trying to make it even easier and it shouldn't, it shouldn't be that way. Yes. Right. But, but I, I have a, I have a question for you on, on that bot implementation. Can you give us, you've sort of give it us examples of how they interact. Yeah. But as your kind of prototypical California driver's license holder, how has that improved a specific transaction that I would be involved with? Can >>You, so well you as a Californian and you as a taxpayer, you as a Californian getting services and you as a taxpayer getting the most out of the money Okay. That the DMV spending on providing services, Right. Both are benefits to you. Sure. So bots have benefited in both of those areas. If you were used to the DMV three years ago, there was a whole lot of paper involved. You gotta fill this form out, you gotta fill this other form out and you gotta go to dmv. Oh by the way, your form, you didn't bring this thing with you. Your form has issues. We are calculated that about 30% of paper workloads are wasted because they just have bad data, right? There is no control. There's nobody telling you, hey, do this. Right. Even dates could be wrong, names could be wrong fields, maybe incomplete and such. >>So we were able to automate a whole lot of that by creating self-service channels, which are accelerated by bot. So we have these web acceleration platforms that collect the data, bots do the validation, they also verify the information, give you real time feedback or near real time feedback that hey, this is what you need to change. This is when you need to verify. So all the business rules are in the bot. And then once you're done, it'll commit the information to our legacy systems, which wouldn't have been possible unless a technician was punching it in manually. So there is a third cohort of Californians, which is our employees. We have 10,000 of those. They, I don't want them to get carpal tunnel. I want them to make sure they're spending more time thinking and helping our customers, looking at the customers rather than typing things. And that's what we are able to accomplish with the bots where you press that one button, which will have required maybe 50 more keystrokes and that's gone. And now you're saving time, you're also saving the effort and the attention loss of serving the best. >>Jay, what does it take to get a new process on board? So I'm thinking about real id, I just went through that in Massachusetts. I took, it was gonna be months to get to the dmv. So I ended up going through a aaa, had to get all these documents, I uploaded all the documents. Of course when I showed up, none were there. Thankfully I had backup copies. But it was really a pleasant experience. Are you, describe what you're doing with real ID and what role bots play? >>Yeah, sure. So with real id, what we are doing today and what I, what we'll be doing in the future, so I can talk about both. What we are doing today is that we are aligning most of the work to be done upfront by the customer. Because real ID is a complex transaction. You've gotta have four different pieces of documentation. You need to provide your information, it needs to match our records. And then you show up to the field office. And by the way, oh man, I did not upload this information. We are getting about 15 to 17% returns customers. And that's a whole lot of time. Every single mile our customer travels to the DMV office, which averages to about 13 miles. In my calculation for average customer, it's a dollar spent in carbon footprint in the time lost in the technician time trying to triage out some other things. So you're talking $26 per visit to the economy. >>Yeah. An amazing frustration, Yes. >>That has to come back and, and our customer satisfaction scores, which we really like to track, goes down right away. So in general, for real, id, what we have been, what we have done is created bunch of self-service channels, which are accelerated by workflow engines, by AI and by bots to collect the documentation, verify the documentation against external systems because we actually connect with Department of Homeland Security verify, you know, what's your passport about? We look at your picture and we verify that yep, it is truly a passport and yours and not your wives. Right? Or not a picture of a dog. And it's actually truly you, right? I mean, people do all kind of fun stuff by mistake or intentionally. So we wanna make sure we save time for our customer, we save time for our, for our employees, and we have zero returns required when employees, where customer shows up, which by the way is requirement right now. But the Department of Homeland Security is in a rule making process. And we are hopeful, very hopeful at this point in time that we'll be able to take the entire experience and get it done from home. And that'll give us a whole lot more efficiency, as you can imagine. And bots are at the tail end of it, committing all the data and transactions into our systems faster and with more accuracy. >>That's a great story. I mean, really congratulations and, and I guess I'll leave it. Last question is, where do you want to take this? What's the, what's your roadmap look like? What's your runway look like? Is it, is there endless opportunities to automate at the state or do you see a sort of light at the end of the tunnel? >>Sure. So there is a thing I shared in the previous session that I was in, which is be modern while we modernize. So that's been the goal with the bot. They are integral part of my transition architecture as I modernize the entire dmv, bring them from 90 60, bringing us from 1960 to 2022 or even 2025 and do it now, right? So bots are able to get me to a place where customers expectations are managed. They are getting their online, they're getting their mobile experience, they are avoiding making field off his trips and avoiding any kind of paper based processing right? For our employees and customers as well. So bots are serving that need today as part of the transition strategy going from 1960 to 2022 in the future. They're continue gonna continue to service. I think it's one thing that was talked about by the previous sessions today that we, they, they're looking at empowering the employees to do their own work back office work also in a full automation way and self-power them to automate their own processes. So that's one of the strategies we're gonna look for. But also we'll continue to have a strategy where we need to remain nimble with upcoming needs and have a faster go to market market plan using the bot. >>Outstanding. Well thanks so much for sharing your, your story and, and thanks for helping Dave. >>Real life testimony. I never, never thought I'd be coming on to praise the California dmv. Here I am and it's legit. Yeah, >>Well done. Can I, can I make an introduction to our Massachusetts colleagues? >>Good to, well actually we have, we have been working with state of New York, Massachusetts, Nevara, Arizona. So goal is to share but also learn from >>That. Help us out, help us out. >>But nice to be here, >>Great >>To have you and looking for feedback next time you was at dmv. >>All right. Oh, absolutely. Yeah. Get that, fill out that NPS score. All right. Thank you for watching. This is Dave Valante for Dave Nicholson. Forward five UI customer conference from the Venetian in Las Vegas. We'll be right back.

>>Hey everyone. Welcome back to Las Vegas. This is the Cube's live coverage of day. One of snowflake summit 22 fourth annual. We're very happy to be here. A lot of people here, Lisa Martin with Dave Valante, David's always great to be at these events with you, but me. This one is shot out of the cannon from day one, data, data, data, data. That's what you heard of here. First, we have two guests joining us next, please. Welcome Matthew Scalian. Who's an alumni of the cube CEO and founder of Matillion and Jer staying chief data architect and global head of data engineering from Western union. Welcome gentlemen. Thank >>You. Great to be here. >>We're gonna unpack the Western union story in a second. I love that, but Matthew, I wanted to start with you, give the audience who might not be familiar with Matillion an overview, your vision, your differentiators, your joint value statement with snowflake, >>Of course. Well, first of all, thank you for having me on the cube. Again, Matillion S mission is to make the world's data useful, and we do that by providing a technology platform that allows our customers to load transform, synchronize, and orchestrate data on the snowflake data cloud. And on, on the cloud in general, we've been doing that for a number of years. We're co headquartered in the UK and the us, hence my dat accents. And we work with all sorts of companies, commercial scale, large end enterprises, particularly including of course, I'm delighted to say our friends at Western union. So that's why we're here today. >>And we're gonna talk about that in a second, but I wanna understand what's new with the data integration platform from Matillion perspective, lots of stuff coming out, give us an overview. >>Yeah, of course, it's been a really busy year and it's great to be here at snowflake summit to be able to share some of what we've been working on. You know, the Matillion platform is all about making our customers as productive as possible in terms of time to value insight on that analytics, data science, AI projects, like get you to value faster. And so the more technology we can put in the platform and the easier we can make it to use, the better we can achieve that goal. So this year we've, we've shipped a product that we call MDL 2.0, that's enterprise focused, exquisitely, easy to use batch data pipelines. So customers can load data even more simply into the snowflake data cloud, very excitingly we've also launched Matillion CDC. And so this is an industry first cloud native writer, head log based change data capture. >>I haven't come up with a shorter way of saying that, but, and surprise customers need this technology and it's been around for years, but mostly pre-cloud technology. That's been repurposed for the cloud. And so Matillion has rebuilt that concept for the cloud. And we launched that earlier this year. And of course we've continued to build out the core Matillion ETL platform that today over a thousand joint snowflake Matillion customers use, including Western union, of course we've been adding features there such as universal connectivity. And so a challenge that all data integration vendors have is having the right connectors for their source systems. Universal connectivity allows you to connect to any source system without writing code point and click. We shape that as well. So it's been a busy year, >>Was really simple. Sorry. I love that. He said that and it also sounded great with your accent. I didn't wanna >>Thank you. Excellent. Javier, talk about your role at Western union in, in what you've seen in terms of the evolution of the, the data stack. >>So in the last few years, well, a little bit of Western union, a 70 or 170 year old company, pretty much everybody knows what Western union is, right? Driving an interesting synergy from what Matthew says, when data moves money moves, that's what we do when he moves the da, he moves the data. We move the money. That's the synergy between, you know, us and the organization that support us from data move perspective. So what I've seen in the last few years is obviously a shift towards the cloud, but, you know, within the cloud itself, obviously there's a lot of players as well. And we as customers have always been wishing to have a short, smaller footprint of data so that the movement becomes a little lesser. You know, interestingly enough, in this conference, I've heard some very interesting stuff, which kind of helping me to bring that footprint down to a manageable number, to be more governed, to be more, you know, effective in terms of delivering more end results for my customers as well. >>So Matillion has been a great partner for us from our cloud adoption perspective. During the COVID times, we were a re we are a, you know, multi-channel organization. We have retail stores as well, our digital presence, but people just couldn't go to the retail stores. So we had to find ways to accelerate our adoption, make sure our systems are scaling and making sure that we are delivering the same experience to our customers. And that's where, you know, tools like Matillion came in and really, really partnered up with us to kind of bring it up to the level. >>So talk specifically about the stack evolution. Cause I have this sort of theory that everybody talks about injecting data and, and machine intelligence and AI and machine learning into apps. But the application development stack is like totally separate from the, the data analytics and the data pipeline stack. And the database is somewhere over here as well. How is that evolving? Are those worlds coming together? >>Some part of those words are coming together, but where I still see the difference is your heavy lifting will still happen on the data stack. You cannot have that heavy lifting on the app because if once the apps becomes heavy, you'll have trouble communicating with, with, with the organizations. You know, you need to be as lean as possible in the front end and make sure things are curated. Things are available on demand as soon as possible. And that's why you see all these API driven applications are doing really, really well because they're delivering those results back to the, the leaner applications much faster. So I'm a big proponent of, yes, it can be hybrid, but the majority of the heavy lifting still needs to happen down at the data layer, which is where I think snowflake plays a really good role >>In APIs are the connective tissue >>APIs connections. Yes. >>Also I think, you know, in terms of the, the data stack, there's another parallel that you can draw from applications, right? So technology is when they're new, we tend to do things in a granular way. We write a lot of code. We do a lot of sticking of things together with plasters and sticky tape. And it's the purview of high end engineers and people enthusiastic about that to get started. Then the business starts to see the value in this stuff, and we need to move a lot faster. And technology solutions come in and this is what the, the data cloud is all about, right? The technology getting out of the way and allowing people to focus on higher order problems of innovating around analytics, data applications, AI, machine learning, you know, that's also where Matillion sit as well as other companies in this modern enterprise data stack is technology vendors are coming in allowing organizations to move faster and have high levels of productivity. So I think that's a good parallel to application development. >>And's just follow up on that. When you think about data prep and you know, all the focus on data quality, you've got a data team, you know, in the data pipeline, a very specialized, maybe even hyper specialized data engineers, quality engineers, data, quality engineers, data analysts, data scientist, but they, and they serve a lot of different business lines. They don't necessarily have the business, they don't have the business context typically. So it's kind of this back and forth. Do you see that changing in your organization or, or the are the lines of business taking more responsibility for the data and, and addressing that problem? It's, >>It's like you die by thousand paper cuts or you just die. Right? That's the kind >>Of, right, >>Because if I say it's, it's good to be federated, it comes with its own flaws. But if I say, if it's good to be decentralized, then I'm the, the guy to choke, right? And in my role, I'm the guy to choke. So I've selectively tried to be a pseudo federated organization, where do I do have folks reporting into our organization, but they sit close to the line of business because the business understands data better. We are working with them hand in glove. We have dedicated teams that support them. And our problem is we are also regional. We are 200 countries. So the regional needs are very different than our us needs. Majority of the organizations that you probably end up talking to have like very us focused, 50 per more than 50% of our revenue is international. So we do, we are dealing with people who are international, their needs for data, their needs for quality and their needs for the, the delivery of those analytics and the data is completely different. And so we have to be a little bit more closer to the business than traditionally. Some, some organizations feel that they need >>To, is there need for the underlying infrastructure and the operational details that as diverse, or is that something that you bring standardization to the, >>So the best part about this, the cloud that happened to us is exactly that, because at one point of time, I had infrastructure in one country. I had another infrastructure sitting in another country, regional teams, making different different decisions of bringing in different tools. Now I can standardize. I will say, Matillion is our standard for doing ETL work. If this is the use case, but then it gets deployed across the geographies because the cloud helps us or the cloud platform helps us to manage it. Sitting down here. I have three centers around the world, you know, Costa Rica, India, and the us. I can manage 24 7 sitting here. No >>Problem. So the underlying our infrastructure is, is global, but the data needs are dealt with locally. Yep. >>One of the pav question, I was just thinking JVE is super well positioned funds for you, which is around that business domain knowledge versus technical expertise. Cause again, early in technology journeys tend, things tend to be very technical and therefore only high end engineers can do it, but high end engineers are scar. Right? Right. And, and also, I mean, we survey our hundreds of large enterprise customers and they tell us they spend two thirds of their time doing stuff they don't really want to do like reinventing the wheel, basic data movement and the low order staff. And so if you can make those people more productive and allow them to focus on higher value problems, but also bring pseudo technical people into it. Overall, the business can go a lot faster. And the way you do that is by making it easier. That's why Matillion is a low code NOCO platform, but Jer and Western union are doing this right. I >>Mean, I can't compete with AWS and Google to hire people. So I need to find people who are smart to figure the products that we have to make them work. I don't want them to spend time on infrastructure, Adam, I don't want them to spend time on trying to manage platforms. I want them to deliver the data, deliver the results to the business so that they can build and serve their customers better. So it's a little bit of a different approach, different mindset. I used to be in consulting for 17 years. I thought I knew it all, but it changed overnight when I own all of these systems. And I'm like, I need to be a little bit more smarter than this. I need to be more proactive and figure out what my business needs rather than what just from a technology needs. It's more what the business needs and how I can deliver that needs to them. So simple analogy, you know, I can build the best architecture in the world. It's gonna cost me an arm and leg, but I can't drive it because the pipeline is not there. So I can have a Ferrari, but I can't drive it. It's still capped at 80, 80 miles an hour. So rather than spend, rather than building one Ferrari, let me have 10 Toyotas or 10 Fs, which will go further along and do better for my cus my, for my customers. >>So how do you see this whole, we hearing about the data cloud. We hear about the marketplace, data products now, application development inside the data cloud. How do you see that affecting not so much the productivity of the data teams. I don't wanna necessarily say, but the product, the value that, that customers like you can get out >>Data. So data is moving closer to the business. That's the value I see, because you are injecting the business and you're injecting the application much more closer to the data because it, in the past, it was days and days of, you know, churn the data to actually clear results. Now the data has moved much closer. So I have a much faster turnaround time. The business can adapt and actually react much, much faster. It took us like 16 to 30 days to deliver, you know, data for marketing. Now I can turn it down in four hours. If I see something happening, I'll give you an example. The war in Ukraine happened. Let is shut down operations in Russia. Ukraine is cash swamp. There's no cash in Ukraine. We have cash. We roll out campaign, $0 money, transferred to Ukraine within four hours of the world going on. That's the impact that we have >>Massive impact. That's huge, especially with such a macro challenge going on, on the, in, in the world. Thank you so much for sharing the Matillion snowflake partnership story, how it's helping Western union really transform into a data company. We love hearing stories of organizations that are 170 years old that have always really been technology focused, but to see it come to life so quickly is pretty powerful. Guys. Thank you so much for your time. Thanks >>Guys. Thank you, having it. Thank >>You >>For Dave Velante and our guests. I'm Lisa Martin. You're watching the cubes live coverage of snowflake summit 22 live from Las Vegas. Stick around. We'll be back after a short break.

(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)

>>Hey everyone. Welcome to the cubes coverage of PagerDuty summit 22. I'm Lisa Martin. I'm here with one of our alumni. Jonathan Ren 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. >>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 are 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. Uh, 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 a PagerDuty to new audiences. So we've been a big part of the back office for a long time with SREs and developers and it ops. And we've really come to realize that, you know, the front office is so important. And one of the, the leading departments there that we can make an impact and extend into with our solution is customer service. >>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 patients patients when you're a consumer patients, when you're a business person. And so the, the, the voice of the customer being able to get things escalated quickly and resolve 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 you're 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. >>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 it, it's very, very hard for a lot of organizations and nobody's exempt from it. Um, the connection between the front office and the back office, there is no real gold standard for that. And, and, and so like, is there, 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. Um, there was another statistic that, uh, we, 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. >>What are some of the, well, the, the LA 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 gonna help, not just the front office back office kind of blurred, um, 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, the trick, the, 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, um, CSAT scores, NPS scores, brand reputation, reducing churn, >>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, uh, 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 Jo you know, uh, customer satisfaction areas. You can turn those bad experiences into really good ones. >>Definitely. And I think we all, we all have that expectation that that's gonna happen when things do when outages do happen, cuz to your point that's, those are the things that's not, is it gonna happen? It's when and how quickly can we recover from that? So it's, we minimize the impact on everybody else. Couple 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? >>Yeah, so we have several partners in the front office and one of, one of the biggest, uh, 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, uh, 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, uh, 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 direct, uh, 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. >>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, uh, we've seen situations where the back office team working on an incident, uh, 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 >>Without that visibility so much can suffer. And, and quickly, we also had this expectation. I, I mentioned one of the things that was in short supply in the pandemic as patients and tolerance. But another thing is we expect things in real time, real time, access to data, real time 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, the 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 in 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. Yeah. 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 CSOPs 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. 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, uh, 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, uh, 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 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, um, 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, and do more faster and make such a huge difference there. Talk a little bit about the, the DevOps CS ops relationship. You know, one of the, one of the things that's kind of ironic is here we are in, 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, and a vendor or a solution provider, or it can really facilitate trust. And that was a big theme of, uh, the keynote this morning is that trust. But talk about the trust that is you PagerDuty really things essential between the DevOps folks and the CS ops folks. >>Yeah. It's, it's, it's critical. As I kind of mentioned before, there really isn't a golden path, a golden connection, uh, 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, it's almost like when I was a little kid, I lived nearby a, um, 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. That's essentially customer service sitting there looking at the game happening constantly, like trying to interrupt the teams and saying, Hey, what about us? Like, 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 knothole at the back of the center field. >>Well, you gotta tell, did you ever get tickets? Can you go to pro games now? >>Uh, no. No. Oh, still waiting. >>Oh man. Talk to me last question here. I asked you before we, we started filming if you had a crystal ball or, or a magic eight ball. So next time at least bring me a magic eight ball. What are some of the predictions that you have is as you see where we are in now, half of calendar, 22, almost gone. The announcements coming from PagerDuty today, the synergy is between PagerDuty. It's what 21,000 plus customers, your partners. What are some of the things that you're excited about that are coming? >>So a couple things. One is, I, 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 it ops and the SRE teams in the back office back offices that have to deal with interrupted, um, real time work, but it's other parts of the organization as well, um, that have to get proactive versus reactive. And the first of those that the, 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 like real time 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, uh, 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 that the trust built between an empowered, proactive CS agent and a customer on the other end that there's the sky is the limit on that one? >>Uh, if I'm a platinum customer or I'm a silver customer on 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 right. Proactively and I'm hearing about issues long before I see them. That to me is building trust. >>Absolutely. Jonathan, thank you so much for joining me on the cube 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, to see your predictions come true. <laugh> thanks for your time. >>Likewise, Lisa, thank you very much. >>My pleasure for Jonathan Ren. I'm Lisa Martin covering the cube on the ground at PagerDuty summit 22, stick around of your rack back with my next guest.

