>>Emily Freeman is here. She's ready to come in and we're going to preview her uh, 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, 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, you know, 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, 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 lifecycle. 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 use personas all the time in tech 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 or your 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, >>you know, lisa 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 their mixing and matching on teams. So you're, you're an SRE on one team and you're doing something on another team where the workflows and the workloads define 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 desktops, 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 going to 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 and 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, you 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 premiere of this revolution in DeVOps and we Freeman's talking, 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'm 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 2000 >>and eight, 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. DeVos 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. 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 DLC >>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 splc 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. >>No, 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. Yeah >>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 security administrator versus a security engineer. But perhaps most critically, I believe personas are immutable. Mhm. 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. Mhm 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. >>We're 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 that 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. The cops. 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, when and how and that is everyone's responsibility at every stage, site, reliability, engineering or SRE is a discipline and 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 through but 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 when 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 in 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 isn't 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. Mm.

(upbeat music) >> Hi, I'm Emily Freeman, author of DevOps for dummies. My talk revolution in DevOps discusses a wild idea that we should throw away the SDLC. That's right. The software development or delivery lifecycle. The thing we talk about all the time has been around since the 1960s. And I think it's time for a refresh. I hope you'll join me at the AWS startup showcase, where we discuss new breakthroughs in DevOps, data analytics, and cloud management tools. It's on September 22nd at 9:00 AM. Pacific. Hope to see you there. (cheerful music)

>>Welcome to the cube conversation here. I'm Sean ferry with the cube in Palo Alto, California. We've got two great guests here featuring armory who has with them Patrion open-source and talking open source and the enterprise. I'm your host, John ferry with the cube. Thanks for watching guys. Thanks for coming on. Really appreciate. I've got two great guests, Ben mapping, and SVP, a strategic partner in the armory and Ian Della horn, S staff SRE at Patrion gentlemen, you know, open source and enterprise is here and we wouldn't talk about thanks for coming. I appreciate it. >>Yeah. Thank you, John. Really happy to be here. Thank you to the Cuban and your whole crew. I'll start with a quick intro. My name is Ben Mappin, farmers founders, lead strategic partnerships. As John mentioned, you know, it all, it really starts with a premise that traditional businesses, such as hotels, banks, car manufacturers are now acting and behaving much more like software companies than they did in the past. And so if you believe that that's true. What does it mean? It means that these businesses need to get great at delivering their software and specifically to the cloud, like AWS. And that's exactly what armory aims to do for our customers. We're based on opensource Spinnaker, which is a continuous delivery platform. And, and I'm very happy that Ian from Patrion is here to talk about our journey together >>And introduce yourself what you do at Patriot and when Patrion does, and then why you guys together here? What's the, what's the story? >>Absolutely. Hi, John and Ben. Thanks for, thanks for having me. So I am Ian. I am a site reliability engineer at Patrion and Patrion is a membership platform for creators. And what we're our mission is to get creators paid, changing the way the art is valued so that creators can make money by having a membership relationship with, with fans. And we are, we're built on top of AWS and we are using Spinnaker with armory to deploy our applications that, you know, help, help creators get paid. Basically >>Talk about the original story of Ben. How are you guys together? What brought you together? Obviously patron is well-known in the creator circles. Congratulations, by the way, all your success. You've done a great service for the industry and have changed the game you were doing creators before it was fashionable. And also you got some cutting-edge decentralization business models as well. So again, we'll come back to that in a minute, but Ben, talk about how this all comes together. Yeah, >>Yeah. So Ian's got a great kind of origin story on our relationship together. I'll give him