>>Hello and welcome today's cube presentation of eight of us startup showcase. I'm john for your host highlighting the hottest companies and devops data analytics and cloud management lisa martin and David want are here to kick it off. We've got a great program for you again. This is our, our new community event model where we're doing every quarter, we have every new episode, this is quarter three this year or episode three, season one of the hottest cloud startups and we're gonna be featured. Then we're gonna do a keynote package and then 15 countries will present their story, Go check them out and then have a closing keynote with a practitioner and we've got some great lineups, lisa Dave, great to see you. Thanks for joining me. >>Hey guys, >>great to be here. So David got to ask you, you know, back in events last night we're at the 14 it's event where they had the golf PGA championship with the cube Now we got the hybrid model, This is the new normal. We're in, we got these great companies were showcasing them. What's your take? >>Well, you're right. I mean, I think there's a combination of things. We're seeing some live shows. We saw what we did with at mobile world Congress. We did the show with AWS storage day where it was, we were at the spheres, there was no, there was a live audience, but they weren't there physically. It was just virtual and yeah, so, and I just got pained about reinvent. Hey Dave, you gotta make your flights. So I'm making my flights >>were gonna be at the amazon web services, public sector summit next week. At least a lot, a lot of cloud convergence going on here. We got many companies being featured here that we spoke with the Ceo and their top people cloud management, devops data, nelson security. Really cutting edge companies, >>yes, cutting edge companies who are all focused on acceleration. We've talked about the acceleration of digital transformation the last 18 months and we've seen a tremendous amount of acceleration in innovation with what these startups are doing. We've talked to like you said, there's, there's C suite, we've also talked to their customers about how they are innovating so quickly with this hybrid environment, this remote work and we've talked a lot about security in the last week or so. You mentioned that we were at Fortinet cybersecurity skills gap. What some of these companies are doing with automation for example, to help shorten that gap, which is a big opportunity >>for the job market. Great stuff. Dave so the format of this event, you're going to have a fireside chat with the practitioner, we'd like to end these programs with a great experienced practitioner cutting edge in data february. The beginning lisa are gonna be kicking off with of course Jeff bar to give us the update on what's going on AWS and then a special presentation from Emily Freeman who is the author of devops for dummies, she's introducing new content. The revolution in devops devops two point oh and of course jerry Chen from Greylock cube alumni is going to come on and talk about his new thesis castles in the cloud creating moats at cloud scale. We've got a great lineup of people and so the front ends can be great. Dave give us a little preview of what people can expect at the end of the fireside chat. >>Well at the highest level john I've always said we're entering that sort of third great wave of cloud. First wave was experimentation. The second big wave was migration. The third wave of integration, Deep business integration and what you're >>going to hear from >>Hello Fresh today is how they like many companies that started early last decade. They started with an on prem Hadoop system and then of course we all know what happened is S three essentially took the knees out from, from the on prem Hadoop market lowered costs, brought things into the cloud and what Hello Fresh is doing is they're transforming from that legacy Hadoop system into its running on AWS but into a data mess, you know, it's a passionate topic of mine. Hello Fresh was scaling they realized that they couldn't keep up so they had to rethink their entire data architecture and they built it around data mesh Clements key and christoph Soewandi gonna explain how they actually did that are on a journey or decentralized data >>measure it and your posts have been awesome on data measure. We get a lot of traction. Certainly you're breaking analysis for the folks watching check out David Landes, Breaking analysis every week, highlighting the cutting edge trends in tech Dave. We're gonna see you later, lisa and I are gonna be here in the morning talking about with Emily. We got Jeff Barr teed up. Dave. Thanks for coming on. Looking forward to fireside chat lisa. We'll see you when Emily comes back on. But we're gonna go to Jeff bar right now for Dave and I are gonna interview Jeff. Mm >>Hey Jeff, >>here he is. Hey, how are you? How's it going really well. So I gotta ask you, the reinvent is on, everyone wants to know that's happening right. We're good with Reinvent. >>Reinvent is happening. I've got my hotel and actually listening today, if I just remembered, I still need to actually book my flights. I've got my to do list on my desk and I do need to get my >>flights. Uh, >>really looking forward >>to it. I can't wait to see the all the announcements and blog posts. We're gonna, we're gonna hear from jerry Chen later. I love the after on our next event. Get your reaction to this castle and castles in the cloud where competitive advantages can be built in the cloud. We're seeing examples of that. But first I gotta ask you give us an update of what's going on. The ap and ecosystem has been an incredible uh, celebration these past couple weeks, >>so, so a lot of different things happening and the interesting thing to me is that as part of my job, I often think that I'm effectively living in the future because I get to see all this really cool stuff that we're building just a little bit before our customers get to, and so I'm always thinking okay, here I am now, and what's the world going to be like in a couple of weeks to a month or two when these launches? I'm working on actually get out the door and that, that's always really, really fun, just kind of getting that, that little edge into where we're going, but this year was a little interesting because we had to really significant birthdays, we had the 15 year anniversary of both EC two and S three and we're so focused on innovating and moving forward, that it's actually pretty rare for us at Aws to look back and say, wow, we've actually done all these amazing things in in the last 15 years, >>you know, it's kind of cool Jeff, if I may is is, you know, of course in the early days everybody said, well, a place for startup is a W. S and now the great thing about the startup showcases, we're seeing the startups that >>are >>very near, or some of them have even reached escape velocity, so they're not, they're not tiny little companies anymore, they're in their transforming their respective industries, >>they really are and I think that as they start ups grow, they really start to lean into the power of the cloud. They as they start to think, okay, we've we've got our basic infrastructure in place, we've got, we were serving data, we're serving up a few customers, everything is actually working pretty well for us. We've got our fundamental model proven out now, we can invest in publicity and marketing and scaling and but they don't have to think about what's happening behind the scenes. They just if they've got their auto scaling or if they're survivalists, the infrastructure simply grows to meet their demand and it's it's just a lot less things that they have to worry about. They can focus on the fun part of their business which is actually listening to customers and building up an awesome business >>Jeff as you guys are putting together all the big pre reinvented, knows a lot of stuff that goes on prior as well and they say all the big good stuff to reinvent. But you start to see some themes emerged this year. One of them is modernization of applications, the speed of application development in the cloud with the cloud scale devops personas, whatever persona you want to talk about but basically speed the speed of of the app developers where other departments have been slowing things down, I won't say name names, but security group and I t I mean I shouldn't have said that but only kidding but no but seriously people want in minutes and seconds now not days or weeks. You know whether it's policy. What are some of the trends that you're seeing around this this year as we get into some of the new stuff coming out >>So Dave customers really do want speed and for we've actually encapsulate this for a long time in amazon in what we call the bias for action leadership principle >>where >>we just need to jump in and move forward and and make things happen. A lot of customers look at that and they say yes this is great. We need to have the same bias fraction. Some do. Some are still trying to figure out exactly how to put it into play. And they absolutely for sure need to pay attention to security. They need to respect the past and make sure that whatever they're doing is in line with I. T. But they do want to move forward. And the interesting thing that I see time and time again is it's not simply about let's adopt a new technology. It's how do we >>how do we keep our workforce >>engaged? How do we make sure that they've got the right training? How do we bring our our I. T. Team along for this. Hopefully new and fun and exciting journey where they get to learn some interesting new technologies they've got all this very much accumulated business knowledge they still want to put to use, maybe they're a little bit apprehensive about something brand new and they hear about the cloud, but there by and large, they really want to move forward. They just need a little bit of >>help to make it happen >>real good guys. One of the things you're gonna hear today, we're talking about speed traditionally going fast. Oftentimes you meant you have to sacrifice some things on quality and what you're going to hear from some of the startups today is how they're addressing that to automation and modern devoPS technologies and sort of rethinking that whole application development approach. That's something I'm really excited to see organization is beginning to adopt so they don't have to make that tradeoff anymore. >>Yeah, I would >>never want to see someone >>sacrifice quality, >>but I do think that iterating very quickly and using the best of devoPS principles to be able to iterate incredibly quickly and get that first launch out there and then listen with both ears just >>as much >>as you can, Everything. You hear iterate really quickly to meet those needs in, in hours and days, not months, quarters or years. >>Great stuff. Chef and a lot of the companies were featuring here in the startup showcase represent that new kind of thinking, um, systems thinking as well as you know, the cloud scale and again and it's finally here, the revolution of deVOps is going to the next generation and uh, we're excited to have Emily Freeman who's going to come on and give a little preview for her new talk on this revolution. So Jeff, thank you for coming on, appreciate you sharing the update here on the cube. Happy >>to be. I'm actually really looking forward to hearing from Emily. >>Yeah, it's great. Great. Looking forward to the talk. Brand new Premier, Okay, uh, lisa martin, Emily Freeman is here. She's ready to come in and we're going to preview her lightning talk Emily. Um, thanks for coming on, we really appreciate you coming on really, this is about to talk around deVOPS next gen and I think lisa this is one of those things we've been, we've been discussing with all the companies. It's a new kind of thinking it's a revolution, it's a systems mindset, you're starting to see the connections there she is. Emily, Thanks for coming. I appreciate it. >>Thank you for having me. So your teaser video >>was amazing. Um, you know, that little secret radical idea, something completely different. Um, you gotta talk coming up, what's the premise behind this revolution, you know, these tying together architecture, development, automation deployment, operating altogether. >>Yes, well, we have traditionally always used the sclc, which is the software delivery life cycle. Um, and it is a straight linear process that has actually been around since the sixties, which is wild to me um, and really originated in manufacturing. Um, and as much as I love the Toyota production system and how much it has shown up in devops as a sort of inspiration on how to run things better. We are not making cars, we are making software and I think we have to use different approaches and create a sort of model that