>>And welcome to this special cube conversation. I'm John for a, your host of the cube. We're here in Palo Alto, California, and I'm here with a very special guest coming down from Seattle remotely into the cube studios is the leader at AWS Amazon web services, the vice president of database analytics and machine learning Swami. Great to see you cube alumni recently taking over the database business at AWS as a leader. Congratulations. And thanks for coming on the cube. >>Hey, my pleasure to be here, John, very excited to talk to you. >>Yeah. We've had many conversations on the cube and also in person and also online around all the major mega trends. You've had your hand in all the action, going back to your days when you were in school learning and, and writing papers. And 10 years ago, Amazon web services launched AWS dynamo, DB, fast, flexible, no SQL database that everyone loves today, which has inspired a generation of what I would call database distributing cloud scale, single digit millisecond performance at scale. And again, the key scale. And again, this is 10 years ago, so it seems like yesterday, but you guys are celebrating and your name was on the original paper with CTO Verner. Vogel's your celebrity. Congratulations. >>Thank you. Not sure about the celebrating part, but I'm very excited. At least I played a hand in building such an amazing technology that has enabled so many amazing customers along the way as well. So >>Trivia on the, on the paper as you were an intern at AWS, so you're getting your PhD. And then since, since rising through the ranks and involved in a lot of products over the years, and then leading the machine learning and AI, which is now changing the game at the industry level, but I got to ask you getting back to the story here. A lot of customers have built amazing things on top of dynamo DB, not to mention lots of other AWS and Amazon tech riding on it. Can you share some of the highlights that came out of the original paper? And so with some examples, because I think this is a point in time, 10 years ago, where you start to, so the KickUp of cloud scale, not just, just for developers and building startups, you're really starting to see the scale rise. >>Yeah, I actually, I mean, as you probably know, based on what he read to explain the Genesis of dynamo DB itself had to explain the Genesis of how Amazon got into building the original dynamo, right? And this was during the time when miner, I joined Ron esteem as an intern and, and Amazon was one of the pioneers in pushing the boundary of scale. And a year over year, our Q4 holiday season tends to be really, really bad for all the right reasons. We all want our holiday shopping done during that time. And you want to be able to scale your website, arters fulfillment centers, all of them at that time. And those are the times around 2005. And the answer is when people think our database, they think of a single database server that actually runs on a box and has a certain characteristics and does a scale and availability and whatnot. >>And it's usually relational. And then when we had a major disruption during Q4 that's when yeah, ask ourselves the question, why are we actually using a relational database for some of these things when they really didn't need the data model complexity of relational database. And normally I would say most companies where to actually ask an intern or a few engineers who are early in the career saying like, what the hell are you suggesting? Just go away. But Amazon being enabling Buddhists to build what they want. And they actually let us start reimagining what a database or our scale could look like. And that led to dynamo. And since she unstained mine, then we migrated from an traditional relational database stair this one for some of the amazon.com services. And then I moved on to actually start building some butts off our storage service and then our managed relational database service, I explicitly remember. >>And one of our customer advisory board, we're just the set off some of our leading customers who actually give us feedback on roadmap. Another son, Don, who's the CEO and chief geek of spunk bargain faker. And him actually looking at the Trinity me, I was starting in the corner and saying like you all, both tomorrow and why do I need to keep shotting my, my sequel database and reshooting assigned scaling. And this is the time when the state of the art in most databases were around. Like, you start sharding your relational database and constantly reshaping. And this is when most websites are starting to experience the kind of scale which we consider a normal month. During those times it was mostly, most companies used to have a single relational database backend and start scaling that way. And that conversation led entirely under duress, unaided read, lot of AWS leaders and myself saying like, Hey, what is a cloud database reimagined without the hampering SQL look like? And that led us to start building dynamo DB, but just a key value database at that time. Now we support document might've too, but that single digit millisecond latency at any scale imagine. So >>I think about that time at that time, 10 years ago, when you were having this conversation and I know the smug mug and I, he said, he's in totally geek and he's, he's good to point that out. You also have Netflix as customers too. I'd like to hear how that's evolved, but, but I think back at the time, if you look back then I got to ask you most people we've talked about this before. No one database rules, a world that's now standard people now don't see one database back then it was a one database kind of mindset back then. Yeah. And then you had that big data movement happening with Hadoop. You had the object store developing. So you're in you're you're circling around that area. What was it like then? I mean, take, take us through that because there was obvious visibility that, Hey, let's just store this. Now you see data lakes and that's all happening. But back then object store was kind of new. Yeah. >>Ah, it's a great question. Now, one of the things I realized early on, especially when I was working with binary, when you're saying amazon.com itself as an example, that the access patterns for various applications and Amazon, but let alone AWS customers tend to be very, very, very, some of them really just needed an object store. Some of them needed a relational database. Some of them really wanted a key value store within a fast latency. Some of them really needed a durable cash. And, but it so happens when you have a giant hammer. You use that for everything looks like a map, which is essentially the story at that time. And so everyone kept using the same database, irrespective of what the problem was because nobody else, I mean, thought about like, what else can we build that is better? So this let us do, literally I remember writing a paper with Bernard internally that is widely used in Amazon explaining what are all the menu of booklets that access. >>And then how do we go about actually solving for each of these things so that they can actually grow and innovate faster. And, and this was led to actually the Genesis of not only building IDs and so forth, but also dynamo and various other non-relational data. There's a still let alone not so storage access patterns and what not. So, and this was one of the big revelations he had just that there is not a single database that is going to meet the customer, needs us. The diversity of workloads in the internet is growing. And this was a key pivotal moment because with cloud now applications can scale very more instantly than before now. Building an application for Superbowl is very easier than before. That means that on, I mean, everybody is pushing the boundaries of what scale means, and they are expecting more from their obligations. That's when you need technologies like dynamo, DB, and that's exactly what dynamo already be set out to do. And since then, we are continuing to innovate on behalf of our customers and the purpose of the database story as well. And this concept has resonated well across the board. If you see that the database industry has also embraced this method, >>It's natural that you obviously evolved into the machine learning side of it because that's data is big part of that. And you see back then you, you bringing up kind of like flashes for me where it's like those, the data conversations back then and the data movement was just beginning. So the idea that you can have diversity in access methods of the kind of databases was a use case driven by the application, not so much database saying, this is how you have to work, that the script was flipped. It it's changed from infrastructure dictating to the applications, what to do. Now, the applications are going to the infrastructure and saying, give me what I want. I want to access something here in an office store, something here in no SQL that became the Genesis of infrastructure as code at a, at a global level. And so your paper kind of set the, the, the wave, the influence for this, no SQL did big data movement. It's created tons of value, maybe a third Mongo might've been influenced by this other people have been influenced. Can you share some stories of how people adopted the concept of dynamo DB and how that's changed in the industry and how has that helped the industry evolve? >>I mean, plus file data. Most share our experience of building and dynamo style data store. Very, it is a non-relational API and showing what are some of the experiences that the Venter in building such an paper and these set out early on itself, that it is should not be just a design paper, but it should be something that we shared our experiences. So even now, when I talked to my friends and colleagues and various other companies, one thing they always tell me is they appreciated the openness with which we were sharing. Some of the examples and learnings that we learned to not optimizing for percentile latencies, and what are some of the scalability challenges, how we solved and some of the techniques around things like sloppy Cora or various other stuff. We invented a lot of towns along the way too, but people really appreciated several of some of our findings and as talking about it. >>And since then I met so many other innovations are happening in the industry and the AWS, but also across the entire academia and industry in this space, the databases I've been going through what I call as a period of Renaissance, where one of the things, if you see our own arc, when Roger and I started on the database, front Disney started over the promo saying like, if you were to build a database where cloud is the new normal, this is again in 2008, we asked ourselves that question and what the belt that led us to start building things like dynamo, DB, RDS star. I know that alone, we reimagined data viruses with Redshift and several, and then several other databases like time stream for time series workloads started running Neptune for graph and whatnot. But at the moment we started actually asking that question and working backwards from customers. Then you will start being able to innovate accordingly. And this has worked really well. Then more than a hundred thousand AWS customers have chosen dynamo DB for mobile gaming tech IOT. Many of these are fast growing businesses, such as ledge, Darryl BNB, red fan, as soon as enterprises like Samsung Toyota, capital one and so far. So these are like really some meaningful clouds, let alone amazon.com. I run this. >>We have an internal customer is always good to have that entire inside customer. You know, I really find this a really profound use case because you're just talking, you know, in Amazonian terms, I'll just translate for the audience working backwards from the customer, which is the customer obsession you guys have. So here's, what's going on off the way I see it. You got dynamo, DB, paper, you and Verner, and the team Paul was a great as a great video on your blog posts that goes into the, to the talk he gave at around that time, which is fun to watch if you look back, but you have a radical enabler here, that's disrupting and changing S3 RDS, Aurora. These are game-changing concepts inside the, the landscape of AWS at the same time, you're working backwards from the customer. So the question I have for you as a leader and as a builder, how did you balance the working backwards from the customer while bringing something brand new and radical at that time to the market? >>Yeah, this is one of the S I mean hardest things to be, as leaders need to balance on. If you see many times, then we actually worked backwards from customers. The literal later translated this, literally do what customers are asking for, which is true nine out of 10 times, but there is one or a 10 times, you got to read between the lines on what they are asking. Because many times customers when are articulate that they need to go fast. If in the right way, they might say, Hey, I wish my heart storage goes faster, but they're not going to tell you they need a car, but you need to know and be able to translate and read between the lines we call it under the bucket of innovate on behalf of customers. And that is exactly the kind of a mantra we had when we were thinking about concepts like dynamo DB, because essentially at that time, almost everybody would, if I asked, they would just say, I wish a relational database could actually be able to scale from not just like a hundred gigabyte to one terabyte are, it can take up to like 2 million transactions, a second and so forth and still be cheap and made in reality as relational databases, the way they were engineered at that time, those are not going to meet the scale needs. >>So this is fair. We hunted read between the lines on what are some of the key Mustang needs from customers and then work backwards and then innovate on behalf of these workloads, be enabled by the sun oh four, which are some of the reasons that led to us launching some of the initial sets on dynamo on a single digit millisecond latency and seamless scale. At that time, databases didn't have the elasticity to go from like 10 requests, a second to like a hundred thousand or 1 million requests a second, and then scaled right back in an hour. So that was not possible. And we kind of enabled that. And that was an, a pretty big game changer that showed the elasticity of the cloud to a database. Well, >>Yeah, I think also just to, not to nerd out on this, but it enables a lot of other kind of cool scaled concepts, like queuing storage. It's all kind of together. This database piece of that you guys are solving. And again, props to you guys on the team. Congratulations. I have to ask, you know, more generally, how has your thinking changed since the paper? I'll see, you've got more experience under your belt. You don't yet have the gray hairs yet, but we'll see those soon come in, but you know, you're, you got a lot more experience. You're running teams, you're launching a lot of products. How has your thinking changed in the industry since the paper what's happening now? What's the big evolution. What are those new things now that are in the innovate on behalf of the customer? What's between the lines now, how do you see this happening? >>I mean, now since wanting dynamo via a victim, I had the opportunity to work on various problems in the big data space. There we've worked on some are fire things that you might be aware of in the analytics all the way from Redshift to quick side, too. Then I moved on to start some of our efforts, having built systems that enabled customer to store process and credit, and then analyze them. One of the realizations, I had this, the in around 2015 or 2016, I kinda had that machine learning was hitting a critical point where now it is ready for being scaled at option. Their cloud has basically enabled limitless compute and limitless storage, which are the factors that are holding back machine learning technology. Then I realized that now we have a unique opportunity to bring machine learning BI to everybody, not just folks with PhD in machine learning. >>And that's when I moved on from database and analytics areas, they started machine learning. We're just a descent area because machine learning is powered by data and then started building capabilities like SageMaker, which is our end to end ML platform to build, train and deploy them on models. And this, what does the leading enterprise platform by several gaggled users and then also a bunch of our AI services since then, I view the reason I'm giving all this historical context is one of the biggest realization I had early on itself. And 2016 as first machine learning is one of the most disruptive technologies. She will then country in our generation. This is right after cloud. I think these still are the most amazing combination that is going to revolutionize how we build applications and how we actually reason about that. Now, the second thing is that at the end of the day, when you look at the ANC and journey, it is not just about one database or one data Varroa. >>So one data lake product, or even 1:00 AM out platform. It is about the end to end journey where a customer is storing their order database. And then they are actually building a data lake that test customer history and order history. And they want to be able to personalize. And for their viewer experience are actually forecast what products to staff in their fulfillment center, but then all these things need to work and to handle. And that view is one of the big things that struck me for the past five years. And I've been on this journey in addition to building this Emma building blocks to connect the dots so that customers can go on this modern end to end data strategy as I call it, right. It goes beyond a single database technology or data technology, but putting now all of these end to end together so that customers don't end up spending six months connecting the dots, which has been the state of the down for the last couple of years. And we are bringing it down to matter of the Sundays. Now >>He's incredible Swami. Thank you so much for spending the time with us here in the, >>Yeah, my pleasure. Thanks again, Sean. Thanks for having me.

>>From the Bellagio hotel in Las Vegas, it's the cube covering UI path forward for brought to you by UI path. >>Welcome back to the queue. We are live at the Bellagio in Las Vegas, Lisa Martin, with Dave Volante UI path forward for it is so great to be sitting in an anchor desk next to Dave and with guests in person, we're going to be talking about automation workforce of the future. I've got two guests here with Dave and me. Kevin crone is here intelligent automation and digital upskilling leader from PWC. Kevin. Welcome. Thank you. Can't wait to talk about upskilling and Bettina Koblick is here the chief people officer at UiPath. Welcome to the program. Thanks for having me. So, as I understand that PWC has embedded UI path into a couple of product offerings. One of them we're going to talk about today, Kevin that's pro pro edge. Talk to me about that. What is that? >>So, um, as we look at, uh, challenges that C-suite leaders are facing, I think one of the biggest emerging challenges right now is around the topic of upscaling. There's the number of jobs that are being displaced, um, is growing by automation. But on the flip side, the number of jobs that are emerging is actually greater and there is a consistent challenge that gets cited there. All of our research through our CEO survey, their work we've done with the world economic forum and other sources around the need to fill that gap. And most leaders feel like they're not doing a good job to, to fill that. So, um, we at PWC have invested in developing a, a new software product called pro edge, which is really focused on, um, identifying the skills needed for the future, teaching those skills and helping to scale the usage of the skills and the organization. And one of the key skills as we look at the digital space is UI path. And we really think that, you know, teaching non technologists around the use of tools like robotic process automation is going to be one of those critical kind of must have skills in the future. >>And patina, you guys are using pro edge. Why, why, why? I mean, you're like the automation pros, what do you need? >>We need it because we have the same, um, challenges that every other company has that introduced us automation, right? People, um, people's time, their tasks that they have been doing are basically displaced. Um, and we're trying to figure out how do we up-skill re-skill how do we position people who now no longer are work working on maybe 50% of what they've done in the past for their next role? Um, so it's incredibly important for us, >>You know, you know this well, Tina and Kevin, you as well, when, when the automation trend RPA first hit, it was like, oh yeah, the trade press, come on. It's going to take away jobs, blah, blah, blah. Now we're working perpetual workdays, right? We all weekend all night, you never stopped working. You're always on. So I think people will brace automation, but as the chief people officer, first of all, how, how was it getting through the pandemic? And have you seen, I presume that UI path folks embrace automation, of course it's in your DNA, but have you seen others sort of, is that narrative done in, is COVID effected that >>I think COVID has affected it, um, for a number of reasons, because so many things shifted in how we do work. Number one, and I can talk about that a little more, but I yesterday I was in a customer advisory board meeting actually with Kevin. And the biggest conversation was not about the technology. It was around what happens when automation is introduced to a company and what happens with people, um, as to whether they want, they're willing to adopt, embrace, have an automation mindset. So that conversation isn't done at all. And it's probably one of the biggest conversations after, you know, adopting the technology, trying to introduce it as how do you drive adoption. And a lot of that is people's people's ability to understand how it will make their life easier, but then not be afraid about what's next. Uh, so I think it's absolutely still a conversation. I don't know if you feel the same. Yeah, >>Yeah. It it's been interesting. I think during the pandemic cause peoples, you know, day to day work lives have gone up ended and he start to think about, um, you know, the, the mixture between what I'll call kind of transaction oriented type work versus analytical type work. And if it just, you know, historically everyone's always said we should do less transaction work and more analytic work. But I think the pandemic was almost like forced that conversation on steroids and people's lot. You I've had to figure out that, like, I don't want to do this type of work and I'm being, I have more demands on me and I'm being asked to do other things, how can I do this more effectively? And so part of this becomes learning these skills to automate the things in front of you right now, the part of this becomes, I need to be able to, to actually have that analytical skill set in the future. And I think that is almost a precursor for what we see happening. And that was, that was the fun part of the conversation yesterday is thinking about, okay, well, what is the, you know, what is the, uh, the accounting analyst role five years from now and what someone does today versus what someone does in five years and how do you actually plan for that, >>The patina, where in your organization, like who's using it and talk to me about the adoption and their willingness to embrace it. >>Yeah. So we are, we've introduced it to our finance organization. One of the reasons we did that is because our finance organization is also a big user of automation, right? So, um, what's been really interesting is that because the technology or because pro edge kind of takes biases out of mapping, what a person can do, um, what learning paths there are for them and what their job will look like in the future, in which job do they go to or could be a potential path. I think it actually motivates people much beyond having work shifting because of automation. Because in addition, you also get to see a path, right. And everywhere you turn, just want to know what's the possibility for the future. So, um, while I'd like to say it was genius for us to envision that it's, it's a pleasant surprise and something, we should talk about more, >>I'm sensing a journey. It's always the case where I know I call it the force March to digital. We were thrown into this. And so, so much was unknown. And I know our team, I mean our producers and it's death by a thousand paper cuts in any one individual thing is not that bad, but yeah, the Moffitt, it's just, that's what kills your work day, your work week and your mental health. And so maybe it's, that's kind of the starting point is, are pigs a band-aid for, for, for, for that fundamental, but then it's wow. I can see the light bulb goes off. I can see the potential, that's where the digital upskilling comes in. I mean, maybe that's oversimplifying it, but how do you see that journey? Yeah. >>Yeah. I think there's a couple of different pieces with this cause, you know, and it goes back to the divide between the things you need to do now. And how do you think about making your life easier, but then it really goes to what you need to do in the future. And that journey to actually get there is tough because it's not just a question of, Hey, I need to pick up a textbook or pick up an online training module. And I'm just going to become an expert. It's really thinking about what are the, what's the combination of different skills. I need to learn. Some of that's going to be hands on technical skills. It's going to be platforms like UI path. It may be other complimentary platforms in the analytics space and other things. Um, some of this may also be on the kind of softer side. >>How do I learn how to work in a more agile way and have a design thinking mindset, have a product mindset, but then it's really how's that going to change my role in the future. And how do I actually, for lack of a better word, start to embed these practices in my job in a way that I'm actually learning these skills and it will stick. And how do you actually manage that co culture change? Um, for lack of better word over time. That was probably the biggest thing I picked up from yesterday was just some of this talk around change management and culture, uh, which is, you know, we, we have a lot of, for lack of better word techie. Cause if this conference would like to think about all the cool stuff that technology doing, but the big lesson learned is really, you know, you actually have to make the stick inside an organization. >>And in the last year, Kevin, I'm curious about the adoption because you know, everything we've seen so much acceleration in the last year that digital acceleration, the acceleration in automation, we've also seen tremendous, uh, people and from a cultural perspective, I'd love to, for you to shed some light on what you've seen since you've rolled this product out, how is the pandemic, has it been an enabler of that change management and that cultural change, which historically is very hard to do. >>It's very hard. And I think this, if, if I want to CFO or COO two years ago and talked about the skills gap in what was happening, the organization, I would probably get someone that would be on and say, okay, yeah, that, that, that is happening. We need to think about this, but I got 50 other things I need to worry about. You know, I think over the past year a while, things like, you know, TA TA, well, time is a big, uh, luxury or having excess time is a big luxury that most people don't have. I think there's a recognition that, um, it's a challenging work environment right now. We're trying to get more done. People are not in person. Um, people have, you know, there's issues with, with mental health and other challenges and there's, uh, almost like a renewed focus on how do we make employees lives better and better can mean different things. >>But when I think about it, it's, it's giving people a hope that they have a future career path, that their job is not going to be eliminated, that they're developing the right skills and they're being given the capacity to actually do that now. And so a lot of the discussions have really been are around that fact. And I would say probably more so than the traditional just cost saving discussion than most automation, um, threads in with RPA begin with this really, you know, became, um, we need to do this and we need to, you know, send a message to our employees that we really care about you. And this is something that is really going to be us investing in you as a perk in the future. >>What's the role of the head of human capital management in the context of automation? Is she the champion? Is she the therapist? Is she the change agent? Well, how do you see that? >>Well, clearly he should have been talking to the head of people, um, two years ago. We even, because, uh, the way I think about it, and I think a lot of people in my role think about it is, you know, a CFO really looks after the financial health of the company. Um, the focus for us is looking after the people, health of the company, right? And so I think, um, in my career, what I've learned is that change is constant. We all know this and, um, change for people is difficult. So the way I think about introducing new technology, introducing automation, introducing anything that changes or being forced to change because of something like a pandemic, um, what I really ended up thinking a lot about is how does that impact people and how do we, how do we help them through it? Um, so that's my lens. And I think that's a chief people officer lens to all of this, but makes a perfect partnership with platforms that make this easy for us. Because if you can imagine, uh, a C-suite person comes to, uh, comes to an HR department and says, tell me what we should do here. How do we develop all our people? And it's, it's an overwhelming task. What pro edge does is just beautifully delivers this on a platter, um, in a way that we could never do manually. So it's >>Talk to me a little bit Bettina about the last year and a half. And obviously as being the chief people, officer, you came in, you said about five months ago, but obviously during a very, very challenging time, I always think that the employee experience is directly related to the customer experience. I see them as inextricably linked, how have you been able to foster a good employee experience in your time here in a very strange world so that the customer experience, I mean, you guys are a fast moving company, 8,000 plus customers. So that, that customer experience is a stellar as UI path has always had to be. >>Yeah, I think for us, it came down to just some simple things. Um, one is just being flexible. Uh, there was not a one size fits all. We had to recognize that we have to meet people in a place that works for them, everybody, uh, dealt with a different reality and the same for our customers. Um, and I agree with you. I think employees that feel enabled that feel safe, that feel they have a future, um, have a much different relationship with their customers, um, then employees that are worried about their safety and security and whatnot. So we really took an approach of flexibility, safety, um, meeting people where they are jumping in when there were big crises in India and whatnot to really, really take care of our people and help them understand that we're here for >>Big impact on mental health. Did you see that, um, there was an insert in the wall street journal. I think it was last week, uh, women at work. Um, and it was a stat in there. I don't know if you're a working mom, but it said that, uh, 30, it was Qualtrics was the source. 30% of working moms said their mental health had declined since the pandemic. Interestingly only 15. I said only 15% of working dads. Um, so that was sort of interesting, uh, and notable, uh, but you know, to your point, CFO, financial health of the company, the chief people officer, the, the human capital health. Yeah. >>Um, very much so. And by the way, I'm not surprised by that stat as a woman. >>I thought it was, I thought it was low. I was talking to my wife about it the other day. She's not a working mom, but she's like my mental health, even though I'm not a working mom, I have my mental health. I'm a working dad, but, but I got it easier than she does, but, but, and I'm not surprised at the disparity. I'm surprised that the only 30% and 50%, I think it's a lot higher than that. And people may be just like, I don't know if that's actually, maybe some people like working at home and that's, I could see that both sides of that equation. Yeah. >>There's also a stigma around mental health that we've, that's been addressed in. So even during the Olympic coverage this last summer, but having your team be really focused and enabled and knowing that they took to your point, Kevin, from an upscale perspective, that they have a path where they can go, they can increase their own value to the company. >>I completely agree. And I think, uh, other studies show that what people really want is a future at work. And, and this is what I think privilege dresses. Beautiful. >>Yeah. It's interesting. Right. Cause I think when you talk about some of the mental health challenges, I think it can get down very quickly to a cab just on this crazy schedule or I'm on zoom for 14 hours a day. And I, I don't have the time to breathe in my time commuting where I may have had time to decompress. It's just been replaced with more meetings. And I think that that may be the, like the surface issue, but I actually think if you go below the surface, not being in the office, not having some of the in-person networking, not having some of that creates anxiety about the future. And you're not really sure around, okay, what does my career path look like? I may not be getting the amount of career counseling that I used to get just by impromptu conversations or, you know, just by more traditional ways. Um, but I think the reality is when we look at the way most companies are thinking about their future work models. It's not going to go back to the way the world operated two years ago, it's, we're going to be in some sort of hybrid model. And so it really becomes more important to actually dig below where maybe some of the challenges were in the passengers, not surfaced. And I think upskilling and thinking about, um, kind of role skills and roles, it just becomes a much more important conversation. >>Absolutely. Last question, Bettina for you, we're almost out of time, but you started this in the, in financial, in the finance organization. What do you see over the next couple of years in terms of being very much UI path land and expand with your customers? Where do you see it rolling out across two iPads. >>We're already talking to our sales enablement group. Um, for a couple of reasons we want them to experience it. We want them to have also, we want them to have the conversations with our customers much like what we learned yesterday, right? It's a multi-dimensional conversation. It's not just a financial ROI, it's a people journey, change management. So we'll, we're taking it to our sales enablement group. We're absolutely gonna use it in HR, uh, obviously. Um, and I, I would just think we'll use it in two years. It'll be enterprise wide >>Different is pro edge in the marketplace. And just in, particularly in terms of its business impact. >>Yeah. I take a stab. So when we think about the challenge in a topic like digital upskilling, I think in traditional approaches to learning, it would be okay, we're going to enroll someone in a learning program. You know, you're going to go through, do certain amount of self study. Maybe there's a class, the some classroom based training. And that's it. I think for us, we saw two different challenges on both sides of that. One was trying to identify who needs to learn, what, and what part of the organization, why is that important? What an executive may need to learn is going to be different from what someone who does the transaction processing, uh, for their, their full-time job needs to learn and learning kind of from basic digital acumen through kind of hands-on skills. What are the different, um, pieces? I think probably the more interesting part is the back end of that. >>And thinking about, you know, how do you actually put these skills into practice and how do you put scale? One of the buzzwords that's thrown around at this conference a lot is the concept of citizen led automation as a topic. And really how, how do you have your, does this users building bots or creating data dashboards or doing other things that is, um, that's challenging. So as we design the product, what we really wanted to think about was that end to end journey from kind of the point of identifying skills through the point of scaling a citizen led effort. It's one of the things we're really excited to be working with UI path on is one of the core technologies that we, that we view in this ecosystem is really thinking about how do you make that happen? And if the outcome is not just people are new things, but the outcome are, people are actually creating solutions that are, that are having an impact on their job on a day to day basis. We think that's a really powerful concept. >>It's really important work, what you guys are doing. Thank you both for joining David me on the program today, talking about this very interesting symbiotic relationship partners, PWC UI path customers. Really interesting, great work that you're doing. Thanks for joining us. Thank you so much for having us for Dave Volante. I'm Lisa Martin. You're watching the cube live from Las Vegas at the Bellagio UI path forward for it. We'll be right back, stick around.

>> From around the globe, it's theCUBE, with digital coverage of VeeamON 2020, brought to you by Veeam. >> Hi, everybody, welcome back to theCUBE's ongoing coverage of VeeamON 2020, it's Veeam online 2020. I'm Dave Vellante. And Rick Vanover's here, he's the senior director of product strategy at Veeam. Rick, it's always a great pleasure to see you. I wish we could see each other face-to-face. >> Yeah, you know it's different this year, but yeah, it is always great to be on theCUBE. I think in 2018, it had a eight-year gap and a couple of times we've been back since, and yeah, happy to be back on theCUBE. >> So how's it going with you guys with the online format? Breakouts are big for you 'cause you're profiling some new products that we're going to get into. How's it all working for you? Well, it's been different. It's a good way to explain it in one word, different. But the reality is, I have a, pardon the language, a side hustle here where at Veeam, I've worked with the event team to bring the best content, and for the breakouts, it's an area that I've been working a lot with our speakers and some of our partners and external experts, users, and people who have beaten ransomware and stuff like that. But I've worked really hard to aggregate the content and get the best blend of content. And we kind of have taken an interesting approach where the breakouts are that library of content that we think organizations and the people who attend the event really take away the most. So, we've got this full spectrum from R&D deep level stuff to just getting started type of stuff, and really all types of levels in between. We want the breakouts to focus on generally available products, right? So I've worked pretty diligently to bring a good spread across the different products. And then a little secret trick we're doing is that into the summer, we're going to open up new content. So there's this broadcast agenda that we've got publicized, but then beyond that we're going to sneak in some new content into the summer. >> Well, I'm glad you're thinking that way, because what a lot of people are doing, they're just trying to take their physical events and mirror it to the digital or the virtual, and I think so often with physical events, people forget about the afterglow, so I'm glad you guys are thinking about it upfront. >> Yeah, it has to be a mechanism, that we've used it a couple of different ways. One to match how things are going to be released, right? 'Cause Veeam, we're always releasing products across the different set. We have one flagship product, but then the other products have their own cycles. So if something works well for that, we'll put it into the summer library. And then it's also a great opportunity for us to reach deep and get some content from people that we might not have been able to get before. In fact, we had one of our engineers who's based in Australia, and great resource, great region, strong market for us, but if we were to have the in-person at that, I can't bring somebody from Australia for one session. But this was a great way to bring her expertise to the event without having the travel burden and different variety of speakers and different varieties of content. So there's ways that we've been able to build on it, but again, the top level word is definitely different. But I feel like it's working for sure. >> So, Rick, give us the helicopter view of some of the product areas that we should really be aware of as it relates to what you guys are doing at VeeamON 2020, and then we'll drill in. Give us the high level though. >> So for people attending the event online, my advice really is that we're spread across about 75 to 80% of the content is for technical people. 