better reflects our modern software development process. >>It's a bold idea and looking forward to the talk and as motivation. I went into my basement and dusted off all my books from college in the 80s and the sea estimates it was waterfall. It was software development life cycle. They trained us to think this way and it came from the mainframe people. It was like, it's old school, like really, really old and it really hasn't been updated. Where's the motivation? I actually cloud is kind of converging everything together. We see that, but you kind of hit on this persona thing. Where did that come from this persona? Because you know, people want to put people in buckets release engineer. I mean, where's that motivation coming from? >>Yes, you're absolutely right that it came from the mainframes. I think, you know, waterfall is necessary when you're using a punch card or mag tape to load things onto a mainframe, but we don't exist in that world anymore. Thank goodness. And um, yes, so we, we use personas all the time in tech, you know, even to register, well not actually to register for this event, but a lot events. A lot of events, you have to click that drop down. Right. Are you a developer? Are you a manager, whatever? And the thing is personas are immutable in my opinion. I was a developer. I will always identify as a developer despite playing a lot of different roles and doing a lot of different jobs. Uh, and this can vary throughout the day. Right. You might have someone who has a title of software architect who ends up helping someone pair program or develop or test or deploy. Um, and so we wear a lot of hats day to day and I think our discussions around roles would be a better, um, certainly a better approach than personas >>lease. And I've been discussing with many of these companies around the roles and we're hearing from them directly and they're finding out that people have, they're mixing and matching on teams. So you're, you're an S R E on one team and you're doing something on another team where the workflows and the workloads defined the team formation. So this is a cultural discussion. >>It absolutely is. Yes. I think it is a cultural discussion and it really comes to the heart of devops, right? It's people process. And then tools deVOps has always been about culture and making sure that developers have all the tools they need to be productive and honestly happy. What good is all of this? If developing software isn't a joyful experience. Well, >>I got to ask you, I got you here obviously with server list and functions just starting to see this kind of this next gen. And we're gonna hear from jerry Chen, who's a Greylock VC who's going to talk about castles in the clouds, where he's discussing the moats that could be created with a competitive advantage in cloud scale. And I think he points to the snowflakes of the world. You're starting to see this new thing happening. This is devops 2.0, this is the revolution. Is this kind of where you see the same vision of your talk? >>Yes, so DeVOps created 2000 and 8, 2000 and nine, totally different ecosystem in the world we were living in, you know, we didn't have things like surveillance and containers, we didn't have this sort of default distributed nature, certainly not the cloud. Uh and so I'm very excited for jerry's talk. I'm curious to hear more about these moz. I think it's fascinating. Um but yeah, you're seeing different companies use different tools and processes to accelerate their delivery and that is the competitive advantage. How can we figure out how to utilize these tools in the most efficient way possible. >>Thank you for coming and giving us a preview. Let's now go to your lightning keynote talk. Fresh content. Premier of this revolution in Devops and the Freemans Talk, we'll go there now. >>Hi, I'm Emily Freeman, I'm the author of devops for dummies and the curator of 97 things every cloud engineer should know. I am thrilled to be here with you all today. I am really excited to share with you a kind of a wild idea, a complete re imagining of the S DLC and I want to be clear, I need your feedback. I want to know what you think of this. You can always find me on twitter at editing. Emily, most of my work centers around deVOps and I really can't overstate what an impact the concept of deVOPS has had on this industry in many ways it built on the foundation of Agile to become a default a standard we all reach for in our everyday work. When devops surfaced as an idea in 2008, the tech industry was in a vastly different space. AWS was an infancy offering only a handful of services. Azure and G C P didn't exist yet. The majority's majority of companies maintained their own infrastructure. Developers wrote code and relied on sys admins to deploy new code at scheduled intervals. Sometimes months apart, container technology hadn't been invented applications adhered to a monolithic architecture, databases were almost exclusively relational and serverless wasn't even a concept. Everything from the application to the engineers was centralized. Our current ecosystem couldn't be more different. Software is still hard, don't get me wrong, but we continue to find novel solutions to consistently difficult, persistent problems. Now, some of these end up being a sort of rebranding of old ideas, but others are a unique and clever take to abstracting complexity or automating toil or perhaps most important, rethinking challenging the very premises we have accepted as Cannon for years, if not decades. In the years since deVOps attempted to answer the critical conflict between developers and operations, engineers, deVOps has become a catch all term and there have been a number of derivative works. Devops has come to mean 5000 different things to 5000 different people. For some, it can be distilled to continuous integration and continuous delivery or C I C D. For others, it's simply deploying code more frequently, perhaps adding a smattering of tests for others. Still, its organizational, they've added a platform team, perhaps even a questionably named DEVOPS team or have created an engineering structure that focuses on a separation of concerns. Leaving feature teams to manage the development, deployment, security and maintenance of their siloed services, say, whatever the interpretation, what's important is that there isn't a universally accepted standard. Well, what deVOPS is or what it looks like an execution, it's a philosophy more than anything else. A framework people can utilize to configure and customize their specific circumstances to modern development practices. The characteristic of deVOPS that I think we can all agree on though, is that an attempted to capture the challenges of the entire software development process. It's that broad umbrella, that holistic view that I think we need to breathe life into again, The challenge we face is that DeVOps isn't increasingly outmoded solution to a previous problem developers now face. Cultural and technical challenge is far greater than how to more quickly deploy a monolithic application. Cloud native is the future the next collection of default development decisions and one the deVOPS story can't absorb in its current form. I believe the era of deVOPS is waning and in this moment as the sun sets on deVOPS, we have a unique opportunity to rethink rebuild free platform. Even now, I don't have a crystal ball. That would be very handy. I'm not completely certain with the next decade of tech looks like and I can't write this story alone. I need you but I have some ideas that can get the conversation started, I believe to build on what was we have to throw away assumptions that we've taken for granted all this time in order to move forward. We must first step back. Mhm. The software or systems development life cycle, what we call the S. D. L. C. has been in use since the 1960s and it's remained more or less the same since before color television and the touch tone phone. Over the last 60 or so odd years we've made tweaks, slight adjustments, massaged it. The stages or steps are always a little different with agile and deVOps we sort of looped it into a circle and then an infinity loop we've added pretty colors. But the sclc is more or less the same and it has become an assumption. We don't even think about it anymore, universally adopted constructs like the sclc have an unspoken permanence. They feel as if they have always been and always will be. I think the impact of that is even more potent. If you were born after a construct was popularized. Nearly everything around us is a construct, a model, an artifact of a human idea. The chair you're sitting in the desk, you work at the mug from which you drink coffee or sometimes wine, buildings, toilets, plumbing, roads, cars, art, computers, everything. The sclc is a remnant an artifact of a previous era and I think we should throw it away or perhaps more accurately replace it, replace it with something that better reflects the actual nature of our work. A linear, single threaded model designed for the manufacturer of material goods cannot possibly capture the distributed complexity of modern socio technical systems. It just can't. Mhm. And these two ideas aren't mutually exclusive that the sclc was industry changing, valuable and extraordinarily impactful and that it's time for something new. I believe we are strong enough to hold these two ideas at the same time, showing respect for the past while envisioning the future. Now, I don't know about you, I've never had a software project goes smoothly in one go. No matter how small. Even if I'm the only person working on it and committing directly to master software development is chaos. It's a study and entropy and it is not getting any more simple. The model with which we think and talk about software development must capture the multithreaded, non sequential nature of our work. It should embody the roles engineers take on and the considerations they make along the way. It should build on the foundations of agile and devops and represent the iterative nature of continuous innovation. Now, when I was thinking about this, I was inspired by ideas like extreme programming and the spiral model. I I wanted something that would have layers, threads, even a way of visually representing multiple processes happening in parallel. And what I settled on is the revolution model. I believe the visualization of revolution is capable of capturing the pivotal moments of any software scenario. And I'm going to dive into all the discrete elements. But I want to give you a moment to have a first impression, to absorb my idea. I call it revolution because well for one it revolves, it's circular shape reflects the continuous and iterative nature of our work, but also because it is revolutionary. I am challenging a 60 year old model that is embedded into our daily language. I don't expect Gartner to build a magic quadrant around this tomorrow, but that would be super cool. And you should call me my mission with. This is to challenge the status quo to create a model that I think more accurately reflects the complexity of modern cloud native software development. The revolution model is constructed of five concentric circles describing the critical roles of software development architect. Ng development, automating, deploying and operating intersecting each loop are six spokes that describe the production considerations every engineer has to consider throughout any engineering work and that's test, ability, secure ability, reliability, observe ability, flexibility and scalability. The considerations listed are not all encompassing. There are of course things not explicitly included. I figured if I put 20 spokes, some of us, including myself, might feel a little overwhelmed. So let's dive into each element in this model. We have long used personas as the default way to do divide audiences and tailor messages to group people. Every company in the world right now is repeating the mantra of developers, developers, developers but personas have always bugged me a bit because this approach typically either oversimplifies someone's career are needlessly complicated. Few people fit cleanly and completely into persona based buckets like developers and operations anymore. The lines have gotten fuzzy on the other hand, I don't think we need to specifically tailor messages as to call out the difference between a devops engineer and a release engineer or a security administrator versus a security engineer but perhaps most critically, I believe personas are immutable. A persona is wholly dependent on how someone identifies themselves. It's intrinsic not extrinsic. Their titles may change their jobs may differ, but they're probably still selecting the same persona on that ubiquitous drop down. We all have to choose from when registering for an event. Probably this one too. I I was a developer and I will always identify as a developer despite doing a ton of work in areas like devops and Ai Ops and Deverell in my heart. I'm a developer I think about problems from that perspective. First it influences my thinking and my approach roles are very different. Roles are temporary, inconsistent, constantly fluctuating. If I were an actress, the parts I would play would be lengthy and varied, but the persona I would identify as would remain an actress and artist lesbian. Your work isn't confined to a single set of skills. It may have been a decade ago, but it is not today in any given week or sprint, you may play the role of an architect. Thinking about how to design a feature or service, developer building out code or fixing a bug and on automation engineer, looking at how to improve manual processes. We often refer to as soil release engineer, deploying code to different environments or releasing it to customers or in operations. Engineer ensuring an application functions inconsistent expected ways and no matter what role we play. We have to consider a number of issues. The first is test ability. All software systems require testing to assure architects that designs work developers, the code works operators, that infrastructure is running as expected and engineers of all disciplines that code changes won't bring down the whole system testing in its many forms is what enables systems to be durable and have longevity. It's what reassures engineers that changes won't impact current functionality. A system without tests is a disaster waiting to happen, which is why test ability is first among equals at this particular roundtable. Security is everyone's responsibility. But if you understand how to design and execute secure systems, I struggle with this security incidents for the most part are high impact, low probability events. The really big disasters, the one that the ones that end up on the news and get us all free credit reporting for a year. They don't happen super frequently and then goodness because you know that there are endless small vulnerabilities lurking in our systems. Security is something we all know we should dedicate time to but often don't make time for. And let's be honest, it's hard and complicated and a little scary def sec apps. The first derivative of deVOPS asked engineers to move security left this approach. Mint security was a consideration early in the process, not something that would block release at the last moment. This is also the consideration under which I'm putting compliance and governance well not perfectly aligned. I figure all the things you have to call lawyers for should just live together. I'm kidding. But in all seriousness, these three concepts are really about risk management, identity, data, authorization. It doesn't really matter what specific issue you're speaking about, the question is who has access to what win and how and that is everyone's responsibility at every stage site reliability engineering or sorry, is a discipline job and approach for good reason. It is absolutely critical that applications and services work as expected. Most of the time. That said, availability is often mistakenly treated as a synonym for reliability. Instead, it's a single aspect of the concept if a system is available but customer data is inaccurate or out of sync. The system is not reliable, reliability has five key components, availability, latency, throughput. Fidelity and durability, reliability is the end result. But resiliency for me is the journey the action engineers can take to improve reliability, observe ability is the ability to have insight into an application or system. It's the combination of telemetry and monitoring and alerting available to engineers and leadership. There's an aspect of observe ability that overlaps with reliability, but the purpose of observe ability isn't just to maintain a reliable system though, that is of course important. It is the capacity for engineers working on a system to have visibility into the inner workings of that system. The concept of observe ability actually originates and linear dynamic systems. It's defined as how well internal states of a system can be understood based on information about its external outputs. If it is critical when companies move systems to the cloud or utilize managed services that they don't lose visibility and confidence in their systems. The shared responsibility model of cloud storage compute and managed services require that engineering teams be able to quickly be alerted to identify and remediate issues as they arise. Flexible systems are capable of adapting to meet the ever changing needs of the customer and the market segment, flexible code bases absorb new code smoothly. Embody a clean separation of concerns. Are partitioned into small components or classes and architected to enable the now as well as the next inflexible systems. Change dependencies are reduced or eliminated. Database schemas accommodate change well components, communicate via a standardized and well documented A. P. I. The only thing constant in our industry is change and every role we play, creating flexibility and solutions that can be flexible that will grow as the applications grow is absolutely critical. Finally, scalability scalability refers to more than a system's ability to scale for additional load. It implies growth scalability and the revolution model carries the continuous innovation of a team and the byproducts of that growth within a system. For me, scalability is the most human of the considerations. It requires each of us in our various roles to consider everyone around us, our customers who use the system or rely on its services, our colleagues current and future with whom we collaborate and even our future selves. Mhm. Software development isn't a straight line, nor is it a perfect loop. It is an ever changing complex dance. There are twirls and pivots and difficult spins forward and backward. Engineers move in parallel, creating truly magnificent pieces of art. We need a modern model for this modern era and I believe this is just the revolution to get us started. Thank you so much for having me. >>Hey, we're back here. Live in the keynote studio. I'm john for your host here with lisa martin. David lot is getting ready for the fireside chat ending keynote with the practitioner. Hello! Fresh without data mesh lisa Emily is amazing. The funky artwork there. She's amazing with the talk. I was mesmerized. It was impressive. >>The revolution of devops and the creative element was a really nice surprise there. But I love what she's doing. She's challenging the status quo. If we've learned nothing in the last year and a half, We need to challenge the status quo. A model from the 1960s that is no longer linear. What she's doing is revolutionary. >>And we hear this all the time. All the cube interviews we do is that you're seeing the leaders, the SVP's of engineering or these departments where there's new new people coming in that are engineering or developers, they're playing multiple roles. It's almost a multidisciplinary aspect where you know, it's like going into in and out burger in the fryer later and then you're doing the grill, you're doing the cashier, people are changing roles or an architect, their test release all in one no longer departmental, slow siloed groups. >>She brought up a great point about persona is that we no longer fit into these buckets. That the changing roles. It's really the driver of how we should be looking at this. >>I think I'm really impressed, really bold idea, no brainer as far as I'm concerned, I think one of the things and then the comments were off the charts in a lot of young people come from discord servers. We had a good traction over there but they're all like learning. Then you have the experience, people saying this is definitely has happened and happening. The dominoes are falling and they're falling in the direction of modernization. That's the key trend speed. >>Absolutely with speed. But the way that Emily is presenting it is not in a brash bold, but it's in a way that makes great sense. The way that she creatively visually lined out what she was talking about Is amenable to the folks that have been doing this for since the 60s and the new folks now to really look at this from a different >>lens and I think she's a great setup on that lightning top of the 15 companies we got because you think about sis dig harness. I white sourced flamingo hacker one send out, I oh, okay. Thought spot rock set Sarah Ops ramp and Ops Monte cloud apps, sani all are doing modern stuff and we talked to them and they're all on this new wave, this monster wave coming. What's your observation when you talk to these companies? >>They are, it was great. I got to talk with eight of the 15 and the amount of acceleration of innovation that they've done in the last 18 months is phenomenal obviously with the power and the fuel and the brand reputation of aws but really what they're all facilitating cultural shift when we think of devoPS and the security folks. Um, there's a lot of work going on with ai to an automation to really kind of enabled to develop the develops folks to be in control of the process and not have to be security experts but ensuring that the security is baked in shifting >>left. We saw that the chat room was really active on the security side and one of the things I noticed was not just shift left but the other groups, the security groups and the theme of cultural, I won't say war but collision cultural shift that's happening between the groups is interesting because you have this new devops persona has been around Emily put it out for a while. But now it's going to the next level. There's new revolutions about a mindset, a systems mindset. It's a thinking and you start to see the new young companies coming out being funded by the gray locks of the world who are now like not going to be given the we lost the top three clouds one, everything. there's new business models and new technical architecture in the cloud and that's gonna be jerry Chen talk coming up next is going to be castles in the clouds because jerry chant always talked about moats, competitive advantage and how moats are key to success to guard the castle. And then we always joke, there's no more moz because the cloud has killed all the boats. But now the motor in the cloud, the castles are in the cloud, not on the ground. So very interesting thought provoking. But he's got data and if you look at the successful companies like the snowflakes of the world, you're starting to see these new formations of this new layer of innovation where companies are growing rapidly, 98 unicorns now in the cloud. Unbelievable, >>wow, that's a lot. One of the things you mentioned, there's competitive advantage and these startups are all fueled by that they know that there are other companies in the rear view mirror right behind them. If they're not able to work as quickly and as flexibly as a competitor, they have to have that speed that time to market that time to value. It was absolutely critical. And that's one of the things I think thematically that I saw along the eighth sort of that I talked to is that time to value is absolutely table stakes. >>Well, I'm looking forward to talking to jerry chan because we've talked on the queue before about this whole idea of What happens when winner takes most would mean the top 3, 4 cloud players. What happens? And we were talking about that and saying, if you have a model where an ecosystem can develop, what does that look like and back in 2013, 2014, 2015, no one really had an answer. Jerry was the only BC. He really nailed it with this castles in the cloud. He nailed the idea that this is going to happen. And so I think, you know, we'll look back at the tape or the videos from the cube, we'll find those cuts. But we were talking about this then we were pontificating and riffing on the fact that there's going to be new winners and they're gonna look different as Andy Jassy always says in the cube you have to be misunderstood if you're really going to make something happen. Most of the most successful companies are misunderstood. Not anymore. The cloud scales there. And that's what's exciting about all this. >>It is exciting that the scale is there, the appetite is there the appetite to challenge the status quo, which is right now in this economic and dynamic market that we're living in is there's nothing better. >>One of the things that's come up and and that's just real quick before we bring jerry in is automation has been insecurity, absolutely security's been in every conversation, but automation is now so hot in the sense of it's real and it's becoming part of all the design decisions. How