20% of the content in the breakouts is going to be for decision-makers or executives, that type. And then within the context of the technical content, we want to have probably 10 to 15% be presenters from our R&D group, so very technical low-level type discussions, highest level architect type stuff after that. Generic use case is a nice in-the-middle area, because we have a lot of people that are getting started with our products, so like maybe they're new to the Office 365 backup or they're new to backing up natively in the cloud. We have a lot of context around the virtual machine backup and storage integration and all those other great things, but when the platform is kind of spread out at Veeam, there's a lot to take in. So the thought is wherever anyone is on their journey with any of the products, and that's a hard task to do with a certain number of slots, we want to provide something for everyone at every level. So that's the helicopter view. >> So let me ask you a couple of followups on that. So let's start with Office 365. Now, you guys have shared data at this event, talking about most customers just say, "Oh, yeah, well, I trust Microsoft to do my backup." Well, of course, as we well know, backup is one thing (chuckles) but recovery is everything. Explain the value that you guys bring. Why can't I just rely on the SaaS vendor to do my backup and recovery? >> Well, there's a lot to that question, Dave. The number one thing I'll say is that at Veeam, we have partnerships with Microsoft, VMware, HPE, all the household brands of IT, and in many of these situations, we've always come into the market with the platform itself providing a basic backup. I'll give Windows, for example, anti-backup. It's there, but there's always a market for more capabilities, more functionality, more portability. So we've taken Office 365 as a different angle for backup, and we lead with the shared responsibility model. Microsoft as well as the other clouds make it very clear that data classification and that responsibility of data, that actually sits 100% with the customer. And so, yes, you can add things to the platform, but if we have organizations where we have things like, I need to retain my content forever, or I need a discovery use case, and then if you think about broader use cases, like OneDrive for business data, especially with the rapid shift of work from home, organizations may now be not so much using the file server, but using things like OneDrive for Business for file exchanges. So, having the control plane open that data is very important, so we really base it on the shared responsibility. And Microsoft is one of our strongest partners, so they are very keen for us to provide solutions that are going to consume and move data around to meet customer needs in the cloud and in the SaaS environment for sure. So, it's been a very easy conversation for our customers and it's our fastest growing product as well. So this product is doing great. I don't have the quarterly numbers but we just released in the mid part or the Q4, we just released the newest release, which implemented object storage support, so that's been the big ask for customers, right? So that product's doing great. >> Yeah, so that notion of shared responsibility, you hear that a lot in cloud security. You're applying it to cloud data protection, which you know security and data protection are now, there's a lot of gray area between them now. And I think security or data protection is a fundamental part of your security strategy. But that notion of shared responsibility is very important and one that's oftentimes misunderstood because people hear, oh, it's in the cloud, okay, my cloud vendor's got it covered. But what does that shared responsibility mean? Ultimately, isn't it up to the customer to own the end result? >> It is, and I look at especially Microsoft. They classify their software four different ways, on-prem software, software as a service, infrastructure as a service, and I forget whatever the third one is, but they have so many different ways that you can package their software, but in all of them, they put the data classification for the customer. And it's the same for other clouds as well. And if I'm an organization today, if I'm running data in a SaaS platform, if I am running systems in IAS platforms, in the hyperscale public clouds, that is an opportunity for me to really think about that control plane of the data, and the backup and restore responsibility, because it has to be easy to use. It has to be very consumable so that customers can avoid that data loss or be in a situation where the complexity to do a restore is so miserable that they may not even want to go do it. I've actually had conversations with organizations as they come to Veeam, that was their alternative. Oh, it's just too painful to do, like, why would you even do that? So that shared responsibility model across the different data types in the cloud and on-prem as well and SaaS models, that's really where we find the conversations go pretty nicely. >> Right, and if it's too complicated, you won't even bother testing it. So, I want to ask you something about cloud data. You mentioned cloud native capabilities, and I'm inferring from that, that you basically are not just taking your on-prem stack and shoving it into the cloud. You're actually taking advantage of the native cloud services. Can you explain what's going on there, and maybe some product specifics? >> Sure, so Veeam has this reputation of number one VM backup. I'm here in my office, I have posters from all over the years, and somewhere down here is number one VM backup. And that's where we cut our teeth and got our name out there. But now if you're in Azure, if you're in Amazon, we have cloud native backup products available. In fact, the last time you and I spoke was at Amazon re:Invent where we launched the Amazon product. And then last month, we launched the Azure product, which provides cloud native backup for Azure, and so now we have this cloud feature parody, and those products are going to move very quickly. As well, we've had this software as a service product for Office 365 where we keep adding services. And we saw in the general session, we're going to add protection for a new service in Office 365. So we're going to continue to innovate around these different areas, and there's also another cloud that we announced a capability for as well. So the platform at Veeam, it's growing, and it's amazing to see this happen, 'cause customers are making cloud investments and there's no cloud for all. So some organizations like this cloud, that cloud, or a little bit of these two clouds combined. So we have to really go into the cloud with customer needs in mind, because there's no one size fits all approach to the cloud, but the data, everybody knows how important that is. >> To that end though, each cloud is going to have a set of native services, and you've got to develop specific to that cloud, right, so that you can have the highest performance, the most efficient, the lowest cost data protection solution backup and recovery possible. Taking advantage of those native cloud services is going to be unique for each cloud, right? 'Cause AWS' cloud and Azure cloud, those are different technically underneath. Is that right? >> You're absolutely right, and the cost models have different behaviors across the clouds. In fact, the breakout that I did here at the event with Melissa Palmer, those who are interested in the economics of the cloud should check that out, because the cloud is all about consuming those resources. When I look at backup, I don't want backup to be a cost-prohibitive insurance policy, basically. I want backup to be a cost-effective, yet resilient technology that when we're using the cloud, we can kind of balance all these needs. And one of the ways that Veeam's done that is we've put in cost estimators, which it's not that big of a flashy part of the user interface, but it's so powerful to customers. The thought is if I want to consume infrastructure as a service in the cloud, and I want to back up via API call snapshots to EC2 instances only, nice and high performance, nice and fast, but the cost profile of that if I kept them for a year is completely different than if I used object storage. And what we're doing with the Veeam backup for Azure and Amazon products is we're putting those numbers right there in the wizard. So you could say, "Hey, I want to keep two years of data, "and I have snapshots and I have object storage," totally different cost profiles, and I'll put those cost estimates in there. You could make egregious examples where it'll be like 10 and 20 x the price, but it really allows customers to get it fast, to get it cost-efficient, and more importantly at the end of the day, have that protection that they need. And that's something I've been trying to evangelize that this cost estimator is a really big deal. >> It provides transparency so that you're going to let the business drive sort of what the data protection level is, as opposed to sort of whether it's a one-size-fit-all or you're under-protected or over-protected and spending too much. I ask Anton, "How do you prioritize?" Basically his answer was we look at the economics. And then ultimately you're giving tools to allow the customer to decide. >> Yeah, you don't want to have that surprise cloud bill at the end of the month. You don't want to have waste in the cloud, and Anton's right, the economics are very important. The modeling process that we use is interesting. I had a chat with one of the product managers who is basically in charge of our cloud economic modeling, and to the organizations out there, this is a really practical bit, is think about modeling, think about cloud economics, because here's the very important part. If you've already implemented something, it's too late. And what I mean by that, the economics, if they're not right when you implement it so you're not modeling it ahead of time, once you implement, you can monitor it all you want, but you're just going to monitor it off the model. So the thought is this is all a backwards process. You have to go backwards with the economics, with the model, and then that will lead you to no surprises down the road. >> I want to ask you about the COVID impact generally, but specifically as it relates to ransomware, we've had a lot more inquiries regarding ransomware. There's certainly a lot more talk about it in the press. What have you seen specifically with respect to ransomware since the pandemic and since the lockdown? >> So that's something that's near and dear to my heart. On the technology team here at product strategy, everyone has a little specialization, industry specialization. ransomware is mine, so good ask. Whew, so the one thing that sticks out to me a lot is identifying where ransomware comes in, and I have one data point that indicated around 58 or so percent of ransomware comes in through remote desktop. And the thought here is if we have shifted to remote access and new working models, what really worries me, Dave, is when people hustle, when people hurry. And the thought here is you can have it right or you can have it right now. In mid-March, we needed to make a move right now. So, I worry about incomplete security models, people hurrying to implement and maybe not taking their security right, especially when you think about most ransomware can come in through remote desktop. I though phish attacks were the main attack vector, but I had some data points that told me this. So I have been, and I just completed a great white paper that those watching this can go to veeam.com and download, but the thought here is I just completed a great white paper on tips to beat ransomware, and yes, Veeam has capabilities, but here's the logic, Dave. I like to explain it this way. Beating ransomware, and we had a breakout that I recorded here at the event and encourage everyone to watch that, I had two users share their story of how they beat ransomware with Veeam, two very different ways, too. Any product is or is not necessarily ransomware-resilient. It's like going through an audit. What I mean by that is people ask me all the time, is Veeam compliant to this standard or that standard? It's 100% dictated how the product's implemented, how the product's audited. Same with ransomware. It's 100% dictated on how Veeam is implemented and then what's the nature of the exploit. And so I break it down into three simple things. We have to educate. We have to know what threats are out there, we have to know who is accessing what data, and then the big part of it is the implementation. How have we implemented Veeam? Are we keeping data in immutable buckets in the cloud? Are we keeping data with an air gap? And then three, the remediation. When something does happen, how do we go about solving that problem? I talked to our tech support team who deals with it every day, and they have very good insights, very good feedback on this phenomena, and that they've helped me shape some of the recommendations I put in the paper. But yeah, it's a 30-page paper. I don't know if I can summarize it here. It's a long one for me, but the threat's real, and this is something we'll never be done with, right? I've done two other papers on it, and I'm going to have another one soon after that. But we're building stuff into the product, we're educating the market, and we're winning. We're seeing like I had the two customers beat ransomware, great stories. I think I learn so much hearing from someone who's gone through it, and that you can find that in the VeeamON broadcast for those attending here. >> Well, you touched on a couple. Take advantage of the cloud guys who have these immutable buckets that you can leverage. A lot of people don't even know about that. And then, like you say, create an air gap, and presumably there's best practice around how often you write to that bucket and how often you create that air gap. You maybe change up the patterns, I don't know, other thoughts on that. >> Well, I collectively put, I've created a term, and nobody's questioning me on it yet, so that's good, but I'm calling it ultra-resilient storage. And what I'm referring to is that immutable backup in the cloud. It becomes a math calculation. If you have one data point in there, that's good, but if you had a week's worth of data points, that's better. If you had a month's worth of data points, that's even better. But of course, those cost profiles are going to change. Same thing with tape, tape's an air gap, removable media, and I go back and forth on this, but some of the more resilient storage snapshot engines can do ultra-resilient techniques as well, such as like Pure Storage SafeMode and NetApp SnapVault. And then the last thing is actually a Veeam technology that's been out for, I don't know, three or four years now, insider protection, it's a completely out-of-band copy of backup data that Veeam Cloud Connect offers. So my thought here is that these ultra-resilient types, those are the best defense in these situations. It becomes a calculated risk of how much of it do I want to keep, because I want to have the most restore options available, I want to have no data loss. But I also don't want it old. There's a huge decline in value of taking your business back a year ago, because that's the last tape you had, for example. I want today's or yesterday's backup if I'm in that type of situation. So, I go through a lot of those points in my paper, but I hope that those out there fighting the war on ransomware have the tools. I know they have the tools to win with Veeam. >> Well, it's like we were talking about before, and ransomware is a really good example of the blurring lines between security and backup and recovery, of course. What role do analytics play in terms of providing transparency and identifying anomalous behavior in the whole ransomware equation? >> Well, the analytics are very important, and I have to be really kind of, be completely transparent. Veeam is a backup company. We're not a security tool. But it's getting awfully close. I don't want to say, the long form historical definition of IT security was something around this thing called a CIA triad, maintaining confidentiality, integrity, and availability of data. So, security tools are really big on the confidentiality and integrity side of it, but on the availability side, that's where Veeam can come in. So the analytics come in to our play pretty naturally. Veeam has had for years now an alarm that detects abnormal behavior in regards to CPU usage and disk write IO. If there's both of those are abnormally high, that is what we call possible ransomware activity. Or if we have a incremental backup that is like 100% change rate, that's a bad sign, things like that. And then the other angle is, even on PC's desktops, like this computer I'm talking to you now on, we have just simple logic of once you take a backup, eject the drive so it's offline, right? So analyzing where the threats come from, what kind of behavior they're going to have, when we apply it to backup, Veeam can have these builtin analytic engines that are just transparently there for our customers. There's no deep re-education necessary to use these, but the thought is we want a very flexible model that's just going to provide simple ease of use and then allow that protection with the threatscape to help the customers where we can, because no two ransomware threats are the same. That's the other thing. They are so varied in what they do, everything from application specific to files, and now there's these new ones that upload data. The ransom is actually a data leak. They're not encrypting the data, the ransom is take down potentially huge amounts of data leakage. So all kinds of threat actors out there, for sure. >> You know, the last line of questioning here, Rick, is I've said a number of times, it's ironic that we're entering this new decade and this pandemic hits. Everybody talks about the acceleration of certain trends, but if you think about the trends, last decade, it's always performance and cost, we talked a lot of granularity, we talked about simplicity, you guys expanded your number of use cases, the support, the compatibility matrix if you will. All those things are sort of things that you've executed on. As you look forward to this coming decade, we talked about cloud. I mean, we were talking about cloud back in 2008, 2009 timeframe, but it was a relatively small portion of the business. Now everybody's talking cloud, so cloud, cloud native, the whole discussion on ransomware, and being broad, our business resiliency. Digital transformation, we've been given lip service in a lot of cases to digital transformation. All of a sudden, that's changed. So as you pull out the telescope and look forward to the trends that are going to drive your thinking and Veeam's decision making, what do you look toward? >> Well, I think that Veeam is laser-focused on four things. Backup solutions for cloud, workloads, and there's incredible opportunity there, right? So yes, we have a great Azure story, great Amazon story, and in the keynote we indicated the next cloud capability, but there's still more, there's more services in the cloud that we need to go after. There's also the SaaS market. We have a great Office 365 story, but there's other SaaS products that we could provide a story for. And then the physical and virtual platforms, I mean, I feel really confident there. We've got really good capabilities, but there's always the 1% and what's in the corner, and what's the 1% of the 1%? And those are workloads we can continue to go after. But my thought is, as long as we attack those four areas, we're going to be on a good trajectory to deliver on that promise of being that most trusted provider of cloud data management for backup solutions. So, my thought here is that we're going to just keep adding projects, and it's very important to make it sometimes a new product. We don't want to just bolt it on to Backup and Replication V11 or V10 for that matter, because it'll slow it down. The cloud native products are going to have to have their own cadence, their own independent development cycles, and they're going to move faster, 'cause they'll need to. So you'll see us continue to add new products, new capabilities, and sometimes it'll intermix, and that's the whole definition of a platform, when one product is talking to another, from a management side, a control plane, giving customer portability, all that stuff. So we're just going to go after cloud virtual/physical SaaS and new products and new capabilities to do it. >> Well, Rick, it's always a pleasure talking to you. Your home studio looks great, you look good, but nonetheless, hopefully we'll be able to see each other face-to-face here shortly. Thanks for coming on. >> Thank you, Dave. >> All right, and thank you for watching, everybody. It's Dave Vellante and our continuous coverage of VeeamON 2020, the online version. We'll be right back right after this short break. (upbeat music)

>> Paige: Hello, everybody and thank you for joining us today on the Virtual Vertica BDC 2020. Today's breakout session is entitled A Technical Overview of the Vertica Architecture. I'm Paige Roberts, Open Source Relations Manager at Vertica and I'll be your host for this webinar. Now joining me is Ryan Role-kuh? Did I say that right? (laughs) He's a Vertica Senior Software Engineer. >> Ryan: So it's Roelke. (laughs) >> Paige: Roelke, okay, I got it, all right. Ryan Roelke. And before we begin, I want to be sure and encourage you guys to submit your questions or your comments during the virtual session while Ryan is talking as you think of them as you go along. You don't have to wait to the end, just type in your question or your comment in the question box below the slides and click submit. There'll be a Q and A at the end of the presentation and we'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to get back to you offline. Now, alternatively, you can visit the Vertica forums to post your question there after the session as well. Our engineering team is planning to join the forums to keep the conversation going, so you can have a chat afterwards with the engineer, just like any other conference. Now also, you can maximize your screen by clicking the double arrow button in the lower right corner of the slides and before you ask, yes, this virtual session is being recorded and it will be available to view on demand this week. We'll send you a notification as soon as it's ready. Now, let's get started. Over to you, Ryan. >> Ryan: Thanks, Paige. Good afternoon, everybody. My name is Ryan and I'm a Senior Software Engineer on Vertica's Development Team. I primarily work on improving Vertica's query execution engine, so usually in the space of making things faster. Today, I'm here to talk about something that's more general than that, so we're going to go through a technical overview of the Vertica architecture. So the intent of this talk, essentially, is to just explain some of the basic aspects of how Vertica works and what makes it such a great database software and to explain what makes a query execute so fast in Vertica, we'll provide some background to explain why other databases don't keep up. And we'll use that as a starting point to discuss an academic database that paved the way for Vertica. And then we'll explain how Vertica design builds upon that academic database to be the great software that it is today. I want to start by sharing somebody's approximation of an internet minute at some point in 2019. All of the data on this slide is generated by thousands or even millions of users and that's a huge amount of activity. Most of the applications depicted here are backed by one or more databases. Most of this activity will eventually result in changes to those databases. For the most part, we can categorize the way these databases are used into one of two paradigms. First up, we have online transaction processing or OLTP. OLTP workloads usually operate on single entries in a database, so an update to a retail inventory or a change in a bank account balance are both great examples of OLTP operations. Updates to these data sets must be visible immediately and there could be many transactions occurring concurrently from many different users. OLTP queries are usually key value queries. The key uniquely identifies the single entry in a database for reading or writing. Early databases and applications were probably designed for OLTP workloads. This example on the slide is typical of an OLTP workload. We have a table, accounts, such as for a bank, which tracks information for each of the bank's clients. An update query, like the one depicted here, might be run whenever a user deposits $10 into their bank account. Our second category is online analytical processing or OLAP which is more about using your data for decision making. If you have a hardware device which periodically records how it's doing, you could analyze trends of all your devices over time to observe what data patterns are likely to lead to failure or if you're Google, you might log user search activity to identify which links helped your users find the answer. Analytical processing has always been around but with the advent of the internet, it happened at scales that were unimaginable, even just 20 years ago. This SQL example is something you might see in an OLAP workload. We have a table, searches, logging user activity. We will eventually see one row in this table for each query submitted by users. If we want to find out what time of day our users are most active, then we could write a query like this one on the slide which counts the number of unique users running searches for each hour of the day. So now let's rewind to 2005. We don't have a picture of an internet minute in 2005, we don't have the data for that. We also don't have the data for a lot of other things. The term Big Data is not quite yet on anyone's radar and The Cloud is also not quite there or it's just starting to be. So if you have a database serving your application, it's probably optimized for OLTP workloads. OLAP workloads just aren't mainstream yet and database engineers probably don't have them in mind. So let's innovate. It's still 2005 and we want to try something new with our database. Let's take a look at what happens when we do run an analytic workload in 2005. Let's use as a motivating example a table of stock prices over time. In our table, the symbol column identifies the stock that was traded, the price column identifies the new price and the timestamp column indicates when the price changed. We have several other columns which, we should know that they're there, but we're not going to use them in any example queries. This table is designed for analytic queries. We're probably not going to make any updates or look at individual rows since we're logging historical data and want to analyze changes in stock price over time. Our database system is built to serve OLTP use cases, so it's probably going to store the table on disk in a single file like this one. Notice that each row contains all of the columns of our data in row major order. There's probably an index somewhere in the memory of the system which will help us to point lookups. Maybe our system expects that we will use the stock symbol and the trade time as lookup keys. So an index will provide quick lookups for those columns to the position of the whole row in the file. If we did have an update to a single row, then this representation would work great. We would seek to the row that we're interested in, finding it would probably be very fast using the in-memory index. And then we would update the file in place with our new value. On the other hand, if we ran an analytic query like we want to, the data access pattern is very different. The index is not helpful because we're looking up a whole range of rows, not just a single row. As a result, the only way to find the rows that we actually need for this query is to scan the entire file. We're going to end up scanning a lot of data that we don't need and that won't just be the rows that we don't need, there's many other columns in this table. Many information about who made the transaction, and we'll also be scanning through those columns for every single row in this table. That could be a very serious problem once we consider the scale of this file. Stocks change a lot, we probably have thousands or millions or maybe even billions of rows that are going to be stored in this file and we're going to scan all of these extra columns for every single row. If we tried out our stocks use case behind the desk for the Fortune 500 company, then we're probably going to be pretty disappointed. Our queries will eventually finish, but it might take so long that we don't even care about the answer anymore by the time that they do. Our database is not built for the task we want to use it for. Around the same time, a team of researchers in the North East have become aware of this problem and they decided to dedicate their time and research to it. These researchers weren't just anybody. The fruits of their labor, which we now like to call the C-Store Paper, was published by eventual Turing Award winner, Mike Stonebraker, along with several other researchers from elite universities. This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. That sounds exactly like what we want for our stocks use case. Reasoning about what makes our