can we automate can we automate faster where the keys to automation? Is that having the right data, What data is available? So I think the idea of automation and Ai are driving all the change and that's to me is what these new companies represent this modern error where AI is built into the outcome and the apps and all that infrastructure. So it's super exciting. Um, let's check in, we got jerry Chen line at least a great. We're gonna come back after jerry and then kick off the day. Let's bring in jerry Chen from Greylock is he here? Let's bring him in there. He is. >>Hey john good to see you. >>Hey, congratulations on an amazing talk and thesis on the castles on the cloud. Thanks for coming on. >>All right, Well thanks for reading it. Um, always were being put a piece of workout out either. Not sure what the responses, but it seemed to resonate with a bunch of developers, founders, investors and folks like yourself. So smart people seem to gravitate to us. So thank you very much. >>Well, one of the benefits of doing the Cube for 11 years, Jerry's we have videotape of many, many people talking about what the future will hold. You kind of are on this early, it wasn't called castles in the cloud, but you were all I was, we had many conversations were kind of connecting the dots in real time. But you've been on this for a while. It's great to see the work. I really think you nailed this. I think you're absolutely on point here. So let's get into it. What is castles in the cloud? New research to come out from Greylock that you spearheaded? It's collaborative effort, but you've got data behind it. Give a quick overview of what is castle the cloud, the new modes of competitive advantage for companies. >>Yeah, it's as a group project that our team put together but basically john the question is, how do you win in the cloud? Remember the conversation we had eight years ago when amazon re event was holy cow, Like can you compete with them? Like is it a winner? Take all? Winner take most And if it is winner take most, where are the white spaces for Some starts to to emerge and clearly the past eight years in the cloud this journey, we've seen big companies, data breaks, snowflakes, elastic Mongo data robot. And so um they spotted the question is, you know, why are the castles in the cloud? The big three cloud providers, Amazon google and Azure winning. You know, what advantage do they have? And then given their modes of scale network effects, how can you as a startup win? And so look, there are 500 plus services between all three cloud vendors, but there are like 500 plus um startups competing gets a cloud vendors and there's like almost 100 unicorn of private companies competing successfully against the cloud vendors, including public companies. So like Alaska, Mongo Snowflake. No data breaks. Not public yet. Hashtag or not public yet. These are some examples of the names that I think are winning and watch this space because you see more of these guys storm the castle if you will. >>Yeah. And you know one of the things that's a funny metaphor because it has many different implications. One, as we talk about security, the perimeter of the gates, the moats being on land. But now you're in the cloud, you have also different security paradigm. You have a different um, new kinds of services that are coming on board faster than ever before. Not just from the cloud players but From companies contributing into the ecosystem. So the combination of the big three making the market the main markets you, I think you call 31 markets that we know of that probably maybe more. And then you have this notion of a sub market, which means that there's like we used to call it white space back in the day, remember how many whites? Where's the white space? I mean if you're in the cloud, there's like a zillion white spaces. So talk about this sub market dynamic between markets and that are being enabled by the cloud players and how these sub markets play into it. >>Sure. So first, the first problem was what we did. We downloaded all the services for the big three clowns. Right? And you know what as recalls a database or database service like a document DB and amazon is like Cosmo dB and Azure. So first thing first is we had to like look at all three cloud providers and you? Re categorize all the services almost 500 Apples, Apples, Apples # one number two is you look at all these markets or sub markets and said, okay, how can we cluster these services into things that you know you and I can rock right. That's what amazon Azure and google think about. It is very different and the beauty of the cloud is this kind of fat long tail of services for developers. So instead of like oracle is a single database for all your needs. They're like 20 or 30 different databases from time series um analytics, databases. We're talking rocks at later today. Right. Um uh, document databases like Mongo search database like elastic. And so what happens is there's not one giant market like databases, there's a database market And 30, 40 sub markets that serve the needs developers. So the Great News is cloud has reduced the cost and create something that new for developers. Um also the good news is for a start up you can find plenty of white speeds solving a pain point, very specific to a different type of problem >>and you can sequence up to power law to this. I love the power of a metaphor, you know, used to be a very thin neck note no torso and then a long tail. But now as you're pointing out this expansion of the fat tail of services, but also there's big tam's and markets available at the top of the power law where you see coming like snowflake essentially take on the data warehousing market by basically sitting on amazon re factoring with new services and then getting a flywheel completely changing the economic unit economics completely changing the consumption model completely changing the value proposition >>literally you >>get Snowflake has created like a storm, create a hole, that mode or that castle wall against red shift. Then companies like rock set do your real time analytics is Russian right behind snowflakes saying, hey snowflake is great for data warehouse but it's not fast enough for real time analytics. Let me give you something new to your, to your parallel argument. Even the big optic snowflake have created kind of a wake behind them that created even more white space for Gaza rock set. So that's exciting for guys like me and >>you. And then also as we were talking about our last episode two or quarter two of our showcase. Um, from a VC came on, it's like the old shelf where you didn't know if a company's successful until they had to return the inventory now with cloud you if you're not successful, you know it right away. It's like there's no debate. Like, I mean you're either winning or not. This is like that's so instrumented so a company can have a good better mousetrap and win and fill the white space and then move up. >>It goes both ways. The cloud vendor, the big three amazon google and Azure for sure. They instrument their own class. They know john which ecosystem partners doing well in which ecosystems doing poorly and they hear from the customers exactly what they want. So it goes both ways they can weaponize that. And just as well as you started to weaponize that info >>and that's the big argument of do that snowflake still pays the amazon bills. They're still there. So again, repatriation comes back, That's a big conversation that's come up. What's your quick take on that? Because if you're gonna have a castle in the cloud, then you're gonna bring it back to land. I mean, what's that dynamic? Where do you see that compete? Because on one hand is innovation. The other ones maybe cost efficiency. Is that a growth indicator slow down? What's your view on the movement from and to the cloud? >>I think there's probably three forces you're finding here. One is the cost advantage in the scale advantage of cloud so that I think has been going for the past eight years, there's a repatriation movement for a certain subset of customers, I think for cost purposes makes sense. I think that's a tiny handful that believe they can actually run things better than a cloud. The third thing we're seeing around repatriation is not necessary against cloud, but you're gonna see more decentralized clouds and things pushed to the edge. Right? So you look at companies like Cloudflare Fastly or a company that we're investing in Cato networks. All ideas focus on secure access at the edge. And so I think that's not the repatriation of my own data center, which is kind of a disaggregated of cloud from one giant monolithic cloud, like AWS east or like a google region in europe to multiple smaller clouds for governance purposes, security purposes or legacy purposes. >>So I'm looking at my notes here, looking down on the screen here for this to read this because it's uh to cut and paste from your thesis on the cloud. The excellent cloud. The of the $38 billion invested this quarter. Um Ai and ml number one, um analytics. Number two, security number three. Actually, security number one. But you can see the bubbles here. So all those are data problems I need to ask you. I see data is hot data as intellectual property. How do you look at that? Because we've been reporting on this and we just started the cube conversation around workflows as intellectual property. If you have scale and your motives in the cloud. You could argue that data and the workflows around those data streams is intellectual property. It's a protocol >>I believe both are. And they just kind of go hand in hand like peanut butter and jelly. Right? So data for sure. I. P. So if you know people talk about days in the oil, the new resource. That's largely true because of powers a bunch. But the workflow to your point john is sticky because every company is a unique snowflake right? Like the process used to run the cube and your business different how we run our business. So if you can build a workflow that leverages the data, that's super sticky. So in terms of switching costs, if my work is very bespoke to your business, then I think that's competitive advantage. >>Well certainly your workflow is a lot different than the cube. You guys just a lot of billions of dollars in capital. We're talking to all the people out here jerry. Great to have you on final thought on your thesis. Where does it go from here? What's been the reaction? Uh No, you put it out there. Great love the restart. Think you're on point on this one. Where did we go from here? >>We have to follow pieces um in the near term one around, you know, deep diver on open source. So look out for that pretty soon and how that's been a powerful strategy a second. Is this kind of just aggregation of the cloud be a Blockchain and you know, decentralized apps, be edge applications. So that's in the near term two more pieces of, of deep dive we're doing. And then the goal here is to update this on a quarterly and annual basis. So we're getting submissions from founders that wanted to say, hey, you missed us or he screwed up here. We got the big cloud vendors saying, Hey jerry, we just lost his new things. So our goal here is to update this every single year and then probably do look back saying, okay, uh, where were we wrong? We're right. And then let's say the castle clouds 2022. We'll see the difference were the more unicorns were there more services were the IPO's happening. So look for some short term work from us on analytics, like around open source and clouds. And then next year we hope that all of this forward saying, Hey, you have two year, what's happening? What's changing? >>Great stuff and, and congratulations on the southern news. You guys put another half a billion dollars into early, early stage, which is your roots. Are you still doing a lot of great investments in a lot of unicorns. Congratulations that. Great luck on the team. Thanks for coming on and congratulations you nailed this one. I think I'm gonna look back and say that this is a pretty seminal piece of work here. Thanks for sharing. >>Thanks john thanks for having us. >>Okay. Okay. This is the cube here and 81 startup showcase. We're about to get going in on all the hot companies closing out the kino lisa uh, see jerry Chen cube alumni. He was right from day one. We've been riffing on this, but he nails it here. I think Greylock is lucky to have him as a general partner. He's done great deals, but I think he's hitting the next wave big. This is, this is huge. >>I was listening to you guys talking thinking if if you had a crystal ball back in 2013, some of the things Jerry saying now his narrative now, what did he have a crystal >>ball? He did. I mean he could be a cuBA host and I could be a venture capital. We were both right. I think so. We could have been, you know, doing that together now and all serious now. He was right. I mean, we talked off camera about who's the next amazon who's going to challenge amazon and Andy Jassy was quoted many times in the queue by saying, you know, he was surprised that it took so long for people to figure out what they were doing. Okay, jerry was that VM where he had visibility into the cloud. He saw amazon right away like we did like this is a winning formula and so he was really out front on this one. >>Well in the investments that they're making in these unicorns is exciting. They have this, this lens that they're able to see the opportunities there almost before anybody else can. And finding more white space where we didn't even know there was any. >>Yeah. And what's interesting about the report I'm gonna dig into and I want to get to him while he's on camera because it's a great report, but He says it's like 500 services I think Amazon has 5000. So how you define services as an interesting thing and a lot of amazon services that they have as your doesn't have and vice versa, they do call that out. So I find the report interesting. It's gonna be a feature game in the future between clouds the big three. They're gonna say we do this, you're starting to see the formation, Google's much more developer oriented. Amazon is much more stronger in the governance area with data obviously as he pointed out, they have such experience Microsoft, not so much their developer cloud and more office, not so much on the government's side. So that that's an indicator of my, my opinion of kind of where they rank. So including the number one is still amazon web services as your long second place, way behind google, right behind Azure. So we'll see how the horses come in, >>right. And it's also kind of speaks to the hybrid world in which we're living the hybrid multi cloud world in which many companies are living as companies to not just survive in the last year and a half, but to thrive and really have to become data companies and leverage that data as a competitive advantage to be able to unlock the value of it. And a lot of these startups that we talked to in the showcase are talking about how they're helping organizations unlock that data value. As jerry said, it is the new oil, it's the new gold. Not unless you can unlock that value faster than your competition. >>Yeah, well, I'm just super excited. We got a great day ahead of us with with all the cots startups. And then at the end day, Volonte is gonna interview, hello, fresh practitioners, We're gonna close it out every episode now, we're going to do with the closing practitioner. We try to get jpmorgan chase data measures. The hottest area right now in the enterprise data is new competitive advantage. We know that data workflows are now intellectual property. You're starting to see data really factoring into these applications now as a key aspect of the competitive advantage and the value creation. So companies that are smart are investing heavily in that and the ones that are kind of slow on the uptake are lagging the market and just trying to figure it out. So you start to see that transition and you're starting to see people fall away now from the fact that they're not gonna make it right, You're starting to, you know, you can look at look at any happens saying how much ai is really in there. Real ai what's their data strategy and you almost squint through that and go, okay, that's gonna be losing application. >>Well the winners are making it a board level conversation >>And security isn't built in. Great to have you on this morning kicking it off. Thanks John Okay, we're going to go into the next set of the program at 10:00 we're going to move into the breakouts. Check out the companies is three tracks in there. We have an awesome track on devops pure devops. We've got the data and analytics and we got the cloud management and just to run down real quick check out the sis dig harness. Io system is doing great, securing devops harness. IO modern software delivery platform, White Source. They're preventing and remediating the rest of the internet for them for the company's that's a really interesting and lumbago, effortless acres land and monitoring functions, server list super hot. And of course hacker one is always great doing a lot of great missions and and bounties you see those success continue to send i O there in Palo alto changing the game on data engineering and data pipe lining. Okay. Data driven another new platform, horizontally scalable and of course thought spot ai driven kind of a search paradigm and of course rock set jerry Chen's companies here and press are all doing great in the analytics and then the cloud management cost side 80 operations day to operate. Ops ramps and ops multi cloud are all there and sunny, all all going to present. So check them out. This is the Cubes Adria's startup showcase episode three.

[Music] welcome to thecube's coverage of pure accelerate 2021 i'm lisa martin pleased to be welcoming back one of our alumni to the cube prakash darjee is here the vp and gm of the digital experience business unit at pure storage prakash it's great to have you back on the program yeah lisa thanks for having me it's been i don't know more than a year since i've seen the cube right pre-covered so it's been a little while recover copa remember those days well thank you for joining us virtually we appreciate that and also excited to hear some of the things that are going to be coming out at accelerate an event that i've covered in person several times so talk to me about this digital experience business unit this is relatively new what does it encompass what are you hoping to deliver from a portfolio perspective to your customers well what's interesting is it's new and it's not right because we've we've been as a company a sas company that happened to ship storage boxes on premise so we've had pure one which was largely used for monitoring and supporting our fleet like a sas company would do and customers had access to that as their single pane of class but as we expanded beyond just observability and monitoring we realized that we could use this observability to do more for customers and we introduced our pure as a service offering about three years ago now which customers just sign up for slas like you know they would on a cloud you sign up i want this performance i want this capacity it's storage so you know why don't you just sign up for what you need and we uh created the dx business unit the digital experience business units to bring those things together because frankly we're using pier one to monitor manage and allow customers to sign up for their slas in a very digital way and i guess the world's changed a little bit because you know previously you would you know call up your sales rep to do things and then it happened and i think a lot of people got a little bit of zoom fatigue um and therefore you know we see a lot of traction right now in terms of people just self-serving and going up and signing up for the slas they need talk to me about some of those slas that customers are signing up for what is it that they know with pure as a service for example in pure one that they can get well you want storage you want storage that's high performing you want storage that supports your applications you know number one thing with storage is you're signing up for capacity and performance right when you think storage you're like oh you know i need to store my videos or i need to store my apps or i need to store something and you know right now we've got customers and uh you know multiple hundreds of petabytes range right like big customers lots of storage um and we got small customers as well you know five to ten terabytes of storage as well so um but across that entire range in storage you're basically want to make sure you don't lose your data it's protected it's safe um the world's becoming a little less secure ransomware and attacks and all of those types of things so we've introduced concepts of ransomware assessment and capabilities like that but the performance of capacity are the two things you want to sign up for so what if you just said i want it this fast and i want this much space and all of the other technology problems you give to pure right because you know what you run out of space we'll ship the box we'll manage it you don't need to call us you don't need to order you don't need to do that so it's more than just a i think when people think about services they think about subscriptions right capex versus opex and sure there's an element to capex versus optics but that's not really what a service is that's just a subscription a service is hey i just want this performance in this capacity who's going to run it and operate it and manage it for me you know when you sign up for a sas service you don't really care when you sign up for salesforce how it runs who's running it etc you just want to manage your crm pipeline and you know we're bringing that same sas experience to storage you do expect that you bring up a good point when you're when you're talking about sas applications one of the things that we saw in the last year is this massive proliferation or acceleration of companies in every industry dependent on so many sas apps just for collaboration alone internally let alone externally brought up ransomware it's something i've been talking a lot about in the last year how that's been on the rise talk to me about you know as enterprise enterprises need storage to do more than just that talk to me about how you're working with customers to ensure that this data across the enterprise is secure well so it's interesting um when i talk to people and they ask me are you secure i'm like well that's kind of a silly question um because you know if you think about security there's always more you could do it's not am i secure it's how secure am i and you want to be the nsa where everything's under a lock and key you can do that and it's just going to be really expensive to do so the what we're the way we're approaching it is we're giving customers levels of ransomware that they can actually implement um for protection level zero right the simplest is make sure that i've got you know an air gap of my data and a copy of it to prevent you from altering it for up to 30 days or some time period which you know is the first level of threat that you know someone can't hold you hostage by encrypting your data those types of things and we've done that for our whole portfolio we provide that and we now even give customers an assessment to tell them you know whether they can go into our digital experience and do an assessment to see how secure are they but that's only the first step hackers are actually getting more sophisticated now on air gap and just saying well what if i do a time delayed encryption thing that overcomes the 30-day thing and you know like the world's evolving so the next level is a physical gap where you take it off the primary system and you actually put it on a secondary system your data well so you know your virtual air gaps one thing your physical distance provides another layer of security because now it's another physical asset with another copy of your data sure it costs more money because you're storing it twice so you have to decide based on the sensitivity of your information how many layers of security you want to build it you can even build in a third layer that says if something happens i don't want to pay the ransomware i just need to be able to recover quickly so let me have a rapid recovery sla and you know we use our flash play to deliver that because it's one of the you know fastest recovery products on the planet based on the performance threshold so you know we've seen a lot of companies now adopt and use flashblade is kind of that level three for rapid recovery in instead of paying for the insurance they're paying for the remediation you know what i mean so it's a different it's interesting how the landscape has evolved right and as the threat actors have access to more and more sophistication obviously that becomes a challenge but you bring up a good point and that is it's sort of it's not a matter of is it going to happen to us it's it's when and it's kind of that tolerance level based on the data but the modern data experience here's been talking about this obviously the modern data experience has changed a lot in the last year talk to us about what that is how does the modern data experience are pure one and pure as a service foundational to that and talk to me about the benefits in it for customers well so when we think about the modern data experience there's really three pillars we talk about in the modern day experience the first one is just innovation leadership pure's got