queries executions so slow brought our researchers to the Memory Hierarchy, which essentially is a visualization of the relative speeds of different parts of a computer. At the top of the hierarchy, we have the fastest data units, which are, of course, also the most expensive to produce. As we move down the hierarchy, components get slower but also much cheaper and thus you can have more of them. Our OLTP databases data is stored in a file on the hard disk. We scanned the entirety of this file, even though we didn't need most of the data and now it turns out, that is just about the slowest thing that our query could possibly be doing by over two orders of magnitude. It should be clear, based on that, that the best thing we can do to optimize our query's execution is to avoid reading unnecessary data from the disk and that's what the C-Store researchers decided to look at. The key innovation of the C-Store paper does exactly that. Instead of storing data in a row major order, in a large file on disk, they transposed the data and stored each column in its own file. Now, if we run the same select query, we read only the relevant columns. The unnamed columns don't factor into the table scan at all since we don't even open the files. Zooming out to an internet scale sized data set, we can appreciate the savings here a lot more. But we still have to read a lot of data that we don't need to answer this particular query. Remember, we had two predicates, one on the symbol column and one on the timestamp column. Our query is only interested in AAPL stock, but we're still reading rows for all of the other stocks. So what can we do to optimize our disk read even more? Let's first partition our data set into different files based on the timestamp date. This means that we will keep separate files for each date. When we query the stocks table, the database knows all of the files we have to open. If we have a simple predicate on the timestamp column, as our sample query does, then the database can use it to figure out which files we don't have to look at at all. So now all of our disk reads that we have to do to answer our query will produce rows that pass the timestamp predicate. This eliminates a lot of wasteful disk reads. But not all of them. We do have another predicate on the symbol column where symbol equals AAPL. We'd like to avoid disk reads of rows that don't satisfy that predicate either. And we can avoid those disk reads by clustering all the rows that match the symbol predicate together. If all of the AAPL rows are adjacent, then as soon as we see something different, we can stop reading the file. We won't see any more rows that can pass the predicate. Then we can use the positions of the rows we did find to identify which pieces of the other columns we need to read. One technique that we can use to cluster the rows is sorting. So we'll use the symbol column as a sort key for all of the columns. And that way we can reconstruct a whole row by seeking to the same row position in each file. It turns out, having sorted all of the rows, we can do a bit more. We don't have any more wasted disk reads but we can still be more efficient with how we're using the disk. We've clustered all of the rows with the same symbol together so we don't really need to bother repeating the symbol so many times in the same file. Let's just write the value once and say how many rows we have. This one length encoding technique can compress large numbers of rows into a small amount of space. In this example, we do de-duplicate just a few rows but you can imagine de-duplicating many thousands of rows instead. This encoding is great for reducing the amounts of disk we need to read at query time, but it also has the additional benefit of reducing the total size of our stored data. Now our query requires substantially fewer disk reads than it did when we started. Let's recap what the C-Store paper did to achieve that. First, we transposed our data to store each column in its own file. Now, queries only have to read the columns used in the query. Second, we partitioned the data into multiple file sets so that all rows in a file have the same value for the partition column. Now, a predicate on the partition column can skip non-matching file sets entirely. Third, we selected a column of our data to use as a sort key. Now rows with the same value for that column are clustered together, which allows our query to stop reading data once it finds non-matching rows. Finally, sorting the data this way enables high compression ratios, using one length encoding which minimizes the size of the data stored on the disk. The C-Store system combined each of these innovative ideas to produce an academically significant result. And if you used it behind the desk of a Fortune 500 company in 2005, you probably would've been pretty pleased. But it's not 2005 anymore and the requirements of a modern database system are much stricter. So let's take a look at how C-Store fairs in 2020. First of all, we have designed the storage layer of our database to optimize a single query in a single application. Our design optimizes the heck out of that query and probably some similar ones but if we want to do anything else with our data, we might be in a bit of trouble. What if we just decide we want to ask a different question? For example, in our stock example, what if we want to plot all the trade made by a single user over a large window of time? How do our optimizations for the previous query measure up here? Well, our data's partitioned on the trade date, that could still be useful, depending on our new query. If we want to look at a trader's activity over a long period of time, we would have to open a lot of files. But if we're still interested in just a day's worth of data, then this optimization is still an optimization. Within each file, our data is ordered on the stock symbol. That's probably not too useful anymore, the rows for a single trader aren't going to be clustered together so we will have to scan all of the rows in order to figure out which ones match. You could imagine a worse design but as it becomes crucial to optimize this new type of query, then we might have to go as far as reconfiguring the whole database. The next problem of one of scale. One server is probably not good enough to serve a database in 2020. C-Store, as described, runs on a single server and stores lots of files. What if the data overwhelms this small system? We could imagine exhausting the file system's inodes limit with lots of small files due to our partitioning scheme. Or we could imagine something simpler, just filling up the disk with huge volumes of data. But there's an even simpler problem than that. What if something goes wrong and C-Store crashes? Then our data is no longer available to us until the single server is brought back up. A third concern, another one of scalability, is that one deployment does not really suit all possible things and use cases we could imagine. We haven't really said anything about being flexible. A contemporary database system has to integrate with many other applications, which might themselves have pretty restricted deployment options. Or the demands imposed by our workloads have changed and the setup you had before doesn't suit what you need now. C-Store doesn't do anything to address these concerns. What the C-Store paper did do was lead very quickly to the founding of Vertica. Vertica's architecture and design are essentially all about bringing the C-Store designs into an enterprise software system. The C-Store paper was just an academic exercise so it didn't really need to address any of the hard problems that we just talked about. But Vertica, the first commercial database built upon the ideas of the C-Store paper would definitely have to. This brings us back to the present to look at how an analytic query runs in 2020 on the Vertica Analytic Database. Vertica takes the key idea from the paper, can we significantly improve query performance by changing the way our data is stored and give its users the tools to customize their storage layer in order to heavily optimize really important or commonly wrong queries. On top of that, Vertica is a distributed system which allows it to scale up to internet-sized data sets, as well as have better reliability and uptime. We'll now take a brief look at what Vertica does to address the three inadequacies of the C-Store system that we mentioned. To avoid locking into a single database design, Vertica provides tools for the database user to customize the way their data is stored. To address the shortcomings of a single node system, Vertica coordinates processing among multiple nodes. To acknowledge the large variety of desirable deployments, Vertica does not require any specialized hardware and has many features which smoothly integrate it with a Cloud computing environment. First, we'll look at the database design problem. We're a SQL database, so our users are writing SQL and describing their data in SQL way, the Create Table statement. Create Table is a logical description of what your data looks like but it doesn't specify the way that it has to be stored, For a single Create Table, we could imagine a lot of different storage layouts. Vertica adds some extensions to SQL so that users can go even further than Create Table and describe the way that they want the data to be stored. Using terminology from the C-Store paper, we provide the Create Projection statement. Create Projection specifies how table data should be laid out, including column encoding and sort order. A table can have multiple projections, each of which could be ordered on different columns. When you query a table, Vertica will answer the query using the projection which it determines to be the best match. Referring back to our stock example, here's a sample Create Table and Create Projection statement. Let's focus on our heavily optimized example query, which had predicates on the stock symbol and date. We specify that the table data is to be partitioned by date. The Create Projection Statement here is excellent for this query. We specify using the order by clause that the data should be ordered according to our predicates. We'll use the timestamp as a secondary sort key. Each projection stores a copy of the table data. If you don't expect to need a particular column in a projection, then you can leave it out. Our average price query didn't care about who did the trading, so maybe our projection design for this query can leave the trader column out entirely. If the question we want to ask ever does change, maybe we already have a suitable projection, but if we don't, then we can create another one. This example shows another projection which would be much better at identifying trends of traders, rather than identifying trends for a particular stock. Next, let's take a look at our second problem, that one, or excuse me, so how should you decide what design is best for your queries? Well, you could spend a lot of time figuring it out on your own, or you could use Vertica's Database Designer tool which will help you by automatically analyzing your queries and spitting out a design which it thinks is going to work really well. If you want to learn more about the Database Designer Tool, then you should attend the session Vertica Database Designer- Today and Tomorrow which will tell you a lot about what the Database Designer does and some recent improvements that we have made. Okay, now we'll move to our next problem. (laughs) The challenge that one server does not fit all. In 2020, we have several orders of magnitude more data than we had in 2005. And you need a lot more hardware to crunch it. It's not tractable to keep multiple petabytes of data in a system with a single server. So Vertica doesn't try. Vertica is a distributed system so will deploy multiple severs which work together to maintain such a high data volume. In a traditional Vertica deployment, each node keeps some of the data in its own locally-attached storage. Data is replicated so that there is a redundant copy somewhere else in the system. If any one node goes down, then the data that it served is still available on a different node. We'll also have it so that in the system, there's no special node with extra duties. All nodes are created equal. This ensures that there is no single point of failure. Rather than replicate all of your data, Vertica divvies it up amongst all of the nodes in your system. We call this segmentation. The way data is segmented is another parameter of storage customization and it can definitely have an impact upon query performance. A common way to segment data is by using a hash expression, which essentially randomizes the node that a row of data belongs to. But with a guarantee that the same data will always end up in the same place. Describing the way data is segmented is another part of the Create Projection Statement, as seen in this example. Here we segment on the hash of the symbol column so all rows with the same symbol will end up on the same node. For each row that we load into the system, we'll apply our segmentation expression. The result determines which segment the row belongs to and then we'll send the row to each node which holds the copy of that segment. In this example, our projection is marked KSAFE 1, so we will keep one redundant copy of each segment. When we load a row, we might find that its segment had copied on Node One and Node Three, so we'll send a copy of the row to each of those nodes. If Node One is temporarily disconnected from the network, then Node Three can serve the other copy of the segment so that the whole system remains available. The last challenge we brought up from the C-Store design was that one deployment does not fit all. Vertica's cluster design neatly addressed many of our concerns here. Our use of segmentation to distribute data means that a Vertica system can scale to any size of deployment. And since we lack any special hardware or nodes with special purposes, Vertica servers can run anywhere, on premise or in the Cloud. But let's suppose you need to scale out your cluster to rise to the demands of a higher workload. Suppose you want to add another node. This changes the division of the segmentation space. We'll have to re-segment every row in the database to find its new home and then we'll have to move around any data that belongs to a different segment. This is a very expensive operation, not something you want to be doing all that often. Traditional Vertica doesn't solve that problem especially well, but Vertica Eon Mode definitely does. Vertica's Eon Mode is a large set of features which are designed with a Cloud computing environment in mind. One feature of this design is elastic throughput scaling, which is the idea that you can smoothly change your cluster size without having to pay the expenses of shuffling your entire database. Vertica Eon Mode had an entire session dedicated to it this morning. I won't say any more about it here, but maybe you already attended that session or if you haven't, then I definitely encourage you to listen to the recording. If you'd like to learn more about the Vertica architecture, then you'll find on this slide links to several of the academic conference publications. These four papers here, as well as Vertica Seven Years Later paper which describes some of the Vertica designs seven years after the founding and also a paper about the innovations of Eon Mode and of course, the Vertica documentation is an excellent resource for learning more about what's going on in a Vertica system. I hope you enjoyed learning about the Vertica architecture. I would be very happy to take all of your questions now. Thank you for attending this session.

>> Announcer: Live from Orlando, Florida it's theCUBE! Covering Microsoft Ignite, brought to you by Cohesity. >> Hello, and happy taco Tuesday CUBE viewers! You are watching theCUBE's live coverage of Microsoft's Ignite here in Orlando, Florida. I'm your host Rebecca Knight, along with Stu Miniman. We're joined by Scott Hanselman, he is the partner program manager at Microsoft. Thank you so much for coming on theCUBE! >> Absolutely, my pleasure! >> Rebecca: And happy taco Tuesday to you! Will code for tacos. >> Will code for tacos. >> I'm digging it, I'm digging it >> I'm a very inexpensive coder. >> So you are the partner program manager, but you're really the people's programmer at Microsoft. Satya Nadella up on the main stage yesterday, talking about programming for everyone, empowering ordinary citizen developers, and you yourself were on the main stage this morning, "App Development for All", why is this such a priority for Microsoft at this point in time? >> Well there's the priority for Microsoft, and then I'll also speak selfishly as a priority for me, because when we talk about inclusion, what does that really mean? Well it is the opposite of exclusion. So when we mean inclusion, we need to mean everyone, we need to include everyone. So what can we do to make technology, to make programming possible, to make everyone enabled, whether that be something like drag and drop, and PowerApps, and the Power platform, all the way down to doing things like we did in the keynote this morning with C# on a tiny micro-controller, and the entire spectrum in between, whether it be citizen programmers in Excel using Power BI to go and do machine learning, or the silly things that we did in the keynote with rock, paper scissors that we might be able to talk about. All of that means including everyone and if the site isn't accessible, if Visual Studio as a tool isn't accessible, if you're training your AI in a non-ethical way, you are consciously excluding people. So back to what Satya thinks is why can't everyone do this? SatyaSacha thinks is why can't everyone do this? Why are we as programmers having any gate keeping, or you know, "You can't do that you're not a programmer, "you know, I'm a programmer, you can't have that." >> So what does the future look like, >> Rebecca: So what does the future look like, if everyone knows how to do it? I mean, do some imagining, visioning right now about if everyone does know how to do this, or at least can learn the building blocks for it, what does technology look like? >> Well hopefully it will be ethical, and it'll be democratized so that everyone can do it. I think that the things that are interesting, or innovative today will become commoditized tomorrow, like, something as simple as a webcam detecting your face, and putting a square around it and then you move around, and the square, we were like, "Oh my God, that was amazing!" And now it's just a library that you can download. What is amazing and interesting today, like AR and VR, where it's like, "Oh wow, I've never seen augmented reality work like that!" My eight-year-old will be able to do it in five years, and they'll be older than eight. >> So Scott, one of the big takeaways I had from the app dev keynote that you did this morning was in the past it was trying to get everybody on the same page, let's move them to our stack, let's move them to our cloud, let's move them on this programming language, and you really talked about how the example of Chipotle is different parts of the organization will write in a different language, and there needs to be, it's almost, you know, that service bus that you have between all of these environments, because we've spent, a lot of us, I know in my career I've spent decades trying to help break down those silos, and get everybody to work together, but we're never going to have everybody doing the same jobs, so we need to meet them where they are, they need to allow them to use the tools, the languages, the platforms that they want, but they need to all be able to work together, and this is not the Microsoft that I grew up with that is now an enabler of that environment. The word we keep coming back to is trust at the keynote. I know there's some awesome, cool new stuff about .net which is a piece of it, but it's all of the things together. >> Right, you know I was teaching a class at Mesa Community College down in San Diego a couple of days ago and they were trying, they were all people who wanted jobs, just community college people, I went to community college and it's like, I just want to know how to get a job, what is the thing that I can do? What language should I learn? And that's a tough question. They wonder, do I learn Java, do I learn C#? And someone had a really funny analogy, and I'll share it with you. They said, well you know English is the language, right? Why don't the other languages just give up? They said, you know, Finland, they're not going to win, right? Their language didn't win, so they should just give up, and they should all speak English, and I said, What an awful thing! They like their language! I'm not going to go to people who do Haskell, or Rust, or Scala, or F#, and say, you should give up! You're not going to win because C won, or Java won, or C# won. So instead, why don't we focus on standards where we can inter-operate, where we can accept that the reality is a hybrid cloud things like Azure Arc that allows us to connect multiple clouds, multi-vendor clouds together. That is all encompassing the concept of inclusion, including everyone means including every language, and as many standards as you can. So it might sound a little bit like a Tower of Babel, but we do have standards and the standards are HTTP, REST, JSON, JavaScript. It may not be the web we deserve, but it's the web that we have, so we'll use those building block technologies, and then let people do their own thing. >> So speaking of the keynote this morning, one of the cool things you were doing was talking about the rock, paper, scissors game, and how it's expanding. Tell our viewers a little bit more about the new elements to rock, paper, scissors. >> So folks named Sam Kass, a gentleman named Sam Kass many, many years ago on the internet, when the internet was much simpler web pages, created a game called Rock, Paper, Scissors, Lizard, Spock, and a lot of people will know that from a popular TV show on CBS, and they'll give credit to that show, in fact it was Sam Kass and Karen Bryla who created that, and we sent them a note and said, "Hey can I write a game about this?" And we basically built a Rock, Paper, Scissors, Lizard, Spock game in the cloud containerized at scale with multiple languages, and then we also put it on a tiny device, and what's fun about the game from a complexity perspective is that rock, paper, scissors is easy. There's only three rules, right? Paper covers rock, which makes no sense, but when you have five, it's hard! Spock shoots the Rock with his phaser, and then the lizard poisons Spock, and the paper disproves, and it gets really hard and complicated, but it's also super fun and nerdy. So we went and created a containerized app where we had all different bots, we had node, Python, Java, C#, and PHP, and then you can say, I'm going to pick Spock and .net, or node and paper, and have them fight, and then we added in some AI, and some machine learning, and some custom vision such that if you sign in with Twitter in this game, it will learn your patterns, and try to defeat you using your patterns and then, clicking on your choices and fun, snd then, clicking on your choices and fun, because we all want to go, "Rock, Paper, Scissors shoot!" So we made a custom vision model that would go, and detect your hand or whatever that is saying, this is Spock and then it would select it and play the game. So it was just great fun, and it was a lot more fun than a lot of the corporate demos that you see these days. >> All right Scott, you're doing a lot of different things at the show here. We said there's just a barrage of different announcements that were made. Love if you could share some of the things that might have flown under the radar. You know, Arc, everyone's talking about, but some cool things or things that you're geeking out on that you'd want to share with others? >> Two of the things that I'm most excited, one is an announcement that's specific to Ignite, and one's a community thing, the announcement is that .net Core 3.1 is coming. .net Core 3 has been a long time coming as we have began to mature, and create a cross platform open source .net runtime, but .net Core 3.1 LTS Long Term Support means that that's a version of .net core that you can put on a system for three years and be supported. Because a lot of people are saying, "All this open source is moving so fast! "I just upgraded to this, "and I don't want to upgrade to that". LTS releases are going to happen every November in the odd numbered years. So that means 2019, 2021, 2023, there's going to be a version of .net you can count on for three years, and then if you want to follow that train, the safe train, you can do that. In the even numbered years we're going to come out with a version of .net that will push the envelope, maybe introduce a new version of C#, it'll do something interesting and new, then we tighten the screws and then the following year that becomes a long term support version of .net. >> A question for you on that. One of the challenges I hear from customers is, when you talk about hybrid cloud, they're starting to get pulled apart a little bit, because in the public cloud, if I'm running Azure, I'm always on the latest version, but in my data center, often as you said, I want longer term support, I'm not ready to be able to take that CICD push all of the time, so it feels like I live, maybe call it bimodal if you want, but I'm being pulled with the am I always on the latest, getting the latest security, and it's all tested by them? Or am I on my own there? How do you help customers with that, when Microsoft's developing things, how do you live in both of those worlds or pull them together? >> Well, we're really just working on this idea of side-by-side, whether it be different versions of Visual Studio that are side-by-side, the stable one that your company is paying for, and then the preview version that you can go have side-by-side, or whether you could have .net Core 3, 3.1, or the next version, a preview version, and a safe version side-by-side. We want to enable people to experiment without fear of us messing up their machine, which is really, really important. >> One of the other things you were talking about is a cool community announcement. Can you tell us a little bit more about that? >> So this is a really cool product from a very, very small company out of