a little bit of a history of redefining storage first of all flash first the unified fast fallen object you know we're on a third generation of qlc technology so we figure if we don't invent the future who else is going to you know we look around the landscape and there's a lot of data technology so we need to invent a future that people have a blueprint to copy like and that's that's our goal of modernizing the landscape you know we don't see a lot of original and innovative thought happening in the industry so we have to create the blueprint of the future right we pride ourselves on that innovation leadership um and evergreen which you know we've introduced is an innovation where you know if people buy a 500 terabytes of storage today they don't have to re-buy it every three to five years that innovation that we introduced is still unmatched in industry after we've been in industry for 10 years because companies haven't figured out how to copy it evergreen is still a differentiator it sounds like the modern data experience what you're looking to do is also define it with and for customers and have that be a unique differentiator for what care delivers 100 um so you know this innovation leadership's big um making sure that you can run your landscape like a cloud you know have a service catalog you know service catalog for developers as containers and you know we we lean very heavily into what we're doing for devops and developers not just storage administrators and you know part of the modern data experience is being cloud ready and container ready and then finally just having the best digital experience which you know pier one and peer piers of services foundational tube uh where customers can go in procure easy support easy and all of it starts with the data like if i was to say hey you're gonna get a get into a tesla right and you're gonna turn on the self-driving mode would you turn it on if you knew that there were zero miles clocked on the odometer right where no like yeah you're the first we haven't really trained this yet right no one would turn that on so for you to be able to offer a digital experience and a service experience to a customer it's all about miles driven and since we've introduced pier one five years ago you know now on a yearly basis we're collecting over 20 petabytes of data tons of signals training the algorithms around giving customers recommendations which we've been doing now customers can get performance recommendations and upgrade recommendations and now we've used the recommendations are such high fidelity that because of our miles driven we're using that internally to run and operate our services on behalf of customers and when companies think about disruptive events let me take my old portfolio and create a new one you're resetting the odometer at zero so without something like evergreen it makes no sense in terms of how do you get to as a service you can get to capex versus opex right and you know we were the first people to do that in storage with peers of service three plus years ago but we've moved beyond a financial offering now to talk about you know how do you run and operate performance and capacity slas well your point is so much more that customers need especially as there's more and more data being generated um you know the edge is exploding iot devices are exploding and there's more challenges that customers have to do but it's also being able to get those fast insights from data to be able to make those data-driven decisions which it sounds like what you're doing from all of the mileage that pure1 and pure as a service have so talk to me about some of the things that are being announced with respect to the digital experience of pure one at accelerate so there's three primary announcements um we've moved beyond observability first to do assessments so you know we can now say you know instead of just monitoring and watching what's going on we can give you a threat level assessment specific to ransomware that's a new capability we're introducing we've also been you know in monitoring monitoring storage and monitoring virtual machines for a while but we've if you take a look at how people deploy on storage they deploy vms and they deploy containers we've seen very little like they also have bare metal right but between those three now you cover how people are using storage from a deployment model and we've brought container monitoring into pier one for end-to-end traceability monitoring for you know both your container landscape as well as your storage landscape underneath with our flash frame flash plate so you know this observability and assessment space has a lot of new capabilities we're bringing the second piece is recommendations so previously we've had this data and customers could go into pure one and use the data they could simulate adding performance they could simulate adding capacity they could simulate moving this workload from here to here but now instead of you doing it we've we've created a recommendation engine where we'll tell you what to do because we actually tracked you know how much time is spent with people trying to figure out what to do there were times when storage admins were in the products like let me try moving it from here to here and see what would happen let me try moving it from here to here if you've got thousands of volumes and hundreds of arrays and that type of thing um you could spend weeks trying to figure out what to do by running permutational combinatorics so instead we've used our ai engine now to simulate taking into account customer preference load capacity previous buying patterns etc to create high fidelity recommendations for performance capacity placing new workloads workflow rebalancing and even for pure as a service which sla should i sign up for when you go to amazon one of the biggest problems on the on the cloud is too much choice there's like 300 items on the service catalog even in storage there's like i don't know 20 30 options of should i pick this storage type or this storage type for that storage type how do you even know um because we've been the miles driven analogy because we now know how customers have been deploying you can choose your workloads and based on what we've seen based on the wisdom of what we've collected across all the other customers we can tell you which service instance type you need so this recommendation approach is big and then the last one is self-service so customers now can control and set their reserved instances expand set their renewals we've even introduced a partner persona where partners can manage things on behalf of a customer and see transparency in billing and order traffic so all of those things that you're used to in kind of a commerce and a cloud experience we've brought that to traditional storage so some pretty big changes there and i like how how here has always been very bold in defining its differentiators using its own data to make better decisions as you you said customers have a ton of choice which is great it's also challenging at the same time for them to be able to understand objectively what is it that my environment needs talk to me a little bit about some of the changes that you saw in the last year as companies shifted almost overnight to a remote working situation can't get into my data center what are some of the ways in which pure has helped organizations with the advancements that you've made in your services portfolio well so the first thing we did and we did this kind of literally i think last february when you know everything immediately went into lockdown we introduced a zero touch provisioning category you don't want people in the data data data center right you like you need to obviously if there's physical stuff you have to rack stack and cable but beyond that everything else should be zero touch and so we've introduced zero patch provisioning capability immediately and some of like the largest uh one of the largest you know video conferencing providers on the planet um happened to call us immediately saying look we can't even get stuff to keep up with the demand and overnight we were able to go ahead and work with them to you know get them the efficiency that they needed so you know if i take a look at our supply chain throughout covid we were able you know to meet most shipments in some four days throughout covid even in a globally disrupted supply chain because of the agility and the flexibility we have in our portfolio and frankly just a phenomenal supply chain team as well so you know that that approach has engendered a ton of trust whenever you do anything like you know in this environment covid pandemic etc people are under stress it creates stress for human beings it even creates stress for families right have two small children it creates stress [Music] what do you how do you get through that stress all the things that are unnecessary are things you just forget about and to get the things that are necessary done you go to the people you trust so that's a great that's a great point you bring up about trust because that is table stakes for an organization to trust its partners or its customers to be able to trust that it's going to deliver what it needs it's no longer a nice to have i think this one of the things that coveted clement has shown us is that it's absolutely essential last question progression i want to get to you is let's talk about ai ops for a second we're seeing more and more organizations turning to ai ops for more intelligent operations what is it what are some of the benefits that pure can deliver in that response well look i have a lot of opinions on aiops but the first one is like saying aaiops now was like saying web 2.0 a few years ago right um it's a hot term everyone likes to talk about it and very few people actually do anything real ai right it's like well let me tell you something so as you think about aiops today you need to first get the data in the miles driven manner the second thing you need to do is you could use that data and create a ton of recommendations that you tell send to customers and you will be the equivalent of facebook ads right like click click click click click some of these are relevant some of these aren't right if all you do is create recommendations you're creating a spam flow to your customers the number one thing to really make it learning based is if someone rejects a recommendation you now have to collect that and train your algorithms to say you know what this person doesn't need that right and maybe the other person accepted that same recommendation and they do so the time isn't just about data collection and miles driven but the amount of recommendations that customers accept and reject can train and personalize how you do your ai operations and i feel like this economy because aiops is hot everyone's just like i have ai ops and it's just so facetious you need to think about how you're going to continually evolve and train and learn and who's going to train the way you train support is support personnel and bug fixes you need to monitor how your support personnel fixes things to be able to replicate and have higher efficiencies and support so even small customers can get the same level of support as the large customers because you know it's not like the big guys get 50 people and the small guys only get one right you need to use software as the great equalizer and the same thing goes in sales when you're approaching customers with offers and recommendations or when customers whether they need performance or capacity the fidelity matters and data and technology will only go so far you need to use the human feedback loop to train your ai if you don't do that you're missing the concept of machine learning agreed to last question since we have about 30 seconds left or so talk to me about how pure is going to continue to utilize ai and to your point not just throw out recommendations but actually have learning going on so that the right relevant offers for example can be delivered to the right customer at the right time well we pride ourselves on simplicity and customer first right our net promoter score is you know one of the top trust scores in the industry and because of that we've got a very vibrant and active customer community that goes into you know pure one on a daily basis to monitor the landscape to see what's going on to create support cases whatever it may be and because of that we're going to continue engaging and learning from our customers and you know i think you can't do it without the trust and you know a large portion of our business is large sas providers so you know you think about you know very very large sas companies we service them because of our evergreen model and now bringing this level of predictability creates a level of efficiency for sas companies um that means they could do more with less and that's what this industry is about well said prakash thank you so much for joining me at your our coverage of accelerate excited to see what's going on with the modern data experience how you're getting in there and working and partnering with customers using the data to learn and tweak and improve uh excited to hear some of the other stuff that comes up but i appreciate you joining me this morning thanks for having me lisa i enjoy the conversation excellent for prakash darjee i'm lisa martin you're watching thecube's coverage of pure accelerate 2021.