Oregon, from a company called Wilderness Labs, and Wilderness Labs makes a micro-controller, not a micro-processor, not a raspberry pie, it doesn't run Linux, what it runs is .net, so we're actually playing Rock, Paper, Scissors, Lizard, Spock on this device. We've wired it all up, this is a screen from our friends at Adafruit, and I can write .net, so somehow if someone is working at, I don't know, the IT department at Little Debbie Snack Cakes, and they're making WinForms applications, they're suddenly now an IOT developer, 'cause they can go and write C# code, and control a device like this. And when you have a micro-controller, this will run for weeks on a battery, not hours. You go and 3D print a case, make this really tiny, it could become a sensor, it could become an IOT device, or one of thousands of devices that could check crops, check humidity, moisture wetness, whatever you want, and we're going to enable all kinds of things. This is just a commodity device here, this screen, it's not special. The actual device, this is the development version, size of my finger, it could be even smaller if we wanted to make it that way, and these are our friends at Wilderness Labs. and they had a successful Kickstarter, and I just wanted to give them a shout out, and I just wanted to give them a shoutout, I don't have any relationship with them, I just think they're great. >> Very cool, very cool. So you are a busy guy, and as Stu said, you're in a lot of different things within Microsoft, and yet you still have time to teach at community college. I'm interested in your perspective of why you do that? Why do you think it's so important to democratize learning about how to do this stuff? >> I am very fortunate and I think that we people, who have achieved some amount of success in our space, need to recognize that luck played a factor in that. That privilege played a factor in that. But, why can't we be the luck for somebody else, the luck can be as simple as a warm introduction. I believe very strongly in what I call the transitive value of friendship, so if we're friends, and you're friends, then the hypotenuse can be friends as well. A warm intro, a LinkedIn, a note that like, "Hey, I met this person, you should talk to them!" Non-transactional networking is really important. So I can go to a community college, and talk to a person that maybe wanted to quit, and give a speech and give them, I don't know, a week, three months, six months, more whatever, chutzpah, moxie, something that will keep them to finish their degree and then succeed, then I'm going to put good karma out into the world. >> Paying it forward. >> Exactly. >> So Scott, you mentioned that when people ask for advice, it's not about what language they do, is to, you know, is to,q you know, we talk in general about intellectual curiosity of course is good, being part of a community is a great way to participate, and Microsoft has a phenomenal one, any other tips you'd give for our listeners out there today? >> The fundamentals will never go out of style, and rather than thinking about learning how to code, why not think about learning how to think, and learning about systems thinking. One of my friends, Kishau Rogers, talked about systems thinking, I've hade her on my podcast a number of times, and we were giving a presentation at Black Girls Code, and I was talking to a fifteen-year-old young woman, and we were giving a presentation. It was clear that her mom wanted her to be there, and she's like, "Why are we here?" And I said, "All right, let's talk about programming "everybody, we're talking about programming. "My toaster is broken and the toast is not working. "What do you think is wrong?" Big, long, awkward pause and someone says, "Well is the power on?" I was like, "Well, I plugged a light in, "and nothing came on" and they were like, "Well is the fuse blown?" and then one little girl said "Well did the neighbors have power?", And I said, "You're debugging, we are debugging right?" This is the thing, you're a systems thinker, I don't know what's going on with the computer when my dad calls, I'm just figuring it out like, "Oh, I'm so happy, you work for Microsoft, "you're able to figure it out." >> Rebecca: He has his own IT guy now in you! >> Yeah, I don't know, I unplug the router, right? But that ability to think about things in the context of a larger system. I want toast, power is out in the neighborhood, drawing that line, that makes you a programmer, the language is secondary. >> Finally, the YouTube videos. Tell our viewers a little bit about those. you can go to D-O-T.net, so dot.net, the word dot, you can go to d-o-t.net, so dot.net, the word dot, slash videos and we went, and we made a 100 YouTube videos on everything from C# 101, .net, all the way up to database access, and putting things in the cloud. A very gentle, "Mr. Rodgers' Neighborhood" on-ramp. A lot of things, if you've ever seen that cartoon that says, "Want to draw an owl? "Well draw two circles, "and then draw the rest of the fricking owl." A lot of tutorials feel like that, and we don't want to do that, you know. We've got to have an on-ramp before we get on the freeway. So we've made those at dot.net/videos. >> Excellent, well that's a great plug! Thank you so much for coming on the show, Scott. >> Absolutely my pleasure! >> I'm Rebecca Knight, for Stu Miniman., stay tuned for more of theCUBE's live coverage of Microsoft Ignite. (upbeat music)

(energetic music) >> Announcer: From our studios in the heart of Silicon Valley; Palo Alto, California. This is a CUBE Conversation. >> Hi everyone, welcome to this special CUBE Power Panel recorded here in Palo Alto, California. We've got remote guests from around the Internet. We have Evan Anderson, Mark Anderson, Phil Lohaus. Thanks for comin' on. Evan is with INVNT/IP, an organization with companies and individuals that fight nation-sponsored intellectual property theft and also author of the huge report Theft Nation Almost a 100 pages of really comprehensive analysis on it. Mark Anderson with the Future in Review CEO of Pattern, Computer and Strategic New Service Chairman of Future in Review Conference, and author of the book "The Pattern Future: "Find the World's Greatest Secrets "and Predicting the Future Using Discovery Patterns" and Phil Lohaus, American Enterprise Institute. Former intelligent analyst, researcher at the American Enterprise Institute, studying competitive strategy and emerging technologies. Guys, thanks for coming on. This topic is, is industrial IoT the new battleground? Mark, you cover the Future Review. Security is the battleground. It's not just a silo'd space. It's horizontally scalable across every single touch point of the Internet, individuals, national security, companies, global, what's your perspective on this new battleground? >> Well, thank you, I took some time and watched your last presentation on this, which I thought was excellent. And maybe I'll try to pick up from there. There's a lot of discussion there about the technical aspects of IoT, or IIoT, and some of the weaknesses, you know firewalls failing, assuming that someone's in your network. But I think that there's a deeper aspect to this. And the problem I think, John, is that yes, they are in your network already, but the deeper problem here is, who is it? Is it an individual? Is it a state? And whoever it is, I'm going to put something out that I think is going to be worth talking more deeply about, and that is, if people who can do the most damage are already in there, and are ready to do it, the question isn't "Can they?" It's "Why have they not?" And so literally, I think if you ask world leaders today, are they in the electric grid? Yes. Is Russia in ours, are we in theirs? Yes. If you said, is China in our most important areas of enterprise? Absolutely. Is Iran in our banks and so forth? They are. And you actually see states of war going on, that are nuisances, but are not what you might call Cybergeddon. And I really believe that the world leaders are truly afraid. Perhaps more afraid of that than of nuclear war. So the amount of death and destruction that could happen if everybody cut loose at the same time, is so horrifying, my guess is that there's a human restraint involved in this, but that technically, it's already game over. >> Phil, Cybergeddon, I love that term, because that's a part of our theme here, is apocalypse now or later? Industrial IoT, or IIoT, or the Internet, all these touch points are creating a surface area that for penetration's purposes, any packet can get in. Nation-states, malware, you name it. It's all problem. But this is the new war battleground. This is now digital Cybergeddon. Forget the wall on the southern border, physical wall. We're talking about a digital wall. We have major threats going on to our society in the United States, and global. This is new, rules of engagement, or no rules of engagement on how to compete in a digital war. This is something that the government's supposed to protect us for. I mean, if someone drops troops in California, physical people, the government's supposed to stop that. But if it's a digital war, it's packets. And the companies are responsible for all this. This doesn't make any sense to me. Break it down, what's the problem? And how do we solve this? >> Sure, well the problem is is that we're actually facing different kinds of threats than we were typically used to facing in the past. So in the past when we go to war, we may have a problem with a foreign country, or a conflict is coming up. We tend to, and by we I mean the United States, we tend to think of these things as we're going to send troops in, or we're going to actually have a physical fight, or we're going to have some other kind of decisive culmination of events, end of a conflict. What we're dealing with now is very different. And it's actually something that isn't entirely new. But the adversaries that we're facing now, so let's say China, Russia, and Iran, just to kind of throw them into some buckets, they think about war very differently. They think about the information space more broadly, and partially because they've been so used to having to kind of be catching up to America in terms of technology, they found other ways to compete with America, and ways that we really haven't been focusing on. And that really, I would argue, extends most prominently to the information space. And by the information space I'm speaking very broadly. I'm talking about, not just information in terms of social media, and emails, and things like that, but also things like what we're talking about today, like IIoT. And these are new threat landscapes, and ones where our competitors have a integrated way of approaching the conflict, one in which the state and private sectors kind of are molded or fused or at least are compelled to work together and we have a very different space here in the United States. And I'm happy to unpack that as we talk about that today, but what we're now facing, is not just about technical capabilities, it's about differences in governing systems, differences in governing paradigms. And so it's much bigger than just talking about the technical specifics. >> Evan, I want you to weigh in on this because one of the things that I feel strongly about, and this is pretty obvious from the commentary, and experts I talk to is, the United States has always been good at defending itself physically, you know war, in being places. Digitally, we've been really good at offense, but terrible on defense, has been the metaphor. I spoke with former four-star General Keith Alexander, who ran the NSA and was first commander of the cyber command, who is now the CEO of IronNet. He and I were talking on-camera and privately and he's saying, "Look it. "we suck at defense digitally. "We're great at offense, we can take someone out "on the offense." But we're talking about IoT, about monitoring. These are technical challenges. This is network nerds, and software engineers have to solve this problem with the prism of defense. This is a new paradigm. This is what we're kind of getting to. And Mark, you kind of addressed it. But this is the challenge. IoT is going to create more points that we have to defend that we suck now at defending, how are we going to get better. This is the paradox. >> Yeah, I think that's certainly accurate. And one of our problems here is that as a society we've always been open. And that was how the Internet was born. And so we have a real paradigm shift now from a world in which the U.S. was leading an open world, that was using the Internet for, I mean there have been problems with security since day one, but originally the Internet was an information-sharing exercise. And we reached a point in human history now where there are enough malicious hackers that have the capabilities we didn't want them to have, but we need to change that outlook. So, looking at things like Industrial IoT, what you're seeing is not so much that this is the battlefield in specific, it's that everything like it is now the battlefield. So in my work specifically we're focused more on economic problems. Economic conflicts and strategies. And if you look at the doctrines that have come out of our adversaries in the last decade, or really 20 years, they very much did what Phil said, and they looked at our weaknesses, and one of those biggest weaknesses that we've always had is that an open society is also unable necessarily to completely defend itself from those who would seek to exploit that openness. And so we have to figure out as a society, and I believe we are. We're running a fine line, we're negotiating this tightrope right now that involves defending the values and the foundational critical aspects of our society that require openness, while also making sure that all the doors aren't open for adversaries. And so we'll continue to deal with that as a society. Everything is now a battlefield and a much grayer area, and IoT certainly isn't helping. And that's why we have to work so hard on it. >> I want to talk about the economic piece on the next talk track of rounds. Theft, and intellectual property that you cover deeply. But Mark and Phil, this notion of Cybergeddon meets the fact that we have to be more defensive. Again, principles of openness are out there. I mean, we have open source. There is a potential path here. Open source software has been, I think, depending on who you talk to, fourth generation, or fifth, depending on how old you are, but it's now mainstream enough now. Are we ever going to get to a formula where we can actually be strong in defense as well as just offense with respect to protecting digitally? >> Phil, do you want that? >> Well, yeah, I would just say that I'm glad to hear that General Alexander is confident about our offensive capabilities. But one of the... To NSA that is conducting these offensive capabilities. When we talk about Russia, Iran, China, or even a smaller group, like let's say an extremist group or something like that, there's an integration between command and control, that we simply don't have here in the States. For example, the Panasonic and Sony examples always come to mind, as ones where there are attacks that can happen against American companies that then have larger implications that go beyond just those companies. So and this may not be a case where the NSA is even tracking the threat. There's been some legislation that's come out, rather controversial legislation about so-called hacking back initiatives and things like that. But I think everybody knows that this is already kind of happening. The real question is going to be, how does the public sector, and how does the private sector work together to create this environment where they're working in synergy, rather than at cross purposes? >> Yeah, and this brings up, I've heard this before. I've heard people talk about the fact that open source nation states can actually empower by releasing tools in open source via the Dark Web or other vehicles, to not actually have, quote, their finger prints, on any attacks. This seems to be a tactic. >> Or go through criminals, right? Use proxies, things like that. It's getting even more complicated and Alexander's talked about that as well, right? He's talked about the convergence of crime and nation-state actions. So whereas with nation-states it's already hard-attributed enough, if that's being outsourced to either whether it's patriotic hackers or criminal groups, it's even more difficult. >> I think you know, Keith is a good friend of all of ours, obviously, good guy. His point is a good one. I'd like to take it a little more extreme state and say, defense is worth doing and probably hopeless. (everyone laughs) So, as they always say, all it takes is one failure. So, we always talk about defense, but really, he's right. Offense is easy. You want to go after somebody? We can get them. But if you want to play defense against a trillion potential points of failure, there's no chance. One way to say this is, if we ignore individuals for a moment and just look at nation-states, it's pretty clear that any nation-state of size, that wants to get into a certain network, will get in. And then the question will be, Well, once they're in, can they actually do damage? And the answer is probably yeah, they probably can. Well, why don't they? Why don't they do more damage? We're kind of back to the original premise here, that there's some restraint going on. And I suspect that Keith's absolutely right because in general, they don't want to get attacked. They don't want to have to come back at them what they're about to do to your banks or your grid, and we could do that. We all could do that. So my guess is, there's a little bit of failure on our part to have deep discussions about how great our defenses either are, or are not, when frankly the idea of defense is a good idea, worthwhile idea, but not really achievable. >> Yeah, that's a great point. That comes up a lot where it's like, people don't want retaliation, so it's a big, critical event that happens, that's noticeable as a counterstrike or equivalent. But there's been discussion of the, I call it "the slow bleed" where they push the line of where that is, like slowly infiltrate, and just cause disruption and inconvenience, as a tactic. This has become something we're seeing a lot of. Whether it's misinformation campaigns on fake news, to just disrupting operations slowly over time, and just kind of, 1,000 paper cuts, if you will. Your guys' thoughts on that? Is that something you guys see out there that's happening? >> Well, you saw Iran go after our banks. And we were pushing Iran pretty hard on the sanctions. Everybody knows they did that. It wasn't very much fun for anybody. But what they didn't do is take down the entire banking system. Not sure they could, but they didn't. >> Yeah, I would just add there that you see this on multiple fronts. You see this is by design. I'm sure that Mark is talking about this in his report but... they talk about this incremental approach that over time, this is part of the problem, right? Is that we have a very kind of black or white conception of warfare in this country. And a lot of times, even companies are going to think, well you know, we're at peace, so why would I do something that may actually be construed as something that's warlike or offensive or things like that? But in reality, even though we aren't technically at war, all of these other actors view this as a real conflict. And so we have to get creative about how we think about this within the paradigm that we have and the legal strictures that we have here in this country. >> Well there's no doubt at least in my non-expert military opinion, but as someone who is a techie, been on the Internet from day one, all my life, and all those tools, you guys as well, I personally think we're at war. 100%, there's no debate on that. And I think that we have to get better policy around this and understand it better. Because it's happening. And one of the obvious areas that we see in the news everyday, it's Huawei and intellectual property theft. This is an economic impact. I mean just look at what's happening in Brexit in the U.K. If that was essentially manipulated, that's the ultimate smart bomb, is to just destroy their financial system, which ended up happening through that misinformation. So there are economic realizations here, Evan,that not only come from the misinformation campaigns and other attacks, but there's real value with intellectual property. This is the report you put out. Your thoughts? >> There's very much an active conflict going on in the economic sphere, and that's certainly an excellent point. I think one of the most important things that most of the world doesn't quite understand yet, but our adversaries certainly understand, is that wars are fought for usually, just a few reasons. And there's a lot of different justification that goes on. But often it's for economic benefit. And if you look at human history, and you look at modern history, a lot of wars are fought for some form of economic benefit, often in the form of territory, et cetera, but in the modern age, information can directly and very quite obviously translate into economic benefit. And so when you're bleeding information, you're really bleeding money. And when I say information, again, it's a broad word, but intellectual property, which our definition, here at INVNT/IP is quite broad too, is incredibly valuable. And so if you have an adversary that's consistently removing intellectual property from what I would call our information ecosystem, and our business ecosystem, we're losing a lot of economic value there, and that's what wars are fought over. And so to pretend that this conflict is inactive, and to pretend that the underlying economy and economic strength that is bolstered or created by intellectual property isn't critical would be silly. And so I think we need to look at those kinds of dynamics and the kind of Gerasimov Doctrine, and the essential doctrine of unrestricted warfare that came out of the People's Republic of China are focused on avoiding kinetic conflict while succeeding at the kinds of conflict that are more preferable, particularly in an asymmetric environment. So that's what we're dealing with. >> Mark and Phil, people waking up to this reality are certainly. People in the know are that I talk to, but generally speaking across the board, is this a woke moment for tech? This Armageddon now or later? >> Woke moment for politicians not for tech, I think. I'm sure Phil would agree with this, but the old guard, go back to when Keith was running the NSA. But at that time, there was a very clear distinction between military and economic security. And so when you said security, that meant military. And now all the rules have changed. All the ways CFIUS works in the United States have changed. The legislation is changing, and now if you want to talk about security, most major nations equate economic security with national security. And that wasn't true 10 years ago. >> That's a great point. That's really profound, I totally agree. Phil. >> I think you're seeing a change in realization in Washington about this. I mean, if you look at the cybersecurity strategy of 2018, it specifically says that we're going to be moving from a posture of active defense to one of defending forward. And we can get into the discussion about what those words mean, but the way I usually boil down is it means, going from defending, but maybe a little bit forward, to actually going out and making sure that our interests are protected. And the reason why that's important, and we're talking about offense versus defense here, obviously the reason why, from what Mark was saying, if they're already in the networks, and they haven't actually done anything, it's because they're afraid of what that offensive response could be. So it's important that we selectively demonstrate what costs we could impose on different actors for different kinds of actions, especially knowing that they're already operating inside of our network. >> That's a great point. I mean, I think that's again another profound statement because it's almost like the pin in the grenade. Once they pull it, the damage is done. Again, back to our theme, Armageddon, now or later? What's the answer to this, guys? Is it the push to policy conversation and the potential consequences higher? Get that narrative going. Is it more technical protection in the networks? What's some of the things that people are talking about and thinking about around this? >> And it's really all of the above. So the tough part about this for any society and for our society is that it's expensive to live in a world with this much insecurity. And so when these kind of low-level conflicts are going on, it costs money and it costs resources. And companies had to deal with that. They spent a long time trying to dodge security costs, and now particularly with the advent of new law like the GDPR in Europe, it's becoming untenable not to spend that defensive money, even as a company, right? But we also are looking at a deepening to change policy. And I think there's been a lot of progress made. Mark mentioned the CFIUS reforms. There are a lot of different essentially games of Whack-A-Mole being played all around the world right now figuring out how to chase these security problems that we let go too long, but there's many, many, many fronts that we need to-- >> Whack-A-Mole's a great example. The visualization of that is just horrendous. You know, not the ideal scenario. But I got to get your point on this, because one of the things that comes up all the time in our conversations in theCUBE is, the government's job is to protect our securities. So again, if someone came in, and invaded my town in Palo Alto, it's not my responsibility to fight for the town. Maybe defend my own house. But if I'm a company being attacked by Russia, or China or Iran, isn't it the government's responsibility to protect me as a citizen and the company doing business there? So again, this is kind of the confusion that people have. If somebody's going to defend their hack, I certainly got to put security practices in place. This is new ground for the government, digitally speaking. >> When we started this INVNT/IP project, it was about seven years ago. And I was told by a very smart guy in D.C. that our greatest challenge was going to be American corporations, global corporations. And he was absolutely right. Literally