>>from >>around the globe, >>it's the >>Cube with digital coverage of IBM. Think 2021 brought to you by IBM >>Well, good to have you here on the cube. We continue our conversations here as part of the IBM think initiative. I'm john Walsh your host here on the cube, joined today by Marco Novakovich, who is the co founder and Ceo of an stana, which is an IBM company they specialize in enterprise observe ability for cloud native applications. And Merkel joins us all the way from Germany near cologne Germany. Merkel good to see you today. How are you doing >>and good. Hi john nice to be here, >>you bet, thank you for taking the time today. Well, first off, let's just let's just give some definitions here. Enterprise observe ability. Um what is that? What are we talking about here? >>Yes. So observe ability is basically the next generation of monitoring, which means it provides data from a system from an application to the outside so that people from the outside can basically judge what's happening inside of an application. So think about your a big e commerce provider and you are, you have your shop application and it doesn't work, observe ability, gives you the ability to really deep dive and see all the relevant metrics, logs, uh, and, and application flows to understand why something is not working as you would expect. >>So if I'm just listening to this, I think, okay, I'm I'm monitoring my applications already right. I've got a PM and force and, and I kind of know what things are going on, what's happening, where the hiccups are all that, how, what is the enhancement here than in terms of observe ability taking it sounds like you're kind of taking a P. M. To a much higher level. >>Absolutely. I mean that's essentially how you can think about it and, and, and we see three things that really make us and stana and enterprise observe ability different. And number one is automation. So the way we gather this information is fully automated, so you don't have to configure anything. We get inside of your code, we analyze the flow up the application, we get the errors, the logs and the metrics fully automatic. And the second is getting context. One of the problems with monitoring is that you have all these monitoring data silos. So you have metrics on the one side locks in a different tool. What we build is a real context. So we tie those data automatically together so that you get real information out of all the data. And and and the third is that we provide actions. So basically use ai to figure out what the problem is and then automate things. Is it a problem resolution restarting a container or resizing your cloud? That's what we suggest automatically out of all the contacts and data that we've gathered. >>So you talk about automation context intelligence, you combine all that in one big bundle here, then basically um that's a big bundle, right? I'm not a giant vacuum if you will. You're ingesting all this information, you're looking for performance metrics. So you're trying to find problems um what's the complexity of tying all that together instead of keeping those functions separate? Um you know what or and what's the benefit to having all that kind of under one roof then? >>Yeah. So from the complexity point of view for the end customer, it's really easy because we do it automated for us as a vendor building this, it's super complex but we wanted to make it very easy for the user and I would say the benefit is that you get, we call it the mean time to repair, like the time from a problem to resolve the problem gets significantly reduced because normally you have to do that correlation of data manually and now with that context you get this automated by a machine and we even suggest you these intelligent actions to fix the problem. >>So so I'm sorry go ahead. >>Yeah. And by the way, one of the things why IBM acquired us and why we are so excited working together with IBM is the combination of that functionality with something like what's in a I ops because as I said, we are suggesting an action and the next step is really fully automating uh this action with something like what's new Ai Ops and the automation functionality that IBM has so that the end users are not only gets the information what to do the machine even does and fix the problem automatically. >>Mm Well, I'm wondering to just about about the kind of the volume that we're dealing with these days in terms of software capabilities and data, uh you've got obviously a lot more inputs, right, a lot more interaction going on, a lot more capabilities. Uh You've got apps uh they're kind of broken down the microservices now, so I mean you've got you got a lot more action basically, right, You've got a lot more going on and and um and what's the challenge to not only keeping up with that, but also building for the future for building for different kinds of capabilities and different kinds of interactions that maybe we can't even predict right now. >>Absolutely, yeah. So uh I'm 20 years in that space. And when I started, as you said, it was a very simple system. Right? You had an application server like web sphere, maybe a DB two database. So that was your applications like today. Applications are broken down and hundreds of little services that communicate with each other. And you can imagine if, if something breaks down in a system where you have two or three components, it's maybe not easy, but it's handled by a human to figure out what the problem is, if you have 1000 pieces that are somehow interconnected and something is broken. It is really hard to figure that out. And that's essentially the problem uh that we have to solve with the contacts with the automation, with ai to figure out how all these things are tied together and then analyze automatically for the user where issues are happening. And and and by the way, that's that's also when you look into the future, I think things will get more and more complicated. You can see now that people break down from micro service into functions. We get more serverless. We got to get more into a hybrid cloud environment where you operate on premise and in multiple clouds. So things get more complex, not less complex. From an architectural perspective, >>you bring up clouds to is this diagnostic I mean or do you work with a an exclusive cloud provider or you open for business? Basically >>we are open for business but but we have to support the different cloud technologies. So we support all the big public cloud vendors from, from IBM to amazon google Microsoft. But on the other hand, we see with enterprises Maybe there is 10 20 of the workload in the public cloud, but the rest is still on premises. And there's also a lot of legacy. So you have to bring all this together in one view and in one context. And that's one of the things we do. We not only support the modern cloud native applications, we also support the legacy on premise world, so that we can bring that together and that helps customer to migrate. Right? Because if they understand the workload in the on premise world, it's easier to transform that into a cloud native world. But it also gives an end to end view from the end user to we we always say from mobile to mainframe, right from a mobile app down to the mainframe application. We can give you an end to end view. >>Yeah, you talk about legacy uh in this case it may be cloud services that people use but there but you know, a lot of these legacy applications right to that are running that that are, they're still very useful and still highly functional, but at some point they're not going to be so would it be easier for you or what do you do in terms of talking with your clients in terms of what do they leave behind? What do they bring with them? How what kind of transition time frames should they be thinking about? Because I don't think you want to be supporting forever. Right. I mean, you you want to be evolving into newer, more efficient services and solutions and so you've got to bring them along too. I would think. Right. >>Yeah. But to be really honest, I think there are two ways of thinking. One is as as a vendor, you would love to support only the new technologies and don't have to support all the legacy technologies. But on the other hand, the reality is especially in bigger enterprises, you will find everything in every word. Right? And so if you want to give a holistic D view into the application stacks, you have to support also the older legacy parts because they are part of the business critical systems of the customer. And yes, we suggest to upgrade and go into a cloud native world. But being realistic, I think for the next decade We will have to live with a world where you have legacy and new things working together. I think that's just the reality. And in 10 years, what is new today is legacy then? Right. So we'll always, we will always live in a kind of hybrid world between legacy and and new things. >>Yeah, you got this technological continuum going on right. That you know that you know what's new and shiny today is going to be, you know, old hat in five years. But that's the beauty of it all. Obviously you talked about Ai Ops. Um, I mean let's go into that relationship a little bit if you would. I mean eventually what is observe ability set you up to do in terms of uh your artificial intelligence operations and what are the capabilities now that you're providing in terms of the observe ability solutions that Ai Ops can benefit from? >>So the way I think about these two categories is that observe abilities, the system of record. That's where all the data is collected and and put into context. So that's what we do as in stana is we take all the data metrics, locks, traces, profiles and put it into a system of record by the way in in in very high granularity. It's very important. So we, we do not sample. We have second granularity metrics. So very high quality data in that system of record where Ai ops is the system of action. This is a system where it takes the data that we have applies machine learning, statistical analytics etcetera on it to figure out for example root cause of problems or even predict problems in the future and then suggests actions. Right? What the next thing that AI does is it suggests or automates an action that you need to do to for example scale up the system, scale down the system scaling down because you want to safe cost for example these are all things that are happening in the system of action which is the IOP space >>when I think about what you're talking about in terms of observe ability. I think well who needs it? Everybody is probably the answer to that. Um Can you give us maybe just a couple of examples of some clients that you've worked with in terms of of particular needs that they had and then how you applied your observe ability platform to provide them with these kinds of solutions? >>Yeah I I remember a big e commerce vendor in the U. S. Approaching us. Uh last october they were approaching the black friday right where where they sell a lot of goods and and they had performance issues but they only had issues with certain types of customers and with their existing APM solution. They couldn't figure out where the problem is because existing solutions sample, which means if you have 1000 customers you only see one of them as an example because the other 999 are not in your in your sample. And so they used us because we don't sample with us. If you have they have more than a billion requests today. You see every of the one billion requests and offer a few days they had all the problems figure out. And that's what that was. One of the things that we really do differently is providing all the needed data, not sampling and then giving the context around the problem so that you can solve issues like performance issues on your e commerce system easily. So they switched and you can imagine switching the system before black friday, you only do that if it's really needed. So they were really under pressure and so they switched their A P. M. Tool to in stana to be able to to fulfill the big demand they have on these black friday days. >>All right. So uh I I before I let you go you were just saying they had a high degree of confidence. How are you sweating? That went out because that was not a small thing at all. I would I >>assume. Uh Yes, it's not a small thing. And to be honest also it's very hard to predict the traffic on black Fridays. Right? Uh And and in this case I remember our SRE team, they had almost 20 times the traffic of the normal day during that black friday. And we because we don't sample, we need to make sure that we can handle and process all these traces. But we did, we did pretty well. So I have high confidence in our platform that we can really handle big amounts of data. We have >>one >>of the biggest companies in the world, the biggest companies in these worlds. They use our tool to monitor billions of requests. So I think we have proven that it works. >>You know, I say you're smiling to about it. So I think it obviously it did work. It >>did work. But yeah, I'm sweating still. Yeah. >>Never let them see you sweat merkel. I think you're very good at that and obviously very good at enterprise observe ability. It's an interesting concept, certainly putting it well under practice and thanks for the time today to talk about it here as part of IBM think to, to share your company's success story. Thank you. Marco. >>Thanks for having me, john >>All right. We're talking about enterprise observe ability here. I P. M. Thank the initiative continues here on the cube. I'm john Walton. Thank you for joining us. >>Yeah. Mhm. >>Yeah.