in this fight to protect intellectual property, and to protect the welfare even of corporations, our greatest enemies so far have been American corporations. And they lobby hard for China, while China is busy stealing from them, and stealing from their company, and stealing from their country. All that stuff's going on, on a daily basis and they're in D.C. lobbying in favor of China. Don't do anything to make them mad. >> They're getting their pockets picked at the same time. And they're trying to do business in China. They're getting their pockets picked. That's what you're saying. >> They're going for the quarterly earnings report and that's all. >> So the problem is-- >> Yeah so-- >> The companies themselves are kind of self-inflicted wounds here for them. >> Yes. >> Yeah, just to add to that, on this note, there have been some... Business to settle interest. And this is something you're seeing a little bit more of. There's been legislation through CFIUS and things like that. There have been reforms that discourage the flow of Chinese money in the Silicon Valley. And there's actually a measurable difference in that. Because people just don't want to deal with the paperwork. They don't want to deal with the reputational risk, et cetera, et cetera. And this is really going to be the key challenge, is having policy makers not only that are interested in addressing this issue, because not all of them are even convinced it's a problem, if you can believe it or not, but having them interested and then having them understand the issue in a way that the legislation can actually be helpful and not get in the way of things that we value, such as innovation and entrepreneurialism and things like that. So it's going to take sophisticated policy-making and providing incentives so that companies actually want to participate and helping to make America safer. >> You're so right about the politicians. Capitol Hill's really not educated. I mean I tell my kids, and they ask the same questions, just look at Mark Zuckerberg and Sundar Pichai present to the government. They don't even know what an Android phone versus an iPhone is, nevermind what the Internet, and how this global economy works. This has become a makeup problem of the personnel in Capitol Hill. You guys see any movement? I'm seeing some change with a new guard, a new generation of younger people coming in. Certainly from the military, that's an easy when you see people get this. But a new generation of young millennials who are saying, "Hey, why are we doing this the old way?" and actually becoming more informed. Not being the lawyer at law-making. It's actually more technically savvy. Is there any movement, any bright hope there? >> I think there's a little hope in the sense that at a time when Congress has trouble keeping the lights on, they seem to have bipartisan agreement on this set of issues that we're talking about. So, that's hopeful. You know, we've seen a number of strongly bipartisan issues supported in Congress, with the Senate, with the House, all agreeing that this is an issue for us all, that they need to protect the country. They need to protect IP. They need to extend the definition of security. There's no argument there. And that's a very strange thing in today's D.C. to have no argument between the parties. There's no error between the GOP and the Democrats as far as I can tell. They seem to all agree on this, and so it is hopeful. >> Freedom has its costs and I think this is a new era of modern freedom and warfare and protection and all these dynamics are changing, just like Cloud 2.0 is changing application developers. Guys, this is a really important topic. Thank you so much for coming on, appreciate it. Love to do a follow-up on this again with you guys. Thanks for sharing your insight. Some great, profound statements there, appreciate it. Thank you very much. >> Thank you. >> Thanks for having us. >> It's been a CUBE Power Panel here from Palo Alto, California with Evan Anderson, Mark Anderson, and Phil Lohaus. Thank you guys for coming on. Power Panel: The Next Battleground in Industrial IoT. Security is a big part of it. Thanks for watching, this has been theCUBE. (energetic music)

>> Narrator: From the Cosmopolitan Hotel in Las Vegas Nevada, it's the CUBE. Covering Coupa Inspire 2019. Brought to you by Coupa. >> Welcome to the CUBE from Coupa Inspire'19 at the Cosmopolitan in Las Vegas. It's a pretty swanky place here. Very excited to welcome to the CUBE for the first time from Accenture, Jesse Hanger, Director of Capability Network Sourcing and Procurement. Jesse, welcome. >> Thank you, glad to be here. >> Oh, our pleasure to have you. So here we are day two of the main stuff going on here. All talking about Business Spend Management, BSM, this new category that Coupa is defining. We had the chance yesterday to speak with Rob Bernshteyn their CEO. Ravi Thakur was there. And it's one of the cool things that Coupa is doing is it's now, it's procurement, it's invoicing, it's expenses, it's payments, but it's also helping to redefine procurement and finance. >> And it is. I mean it's a huge shift when we think about, in industry, the same shift that Salesforce had years ago when it comes to CRM. When Coupa started talking about this, maybe two years ago, I had a little bit of a head-scratcher, I saw some of their slides and I thought to myself, that's a bit much to say you're going to change this, but the funny thing was, no one else had come up with a real definition of this. We finally had procurement technology that was at a level that you could capture this type of data and information, and it could go broader than just my MRP system and bills of materials, and to everything. Into your traveling expenses, into how you're sourcing things, into your basic inventory, and so it took me awhile to come around, but it was a slow journey for me, but clearly Business Spend Management is the future, what we look at with procurement. Because for a CPO, it can't just be about saving money or reducing costs, you have to start driving business and you can't drive business if all you do is save money. >> Exactly, and that's been something that I've learned a lot from in the last a week or so, alone, is how influential a CPO can be. This person can be, not just the money saver, it's shareholder value. >> Jesse: Right, bottom line growth of the business. >> Yes, and one of the things that I really appreciate is Coupa's done a great job the last two days of sharing the voice of the customer. Because I said to you, before we went live, I said, I don't as a marketer, I'm a little bias, but I don't think there's anything that's more brand validating than the voice of a successful customer that actually shows measurable business outcomes and they showed that this morning. That transcends any industry whether you're manufacturing or a retailer. >> Yep, and so when you do think about it from their customer's perspective, from our client's perspective at Accenture, this is not easy. Changing the way you do things and changing your overall procurement operating model it's not a easy stuff. There's a reason why there're so many big companies like Accenture that do this kind of work. Because it's hard and it's needed. We come in with a different perspective. Having a platform like Coupa to really initiate that transformation, to be the to be the lever that moves the company from where they were to where they want to be and where they need to be to be competitive in the market, it makes our job so much easier across the broader supply chain practice to really, not just make the change, but you know we use a big consulting word, to instantiate it, so that it stays. We don't make it better this year, we make it better moving forward. >> It's an evolution. >> Jesse: It is. >> But that requires the right mindsets to go from a tactical role, of managing budgets and things, to being strategic, being able to identify fraud detection, for example. >> Well and again, when they talk about their suite synergy and the fact that all of these components of the platform, they're not separate modules, they hate when we say modules, so it's the T&E module. All of these components because they are all natively integrated and the data structure is the same on the back end, things like the fraud detection become easy. for Coupa, not in other platforms. Again, the more things you are doing with Coupa, the more data you have and the more you can get the benefit from the broader ecosystem, from the over 1 trillion dollars in spend that's gone through, that's fully classified, coded, detailed, now all of that spend helps that fraud engine do a better job. >> The community that you mentioned they were saying, I think Rob Bernshteyn CEO Coupa said yesterday that since 2016, around the time they went public, it's been a 5X increase in the amount of spend being managed through the Coupa platform. Accenture has over 50 deployments of Coupa in 72 countries, you guys are also managing over 100 billion of that, but this community that they described yesterday, so eloquently, is very collaborative, allowing not just customers to leverage from peer's best practices, but suppliers, as well. Talk to us about some of the things, like the wave that they're riding now, in terms of this community intelligence, and how is it going to help Accenture really be able to help more companies get that visibility and that control of all their spend? >> So as an example, at Accenture when you look at the analyst reports, we do very well when it comes to our procurement practice and the spend that we're helping companies manage outside of a platform. So we've got I think the latest number I saw was like 1.8 trillion dollars that we have helped companies source in the last handful of years. >> Wow. So that is something that gives us a huge competitive advantage. The same thing is true of Coupa and you said, how they're riding this wave, honestly, I don't think they're riding the wave yet, I think the wave is still building and they're about to start riding it, I think that what we're going to see over the next one to four years is going to be a fairly significant shift in how that data is going to drive very discrete and concrete value to all the members of the community. >> Wow, that is exciting. One of the things that we talk about in terms of changes to the CPO's role and CFO are these ways of disruption. One of them is consumerization. And you know I think Raja talked about that this morning, it was talked about a number of times yesterday, we spoke about it on the program, we're consumers all the time. Whether we're getting up in the morning at a conference and going to buy a coffee at Starbucks, or something that we want to order from a vendor like an Amazon, we have this expectation that we can get it, or if you want to buy a car, we have all of this data that we've never had before, so empowered, but then we go to our work lives, and if we're in whatever role we're in, maybe I'm in marketing and I need to do a trip, so I've got to go and do it, travel expense, we want the same ease of consumerization. Your thoughts on Coupa Pay, the expansion of Coupa with open buying the AWS Marketplace, on bringing that consumerization in, do you think like, (hand clapping) yes, that's exactly what we need? >> The first place of bringing in the consumerization was really how Coupa was engineered, years ago. When we go back to before they had released numbers and it was fall of 2007, they had numbers like that, Coupa really did give you an experience that was like Amazon. It was, we used to say, we're going to bring your shopping experience from Sunday afternoon to your desk Monday morning. And as that happened, now you start to see a different piece and that is a greater uptake in terms of the usage of procurement platforms. So instead of people, it's easier to pick up the phone and call Bill over at my supplier and say I need a case of whatever, it's actually easier to do it in the platform, and I can still give Bill a call and go have a beer with him if I want to maintain the relationship, but I don't have to make every one of my transactions start with a phone call that necessitates three additional phone calls later on to check on the status. Instead, I can do it in the platform very quickly. When you expand that out to what now Coupa Pay is going to offer, especially when we look at our clients that have challenges with multiple financial systems, multiple banks that are processing their payments, as you shift it away from that multiple outlet situation and you can move it large, if not all of that, into Coupa Pay, you're streamlining things for dozens if not scores of people in your company and making it better for them. >> Some of the stats I saw on the press release about the amount of payment processes that are still manual, and still 40% of it by paper check? >> I've got one client that writes 40,000 paper checks a year. >> How receptive are they to digital transformation? >> They almost think it's too good to be true. When you when you talk to clients like that, Fortune 500 companies, and when we talk to clients like that and you tell them, what you heard from Coupa is true, they're not just selling you, or trying to sell you something, they're telling you how it really works for clients and we've seen it. I look at the last dozen or so clients I've worked with, last year and a half I was doing some analysis, 51 billion dollars, 50.8 billion dollars in revenues is the average for those clients. So big companies. >> Big, yes. Really big companies. And as we look at those, you'd be surprised at how many of them have challenges with a lot of manual processes, still. They're the top of their field but they still have those challenges. So bringing this to them as they are deploying Coupa and seeing what they can realize in terms of efficiencies, it actually makes my job really fun because everybody's going to be happy. >> That is a win-win. One of the things Rob said yesterday, I know a little bit about Rob, and some of his proudest moments are hearing clients articulate success and he goes, one of my favorite things that's going into, whether it's a 50 billion dollar a year company or not, where there's someone maybe in the C-suite that just is skeptical, and he goes, and that just takes one champion who sees this vision, to convert that person to, oh my gosh, we can have this crystal ball of visibility of everything, and really leverage that to drive digital transformation so that the business is faster to identify new products, new revenues, convert customers faster, increase customer lifetime value and, and, and, the impact there is exponential. >> Well and that's one of the reasons why I think our partnership with Coupa is so rich, is because Accenture is more a technology company. We're not just focused on accounting, we're not just focused on finance, we have a lot of technology resources. We usually have a lot of connection into the CIO and the IT suite of leadership. They're the ones that are typically the most skeptical. They've been through dozens of roll outs of different things and they've seen them go anywhere from 0% to 50% effective. So because we've got the relationships there and we can have these conversations with the CIO, and say, this is different. This is going to be a very different kind of program for you and we're coming in and telling you that we can work this together as your partner and be successful, and again, you get six months into it and the lights fully on at that point and they're on board. In fact next year we're looking forward to bringing one or two CIOs on stage with us at Inspire'20 to talk about it from an IT perspective. >> Awesome, well I look forward to hearing that. Jesse, thank you so much for joining me on the Cube this afternoon. Exciting stuff. Control, visibility, who doesn't want that? >> Exactly, it's good times. >> Excellent, thanks Jesse, appreciate it. >> Thank you, appreciate being here. >> For Jesse Hanger, I'm Lisa Martin. You're watching the Cube from Coupa Inspire'19. Thanks for watching. (upbeat music)

>> Live from Miami Beach, Florida, it's theCUBE covering VeeamON 2019 brought to you by Veeam! >> Welcome back to Miami, everybody. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante. We're here at the Fontainebleau Hotel. VeeamON day one of two-day coverage of the Veeam conference, very swaggy hotel. Dave Russell is here. He's the Vice President of NFI Strategy at Veeam. David, good to see you again. >> Good to see you. >> Thanks so much for coming onto theCUBE. >> Yeah, thanks for having me again. >> You're very welcome. So let's see, you're well over, let's see, a year out, just about a year out of Gartner. Right? >> Yeah, yeah. >> And so okay you've been injected with the Kool-Aid fully, I presume, right? >> There you go, in the green, yes. >> But we're still going to talk a little bit about the magic water, but before we get into that, talk about your first year here. >> Yeah. >> Your impressions. Do they meet, exceed your expectations? >> It exceeded my expectations, but I can honestly say I'm not doing what I thought I was going to be doing here, but it actually turned out to be better. The other thing I will honestly tell you is I'm now on Pacific Coast time at the moment. Arizona, we're too unsophisticated for Daylights Saving, right so I'm either Mountain or Pacific but I'm Pacific now. But by 10 a.m. my time, I pretty much what I thought I was going to do that day is out the window and I'm doing something else and it's fun though. I mean now especially with the investment that we had earlier in the year and the cash reserves we ended last year with, looking at a lot of partnership capabilities, looking at ecosystem activities, certainly involved with customer activity. We're redoing our marketing and how we're focusing our go-to-market so it's a whole variety of things that sort of change hourly. >> So on the, I think we just talked about the M&A side. You've always been a dot connector in your, right? Because you talk to all the vendors, you talk to all the customers and you could see the picture. You have a huge observation space so part of your job on strategy is to try to what? Figure out where the gaps are. >> Yeah. >> And then drive strategy around do we build, do we buy? Maybe you can talk about that a little bit. >> Yeah and it really does net down to what you said. It's a build/buy decision. It's an acceleration to market kind of decision and then the hard part is what are you willing to trade off and of course the real answer is as little as humanly possible. But you have to decide, just because you can do it, just 'cause you have the money doesn't necessarily mean you should pull the trigger. So if anything, it's curious because people like myself and a couple of my colleagues, we almost are more discerning. So we look at, okay, the technology, is it really viable? Do our due diligence, right? But then we also look at well, does this fit culturally? Is the integration point really there? Is the customer value really going to be significantly improved and if you cannot answer that very favorably, then keep the money. >> So you worked at IBM for a number of years, you worked at Gartner for a number of years. Now you're back working for a vendor. >> Yeah. >> Compare and contrast those roles. I mean Gartner, you do a lot of writing, you do a lot of traveling, you talk to a zillion people. I'm sure you talk to a lot of people here too, but you're coming at it from a very biased perspective whereas Gartner of course you're unbiased. You're serving the end customer. So talk about the difference in those two roles. >> So I approach it a little uniquely in that I'm biased. I mean I'm paid by a vendor, right? And so there's a certain inherent bias in there, but I go into a customer conversation and say "Maybe you shouldn't be using Veeam for certain things." So I'll give you an example. We have Unix capabilities with Solaris AIX. There are other vendors that do that even better than we do. They have rich application integration. If someone says that's my number one problem, honestly we're not your best choice. Now the reality is most of the world is moving towards more physical and virtual Windows and Linux. So I'll come in, say, a large enterprise and I'll say, "Okay, if you're like most shops," and I'll always undersell it. "Like probably 85% of your workload "is physical virtual Windows Linux." and they always interrupt me and go, "No, no, no, it's 92%." Like, "Okay, well we can help with that 92%." >> Yeah, yeah. >> The other 7%, I'm honestly going to tell you, we're not best of breed. >> Yeah that's a safe balance view that the AIX Solaris piece. >> Series. (Dave laughs) There's certain things. >> Yeah. >> We want to stick to our swim lane. We think it's a pretty wide lane, but there's no reason to come out of it. >> So your role as strategy, talk a little bit about how you're turning that strategy into action and specifics at Veeam. >> Yeah a big part of it has to do with cloud. >> I know that's the word that we've been talking about for a long, long time. So there's the aspirational aspect of Cloud and the operational. The aspirational is I want to be able to move in and out. I want mobility, I want the ability to exit. The operational is I want to be able to do this efficiently, meaning I want to be able to either send data to the cloud, my on-prem backup or I want to be able to protect SAAS-based workloads or infrastructure as a service workload so cloud-native workloads and then over time, I might want to be able to leverage that for something other than availability. So how can you rapidly make the data and only the portion of data that I need available to me when I need it? >> I was taking some notes during the key notes and I was just doing like a little, not really a tag cloud, but I was trying to identify as I heard them and grabbed them, the attributes of cloud data protection. I want to throw some out to you. You tell me. We'll play kind of word association, I guess. So I have fast recovery, API-based, open, simple, transparent, data-oriented, automated, cloud pricing, federated to accomodate the edge. Are these some of the attributes that we should associate with cloud data protection, maybe some of the things that I'm missing. How do you look at the attributes of a company and its products providing cloud data protection? >> Yeah so a big part of it, I actually like the phrase hybrid cloud even better than people say multi-cloud. The reason I like that is because hybrid presumes that you can have on premises as well. So like if it was the Dave and Dave company tomorrow, we'd probably be born in the cloud. Everything would be software as a service. We'd get some public cloud space. Now if we'd been in business for 20 years, we've got investments that we've made and we don't want to get rid of that any sooner than we have to. So hybrid cloud I like, but I think you nailed it in that what do every one of those attributes have in common? It's trying to get your most precious resource to you in a way that you want to consume it with as least amount of friction as possible. We want to reduce the aggravation associated with being able to access that rapidly. >> When you think about the customer conversations that you've had at Veeam and even going back to your Gartner days, I've always felt this notion of not hybrid, I see hybrid and multi-cloud as different. I've always looked at multi-cloud as multi-vendor. >> Yeah. >> Yeah I've got line of business, I've got shadow IT, I've got different IT projects and I've got multiple clouds and it's just, to me it was always less of a strategy than sort of this is where we are and now people need to put together a hybrid strategy. So IT's been asked to come clean up this mess as it always is. What's your take on the hybrid landscape and how we got here but more specifically, customer strategies when you consult with your customers? >> Yeah you're right that there's a lot of departmental buying, there's a lot of, in some cases, it's best of breed so I'm very willing to go look at multiple providers because I didn't sign up to go deploy the third best solution. Everyone wants what they think will be the most appropriate tool for them and rightfully so. So I think that's how we got, to your point, we didn't have a strategy that said I want 10 vendors. We arrived at an implementation choice that resulted in 10 vendors being deployed and then to your point further, then we had to layer on something on top of that. That's really where we come in and simple as it sounds, we really want to promote choice, choice of infrastructure, choice of cloud, choice of hypervisor, choice of operating system. >> So great discussion vector is the best of breed versus sort of integration. >> Yeah. >> And my question is that's been a decades-long. >> Yeah. >> Sort of trade-off that people have made. You see it in the software business, the hardware business and all through the industry. Is the API economy changing that. Can you be both, I mean Veeam, let's agree. Veeam is a best-of-breed provider. While your portfolio's growing, you're a billion-dollar company, you take a company like Dell who's got this ridiculously large portfolio. They can come into a customer and say well even with services or at IBM, we can wrap the big blue blanket around you and integrate everything. With the API economy, does that change the game on that argument of best of breed versus integration and convenience? >> It's a nuanced answer. The answer is a little yes and a little no. >> It depends, right? >> Let me decompose that because that's a cop-out, but the "it depends" aspect is really, APIs are wonderful to create an ecosystem and other integration points. If that's about offering your expandability to do something, that's a positive. If that really means that well because I can't deliver what you need, you got to go and write it yourself, that is a negative. So if the API is leveraging something for even greater value but beyond what the tools are originally designed to do, I think that's net positive, but if you have to exploit the API to just to get the product to work, why did I buy your product when I have to go hire someone to write code to work on your product? That's, you don't want that business. >> Okay so the last Gartner Magic Quadrant that came out was one that you sort of spearheaded back in 2017. It was like this perfect storm of backup analysts leaving Gartner and so there's been a little bit of delay in terms of the new one coming out which is coming our shortly as I understand it, but one of the observations that you can make if you look at the 2016-2017 Gartner Magic Quadrant is that Veeam moved from lower right to upper right which is rare. Can you explain that a little bit? You were saying that it usually goes in a different pattern. Elucidate, please. >> Yeah. Yeah so the magic in the Magic Quadrant is if you could actually jump from one quadrant to straight to leaders and that would be a very atypical progression. Usually it's a backwards Z. You come into the lower left, probably get over to the lower right, fall back, but go up to the upper left and then maybe you get to leaders in the upper right. The magic part in Veeam, the thing that they were able to do is go from visionary lower right to leader upper right. >> Okay and why do you think they were able to do that? I mean there are numerous attributes, but presumably 350,000 I think is the number of customers helped and so you've got a lot of references and proof points, the technology itself, but it's rare. Why do you think Veeam has been able to succeed in that regard? >> I think it's because Veeam has been good about getting answers to the most pressing problems. Again Veeam doesn't do everything. It doesn't support every single operating system, but the vast majority of the concentration of where customer issues are and where customer environments are getting deployed at, we can address very well and actually this weekend, I got here Friday night. So all day Saturday, all day Sunday and yesterday 'til 5 p.m. I took our SE training and so I've deployed Veeam, worked with active directories, all kinds of things for 72 hours basically and it was really that easy to use. In fact, my most difficult thing is I stayed in class until 6:30 at night because I'd never done active directory. I've never been an exchange admin before so I had to kind of come up to speed on those tools a little bit, but once I got that, the product was incredibly powerful, but also very intuitive. So you still have a little bit of that independent analyst DNA in you so I'm going to ask you to try to put that independent hat on. When you think about Veeam's traditional base of SMB, they're very successful there, obviously superglued itself to the virtualization trend. The last couple of years, Veeam has tried to move up-market, develop some relationships with some large players and has had some success there. Is the product well-suited for that larger enterprise and where do you see that going in terms of the up-market progression? >> Yeah so in theory, that's what I'm here to drive, the enterprise word is in my title, but in reality I focus more broadly than that. But if I just think about enterprise, I ran the numbers last week and company inception to date, we've actually derived over $2 billion of software-only revenue from the enterprise market and that's been accelerating. Now in 2017-18 and the first quarter of this year, almost $1 billion. So we're moving and we're moving fast. We had our sales kick off like most companies do. January, go to sales kick off and Ratmir says, "Hey don't chase just the big deals, the $2 million deals. "We've never sold a $2 million "without having a $200,000 deal first." The very next week, we got a $2 million deal on the first paper so he shot low. He should've said five million, but the interesting thing about Veeam and to answer your question, I think we resonate with the kind of challenges a large enterprise has. We allow them to move at their own scale if they want to move in a very large fashion, they can with Veeam. I would honestly tell them move as appropriate for you. As assets age, as you're willing to take on the change in an environment, do so, but I think Veeam is interesting. It's the same piece of software that I installed on my laptop this weekend that can also go to a Fortune 100 company. The same piece of software that manages 50,000 agents, we have at one shop, 50,000 Windows agents. We can do that with same code base and the only thing that's different is we just horizontally scale out how we deploy the capacity and then how we deploy the mover agents. >> I tweeted out this morning, Ratmir was standing in front of a chart with all these features and over the time and that's been part of the hallmark of Veeam is not checkbox features but real substantive features and you've had a consistent progression. Even Ratmir said, we don't have a big long-term roadmap that we share with our customers even internally. Yeah we have a direction and a vision, but very focused, almost like a bit of an Agile development methodology but the point is that, and you see that some companies are really good at this, some companies, not so good at this, but just consistently delivering features that are in-demand, that customers want, listening to their customers and just nailing it and that seems to be the hallmark of Veeam and as they say, some companies just don't have that in their DNA. Your thoughts on that? >> Yeah I think what it really comes down to is at the end of the day, every developer thinks like a customer and they do that because they spend a lot of time on our Veeam forums and I'll be honest, when I was a mainframe backup developer, I didn't talk to that many customers. I was just writing code and I didn't know how people were actually putting the product to use in production. I didn't always know what feature might be most helpful for them. >> You were guessing. >> I was trying to think of the art of the possible, hopefully an educated guess, but I was really just trying to say what might be good, what might be of resonance versus actually having someone goes on a forum and says Veeam, what I would like you to do is X. That's one of the reasons why we do have, to your point, we don't have a 10-year roadmap where we say this feature is coming in 12 months, this feature is coming in 24 months. It's fluid and in some cases, we actually moved up delivering our physical agent management by a year because we started selling more and more of those and people said I need that feature functionality faster. We're willing to trade-off some of our other feature functionality. So if we can be, as long as we can continue to respond to the market, I think we're well-positioned. >> How does a capability like that surface itself? Obviously by talking to customers, but how does it get into the development pipeline so quickly? >> Yeah well in some cases, we've got a huge amount of not just, our part of R&D. It's the research, it's experimentation, it's incubation of new things. So when we find that sweet intersection point, then we can quickly operationalize that. In other cases, we just have to be nimble. We have to react fast. >> Is it a command and control culture though where somebody says okay this is what we're doing or is it more sort of the team gets together and says oh this really makes sense based on what the customers are telling us, let's go. How does that decision get made? >> Yeah well ultimately it is a command and control in the sense that our co-founder, one of our co-founders runs sales and marketing. Our other co-founders runs R&D and they ultimately get sign-off on their respective areas, but it is collaborative in the sense of we do bring forward, here's what we see in market, here's what see in our customer forums. Here's what our ecosystem of partners are telling us, here's our view of the top five things we ought to go do. >> I was struck by the other slide that Ratmir had. It was the $15 billion slide and it was probably, backup and recover was maybe I don't know seven out of the 15 if I remember, but there were all these other segments. It was sort of analytics and disaster recovery and data management, all new pockets of opportunity. $15 billion today, obviously growing with especially the cloud. How do you see that landscape and how does that affect the way you look at strategy? >> Yeah so I actually put that bubble chart together. >> Oh, I like it. >> The rationale between the bubbles, we have core, we put backup in the middle because that's what we do but also that's how we ingest data and now we can do other things around it. So the reason for those bubbles and they were of varying sizes and the bubbles were sort of in and out of to varying degrees the main backup bubble according to how much intersection we thought as a company we could have with that. Where we thought we could add value, where we thought there was an ecosystem potential. So for example, analytics. We're not going to become the next best analytics company tomorrow, not even years from now. We could partner and we can provide data and we get better access to data to be able to do that. So we'd want to facilitate that. In other cases, maybe we really do want to go own and acquire. >> Well and so to your earlier comments there, I didn't use the term, the phrase land and expand, but that's clearly what you guys are doing starting with the $200,000 sale and growing it to a $2 million sale. So those bubbles are potentially cohort sales. >> Yes. >> That you can sell sort of like bananas in bunches I like to say, right? >> Yeah. And part of that is who do you sell that to. And so if you're able to go and address some of those ancillary bubbles or markets, now you've got a different entree point into the organization. If you're already involved with an organization, now you can offer more value because you can get more out of your data that you've already protected. So it opens up new conversations for us to have. It opens up entirely new buying centers for us too. >> Well how is the role of whom you sell to changing? I mean it was backup admin historically, right or maybe a Veeamware admin. Veeam admin. How is that changing? >> So greatest example I would tell you are events. So we acquired a company last January or a year ago January called N2W Software. So they're predominantly at Amazon re:Invent conferences. You go to Amazon re:Invent and no one's heard of Veeam and if anyone's heard of either of the two companies, it's definitely N2WS and someone's seen it in the marketplace. That demographic tends to be totally different from the demographic if you go to the on-premises data center type of conference where they have heard of Veeam and it's a very different sort of mindset. To your point, they grew up in a very different landscape. Now instead of someone who's well-steeped in server storage and networking and maybe majored in one, possibly two of those things, now you've got a generalist where he or she is probably in their 20s, has a very different point of view of what it should take to get something working and has a very different view of how they want to be sold to, how you can go and reach them. >> So at the cloud show, there might be a development persona. >> Yes. >> That you're selling to. Obviously VMWare, VMWorld, we know what that is. It's IT guys, right, is the predominant and how do you see cloud changing that? Is it cloud architects or sort of cloud leaders? CTOs increasingly? Data Protection becomes more and more important to digital business. So how are you seeing that role change due to cloud? >> So right now we have to basically have more touchpoints. Our typical legacy fan of our customer, our customer base, our product's sweet spot still remains and it's in some cases will pull us into the cloud. In other cases, we have to go talk to someone that's entirely different. But again, that's more of an administrative view. But to your point, going up the stack now, if you go to the not even Vice President of Infrastructure, you go to the CIO, he or she says, "I am tired of thinking about boxes. "I am tired of thinking about where this resides. "I want to think business outcome." So for us that's actually a great conversation because it all comes back to data. That's what we're in the business of doing. We capture, protect and move data. >> So that brings it back to strategy. We got to run, but summarize in your words, just sort of the strategy of Veeam and where you see this whole thing going. >> Yeah I will simplistically say it's more of the same. We want to continue to offer what we think is a best of breed solution for on-prem and increasingly cloud availability, but also we want to offer real customer value in terms of now being able to leverage that data, get more value out of that whether that's DevOps, running analytics against that, security test patch, whatever it may be, we want to be able to give you just the data you need, so have granularity, and offer speed and ease of use to do that. >> So as data becomes more and more important, you're seeing companies go beyond backup, trying to get more out of there, their backup, moving to data protection, data management, not just an insurance policy anymore. Dave Russell, thanks very much for coming to theCUBE. It was great to have you. >> Thank you so much. >> You're welcome. All right, keep it right there, everybody. We'll be back with Peter Burris as my cohost. We're at VeeamON Live from Miami. You're watching theCUBE. (upbeat music)

>> Live, from Las Vegas, it's theCUBE. Covering VMworld 2018. Brought to you by VMware and its eco-system partners. >> Welcome back, this is theCUBE's coverage of VMworld 2018, three days live coverage, kicking-off day two. On the right set, Michael Dell himself is talking. But I'm even more excited, because when we get to talk to the users here. That's what we love doing; talking, peers talking to their peers. I'm Stu Miniman, my guest host for this segment is, Joep Piscaer. Who's actually a user himself, and joining me, first time on the program, is PJ Romero who is the Principal IT Engineer at Turnitin, out of Oakland. >> Out of Oakland. >> Thanks for joining us. >> Thanks for having me, Stu. >> All right, so, PJ, first of all, your forth year at VMworld. Give us your initial impressions, and what brings you back to VMworld? >> VMworld is just getting, re-upping my education, so I'm learning more stuff, seeing what's new on the horizon, to get implemented in my situation. >> All right, you talk about learning more stuff. Tell us about Turnitin. Explain what this is and, you know, I think it'll resonate with a lot of our audience. >> Right. Turnitin is plagiarism detection software. So we're probably in most major universities throughout the world, really big in Europe and here at major universities. >> Okay. >> And we are also in the high school, high school down. >> Okay. Ya, I was wondering about that. My daughter actually starts high school tomorrow, so, make sure she understands that this is serious stuff. I mean, talk about education, I mean, heck, in this community when you talk about certification people are always worried about you know, tests getting out. >> Exactly. >> Things like that. >> Exactly. >> We take education seriously. >> Exactly. >> As a community, as we should. And your role at the company? >> My role; I'm the Principal IT Engineer, so basically I architect the corporate infrastructure, aside from the Turnitin papers. So I manage global infrastructure. >> Before we get into, you know, kind of the infrastructure itself, the business itself. How long has it been around? How long have you been in there? And what is the kind of, you know, mobile-web, digital-transformation impact your business? >> Oh, so everything's mobile now. Everything's on the web. We've migrated out there. We've moved out to the Cloud. And how it's migrated us, so, Turnitin's been around for about 20 years. We just uploaded our billionth paper a few weeks ago. So we have about nine petabytes worth of data to pull from. >> Oh wow. >> So you can imagine how we're getting that from our data centers into the Cloud. With nine petabytes, it's been a challenge. So recently we virtualized some VMware and to make that transition, we had rows and rows of servers to move them out; to virtualize. >> So nine terabytes. That's a lot of data. >> Petabytes. >> Petabytes even. >> Petabytes, ya. >> So tell me, how does that work from a tech perspective? What are you, what are you running, what's that tech stack look like? >> Well, Turnitin is actually a home-grown infrastructure from the ground for the storage. So it's highly available, it's highly redundant, we have multiple data centers new with the GDPR requirements. Now we have data centers in Europe, and we're moving all over the country. We're looking at EMA, APAC, and then South America. (laughing) Get it out there somewhere. >> So you're running your own data centers? >> Yes. >> I presume. >> Yes, we're running our own data centers. >> What does that mean for your hybrid cloud strategy? How much is in your data center? What are you considering to move to the Cloud? How does that impact your business? >> So right now we're probably 75%, 25% and, you know, with the Cloud being elastic as it is, as term papers come up, we're spinning them out. You know, so we're moving. >> Great, okay. So you're virtualized. Do you know what percentage of your applications are virtualized? And maybe walk us through a little bit about the stacks that you have, both on premises, as well as who you use for a public cloud. >> Oh, so we're using AWS and we're also, I think we use, some Google stuff. And Desrrve for some of the development. So we're using all of them, basically; to make sure we're fluid that way. We also do, all the applications, all the web servers are virtualized, and put up in the Cloud. But the main guts of it is still on premise. >> Okay, and what's that stack look like on premises? Who are you using today? >> As far as. >> Like, your whole infrastructure stacks? >> All the infrastructure has been super micro. >> Okay. >> Ya. >> But you're using like, an HCI solution? >> The corporate is. >> Okay. >> The corporate. Ya, I manage the corporate infrastructure. >> Right. >> Ya, we use HCI Solution. >> So whose are you're using. >> I'm using Nutanix. >> Okay. >> Ya. >> Great. So why don't you tell us a little bit about how you got to Nutanix. What apps you use that for? What apps you don't use that for? Maybe help to ease that out a little bit for us. >> Ya, of course. So I have the corporate infrastructure started out when I got there three years ago. I had server-sprawl. I had all physical serves. They weren't virtualizing yet and I got in there and was like, why not? So I did a small PoC with a couple of servers in a NAS that I built homemade and put VMware on it and said, look, this stuff works great. I can move stuff back. I can kill this box. And they were like, wow, that's pretty cool! And then I got a business intelligence project for the financial services. So they were doing some really high-end modeling based on Oracle database, and needed something redundant, powerful and fast to deploy. Well, that was the problem. It was going to take six weeks to get servers in, get them configured, stacked. I got Nutanix in within two weeks. So got Nutanix in there, I think I spent more time convincing them that this is really a to you box and I'm going to stick all our stuff in there, we started out with the three node unit, and got VMware on there to show them what I was doing, and then we deployed our Oracle stack in no time. >> So tell a little about the cost-model behind it. Has it changed the way, using HCI, has it changed the way you do business? Has is made it easier, cheaper faster? >> It's made it cheaper and faster. For me, easier, I don't say the easy part too much 'cause then they wonder what I'm doing. But it's really easy. (laughing) >> Yeah, that's interesting. When you talk about you've had homegrown stuff before. >> Yeah. >> Verses now. I've talked to some Nutanix customers they say, like hey, I got my nights and weekends back. >> Yeah. >> I don't have to worry about so many of the other pieces, maybe you talk a little bit about that dynamic. Did you have any change in personnel? Or who manages what after, or is it you? >> So I'm it. But with the ability to put Nutanix in there and ease of use, I give them access to the dashboards and show them how things work. It's been really simple, especially for some of my newer guys, the younger sys admins, who don't understand virtualization and it's still kind of magic for everybody. But now they got one dashboard. Green heart means good. Everything else, look at it. >> So you're saying you're the wizard now. >> I'm the wizard. >> Pay no attention behind the curtain. (laughing) It got really easy, but I'll just keep that behind. I can do more stuff and I'll just be the superhero. >> Yeah, exactly, exactly. It made my monitoring easier for them and my guys love it. They really love it. >> Tell a little bit about how you're using Nutanix. So Nutanix started-out as a virtualization, pure HCI company, but they've broadened their portfolio. So tell us a little bit about how you're using the Nutanix solution inside of your data centers. >> Right, so originally I put Nutanix with a virtualization product or the financial product. I was able to get a forth node. So I was able to use their analytics in there and say, hey, we're going to run out of space. So I'm running 47 machines on four nodes and I still have high redundancy. But I had no back-ups. So what do we do? So I got a second box, I put it inside of one of my other data centers and used that for replication in the back. And now with the Zy coming out, I'm going to start pushing that up to the Cloud, and start moving my single data center, toothpick as it were, it's going to be in the Cloud quickly. >> And you mentioned Oracle's, the application that catalyzed this. All certified, didn't have any issues. >> No issues. >> That's awesome. >> It was great. >> Those of us in the virtualization community, how many years did we spend just virtualizing Oracle, let alone, every new platform. It's challenging. >> Exactly. >> Your peers? All clear? They don't have to worry about it? >> Oh ya, they love it, they love it. They can't believe I got it all in the to you box. I like to take the picture of it and say, here's their stuff. I don't need this big stack, I just need the little box. (laughing) >> So basically, your whole operational model changed I'm guessing? You're not spending as much time anymore on operational issues. >> No. It's more of architecture now. We start moving the Cloud. I'm getting away from virtualizing more of the applications that we use. We just use basic active directory and DNS and that stuff. So it's all fine but, I'm going to start moving it so I push the button it will be in the Cloud, and I can literally lose my data center. >> Talk a little bit about the Zy. We've heard a lot the vision, so what's the roadmap for you to kind of embrace, adopt that? What's interesting to you about it? >> For me, I'm going to take the financial stack and really moving it right now in the tip is re-iping, it's a lot of back-end work. With the Zy, it should be a click and I mean, I've seen the database, so we're talking right now to get that done. It should be a click of the button and it's going to spin me up an AWS. So that's where I'm going next with my next project. That looks pretty cool. >> Okay, the rest of your applications, will you expand your Nutanix environment? Is this something to help you deal with that hybrid cloud environment? >> Yeah. >> What does the future look like? >> If I have my way, as I age-out my remote sites, we'll be putting more Nutanix out there. And then I can do more three to one back-ups. >> That frees-up even more time to be spending on future architecture. >> Yep, exactly. >> Instead of just the operational stuff. >> Yep, I'm making it so we can lose any leg and we're going to be fine. >> One of the things that everyone's poking at at this show is that whole multi-cloud environment. We said, I can make my data center kind of simple today. >> Yeah. >> But, multi-cloud, most people, at least I talked to, it's not simple. The Cloud is a little bit complex, it's not just swipe a credit card anymore. Managing between multiple environments, depending on how many clouds you have. What have you seen today? What would you like to see get even better over time? >> I'd like to see where Nutanix is going really, with the single-environment. I want to go one-spot. And right now I'm going to one-spot for my virtualization and all my on-prem stuff, but as I move up to the Cloud and spin stuff up, I want to go to the same spot. I don't want to have to think about it so much. Simple is good for me. I'm big in the KISS system. (laughing) >> Absolutely. Keep it simple. >> That's right. >> Engineering design, absolutely. >> I imagine your role is changing as well, right? It is becoming simpler, you get to spend more time on new projects. How is your role changing as an IT Engineer? >> I'm getting to think more. I'm not reactive anymore at all. When I got there it was a very reactive environment. And now it is more on design and how we can make sure we can tighten-up securities. We went through a whole bunch of new sox audits. And it's made it simple. It's made it simple for me. We're all in compliance now within the physical hardware and security and now, some of the other touches I'm able to think about and get those implemented. >> So outside of the Nutanix stuff, at VMworld, what kind of things are you digging into, learning, anything excite you that you either heard from your peers or announcements or sessions you've been in? >> VDI is still exciting to me. I'm still looking at those projects, and I have just enough space to do a PoC on my stuff, so I'm talking to management about that. As soon as I can show them they can do anything, from a web browser, I'd like to give them Chrome Books, and say, have a nice day. (laughing) >> It's funny you say that, because most people think of the HCI space and like, you start with VDI. And now you're like, oh well now I've got some fair capacity, I'm guessing. I can put in environment, manage it. Yeah, do some of the dynamics inside the company sounds like they're some of the bigger challenges. Always for VDI, has been a challenge. >> Yeah, it's always a challenge but so far, everything I've said's worked for them, so I've got a good trust-base. >> PJ Romero, really appreciate you talking about Turnitin. No plagiarism at this show, right? (laughing) >> That's right. We'll check. (laughing) >> PJ Romero, Turnitin, really appreciate you joining us. For you, Piscaer, I'm Stu Miniman. Lots more coverage. Wall-to-wall, here at VMworld2018. Thanks so much for watching theCUBE. (techno music)