>> Reporter: From New York City, it's theCUBE, covering New Relic FutureStack 2019, brought to you by New Relic. >> I'm Stu Miniman, we're here in New York City right next door to Grand Central Station, at the Grand Hyatt. This first year of theCUBE, attending New Relic's Futurestack, the seventh year of the show, and happy to welcome to the program, Guy Fighel, who's the vice president and general manager of New Relic AI of course, CEO was up on stage this morning announcing New Relic AI, it's in beta, Lew said expect early 2020 for to come out, so thank you so much for joining us. >> Thank you for being here. >> All right, so Guy, you came to New Relic by way of the acquisition of signifAI. And that ends in AI of course, even though we pronounce it signify, so help us understand is this a repackaging, rebranding you know, New Relic-izing the product that was through the acquisition, tell us how we've gotten here. >> Yes, sure, so New Relic AI is a whole new set of capabilities, it's a suite of capabilities that we are launching today in beta that pretty much augments the site reliability engineers with AI and ML capabilities. It runs on top of the New Relic One platform, which is the first observability platform that is connected, open, and programmable so you have all of the existing information and data that you already have inside New Relic. And we've incorporated a lot of the technologies and the techniques that we have developed as part of signifAI with existing capabilities that New Relic already had, and pretty much integrated all of that into single user experience and single type of capabilities across the stack. >> All right so, Guy, AI is a really broad category you know, you got your AI and ML and cognitive and you know all these things, what was kind of the core IP of SignifAI when they came in. >> Sure, so we really focused on correlating and reducing the noise of all of your different alerts and incidents but not just that, we've actually built a recommendation engine on top of that, to provide you much faster context to get into potential root cause of all of your different information focused on events. And now we're combining that with all the time series data that New Relic as a platform has to offer, so you're getting a much broader capabilities for understanding. >> Yeah, you know, definitely there's that promise of AI as we know that humans alone or my traditional tooling just can't keep up, you know, talk about all the different sources of data, the volume of data. I just saw Lew talking about the amount of the millions of items being ingested into the New Relic database, and the billions of items that are being read basically per second. So, help us understand. You say we love, we talk about our videos or extracting the signal from the noise, so, did I hear it was like 80, 85% your early customers are helping to reduce that noise Bring us in a little bit more. >> True, yes, so definitely early results shows us over 80% noise reduction for some of the customers and it is important to understand this is automatic relations, so this is truly based on the engines with no human interaction. Now, we actually have even greater results when some user input is driven into the system and that raises the capabilities as well. In terms of the number of events, yes, we are dealing with huge amount of events and information in the platform and I think it's, all around, not replacing the humans, but actually augmenting the site reliability engineers, so you talked about how systems, you know, there is a great promise for those capabilities. We believe that applied intelligence is a much better term, because it gives really enabling the augmentation for the site reliability engineers. We don't believe that site reliability engineers needs to go away or can even be replaced anytime soon. We definitely think that we can help them understand better and faster, what is the type of problems that they see in their production environments, and then help them resolve that much faster and better. >> Yeah, absolutely, we're huge supporters of really, the best solutions are when you have the people plus machines, there are certain things the machines are going to do on their own, but it's the marrying, so help us understand who's going to be using New Relic AI how is it going to change their day-to-day life and maybe even kind of organizationally, what the impact will be. >> Sure, so if you're a site reliability engineer, or a DevOps themed depending on, how you want to call yourself and, you know, there's a big debate in the industry, whether it's DevOps or site reliability engineers. Pretty much anyone who is responsible for Op time in the digital production environments you're a relevant user, If you carry the pager, if you're on call, you're a relevant user, so you're going to be interacting with the system to be able to actually see what are the problems with potential recommendations and then, you can infuse the system with your own logic. Whether it's based on the logic, we also provide very easy user experience we'd like thumbs up, thumbs down, different types of feedbacks as part of the workflow and I think the most important piece is that we're connecting to users where they are. Meaning, we don't believe we need to change the workflows so, if you're a user and you're already using with a specific internet management providers and you've already connected some of the additional monitoring tools to those providers, we now offer you a streamline of syncing to those instant management platforms and then, in reaching them with all of the information that we already have on the platform. >> So Guy, we've talked about AI but, let's talk a little bit about AI Ops. So, you know I've talked to the number of the vendors I actually went to an AI ops conference earlier this year and some of the talk track was, APM is the old way, AI ops is going to replace what you were doing before Let's take all your scattered tools and consolidate them down. some of the messaging reminds me of what I heard this morning, the New Relic One platform is going to replace a number of tools, pull everything together. Help us kind of, you know, square that circle of APM and AI ops and where you see New Relic compared to some of those competitors out there today. >> Sure, so APM is application performance monitoring. it's all about monitor and have that visibility to your application layer, it has nothing to do with AI ops it has nothing to do with replacing the tools. We believe that everyone should have visibility into their application, and that's, a lot of that messaging came through Lew's key note this morning, and opening it up to any type of open source instrumentation so we can bring it to the platform whether you want to drop an agent, whether you want to use any other open source SDK, we allow you to do that. Pretty much opening up the platform and giving you the option. AI ops is a term coined by Gardner actually, and it is pretty much applying some automation, AI capabilities, ML capabilities, statistical analysis capabilities on huge amount of data that you have in a centralized place. It has nothing to do with the monitoring, per se, so, I definitely think that the industry's going into a new space, where there is a consolidation obviously with different vendors. I believe that New Relic is giving customers the choice to make, whether they want to go and continue using their old tools, and that's okay, and we are an open platform so we will sync up with their data as part of New Relic AI we'll be able to bring in the new data whether by, again inter-connecting with their incident management platform or through a rest API or native integrations or if customer choose to do that, they can just send us all of the data directly and then, we apply the AI ops capabilities on top of the existing platforms. So, it's really opening up for the choice of the customer. >> All right it's been less than a year since the acquisition of SignifAI we know that some of the things when you do an acquisition it's an area of investment, you're going to get more resources, more people but, you've mentioned customers a couple of times, maybe give us a little bit of insight as to how the customer conversations have changed now working for New Relic, as opposed to being a customer understanding that piece of the New Relic ecosystem. >> Oh absolutely, I think, you know, as you transition from a small start up into a company like New Relic you get much more exposure to enterprise customer, your scaling capabilities are much better so we're in serious conversations with a lot of the enterprises customers that have a lot of interest in what we do. A lot of it is part of the branding recognition and all of the great capabilities that New Relic has already, and then marinade that with all of the capabilities that we're bringing or that we brought into New Relic as a young start-up with all of the latest technologies and a lot of the AI capabilities which are truly innovative ones, so definitely see a lot of traction from the enterprise customers, the more sophisticated ones as well. >> All right, so the solution announced today is in beta give us a little bit of a look forward as to what we should expect to see and what feedback you're hoping to get from customers along the way and how they might get engaged if they want to. >> Yeah so definitely we are in beta today. We've engaged with customers prior to the beta, so, we already got a lot of feedback and great feedback and we make some tweaks to the product based on that. We're actually announcing AGI of a small feature today which is enhanced incident context, which provides you active detection for time series data all the way to your slack channels but the overall solution is currently in beta and as we are progressing, within every month we're going to get more and more customers engaging with the platform, and then we're going to release a much more advanced capabilities even than what we have today in GA coming early next year. >> All right great, last thing, big mention and push about observability this morning, help us understand where AI fits into the broader discussion of observability. >> So again, as I mentioned before the observability will allow you to see all of your data in a centralized place. So, it's combining matrix, events, logs and traces in a specific place that now algorithms and different techniques such as AI and ML based algorithms really, really be successful in gathering, understanding, because you have all of that different information for the human brain, it's very hard to actually go and crawl and kind of ingest all of that vast amount of different data points for machines, they're very good at that. They're starving for broad amount of data and so having that capability, building on top of a true observability platform is what makes the AI and ML so successful and drive value to customers in really understanding what the data means. >> All right well, Guy thank you so much for sharing best of luck on the journey towards GA for the the full New Relic AI in the future. We look forward to, launching it. >> Thank you so much. >> All right and once more here, walking through at the New Relic Futurestack 2019, here in New York City. I'm Stu Miniman and thanks for watching theCUBE. (upbeat music)