a lead in which is, you know, what we've learned over the years from our large customers is that in order to get great at deploying software, it really comes down to three things or at least three things. The first being velocity, you have to ship your software with velocity. So if you're deploying your software once a quarter or even once a year, that does no good to your customers or to your business, like just code sitting in a feature branch on a shelf, more or less not creating any business value. So you have to ship with speed. Second, you have to ship with reliability. So invariably there will be bugs. There will be some outages, but you know, one of the things that armory provides with Spinnaker open sources, the ability to create hardened deployment pipeline so that you're testing the right things at the right times with the right folks involved to do reviews. >>And if there is hopefully not, but if there is a problem in production, you're isolating that problem to a small group of users. And then we call this the progressive deployment or Canary deployment where you're deploying to a small number of users. You measure the results, make sure it's good, expand it and expand it. And so I think, you know, preventing outages is incredibly incredibly important. And then the last thing is being able to deploy multi target multi-cloud. And so in the AWS ecosystem, we're talking about ECS, EKS Lambda. And so I think that these pieces of value or kind of the, the pain points that, that enterprises face can resonate with a lot of companies out there, including ENN Patriot. And so I'll, I'll, I'll let you tell the story. >>Yeah, go ahead. Absolutely. Thanks. Thanks for the intro, man. So background background of our partnership with armory as back in the backend, February of 2019, we had a payments payments slowed down for payments processing, and we were risking not getting creators paid on time, which is a doc great for creators because they rely on us for income to be able to pay themselves, pay their rent or mortgage, but also pay staff because they have video editors, website admins, people that nature work with them. And there were, they're a very, there's a very many root causes to this, to this incident, all kind of culminate at once. One of the things that we saw was that deploying D point fixes to remediate. This took too long or taking at least 45 minutes to deploy a new version of the application. And so we've had continuous delivery before using a custom custom home built, rolling deploy. >>We needed to get that time down. We also needed to be secure in our knowledge of like that deploy was stable. So we had had to place a break in the middle due to various factors that that can happen during the deploy previously, I had used a Spinnaker at previous employers. I have been set it up myself and introduced it. And I knew about, I knew like, oh, this is something we could, this would be great. But the Patriot team, the patron SRE team at that time was two people. So I don't have the ability to manage Spinnaker on my own. It's a complex open-source product. It can do a lot of things. There's a lot of knobs to tweak a lot of various settings and stuff you need to know about tangentially. One of the co-founders of, of armory had been, had to hit, had hit me up earlier. I was like, Hey, have you heard of armory? We're doing this thing, opens our Spinnaker, we're packaging this and managing it, check us out if you want. I kind of like filed it away. Like, okay, well that might be something we can use later. And then like two weeks later, I was like, oh wait, this company that does Spinnaker, I know of them. We should probably have a conversation with them and engage with them. >>And so you hit him up and said, Hey, too many knobs and buttons to push what's the deal. >>Yeah, exactly. Yeah. So I was, I was like, Hey, so by the way, I about that thing, how, how soon can you get someone get someone over here? >>So Ben take us through the progression. Cause that really is how things work in the open source. Open source is really one of those things where a lot of community outreach, a lot of people are literally a one degree or two separation from someone who either wrote the project or is involved in the project. Here's a great example. He saw the need for Spinnaker. The business model was there for him to solve. Okay. Fixes rolling deployments, homegrown all the things, pick your pick, your use case, but he wanted to make it easier. This tends to, this is kind of a pattern. What did you guys do? What's the next step? How did this go from here? >>Yeah. You know, Spinnaker being source is critical to armory's success. Many companies, not just pastry on open source software, I think is not really debatable anymore in terms of being applicable to enterprise companies. But the thing with selling open source software to large companies is that they need a backstop. They need not just enterprise support, but they need features and functionality that enable them to use that software at scale and safely. And so those are really the things that, that we focus on and we use open source as a really, it's a great community to collaborate and to contribute fixes that other companies can use. Other companies contribute fixes and functionality that we then use. But it's, it's really a great place to get feedback and to find new customers that perhaps need that enhanced level of functionality and support. And, and I'm very, very happy that Patrion was one of those companies. >>Okay. So let's talk about the Patrion. Okay. Obviously scaling is a big part of it. You're an SRE site, reliability engineers with folks who don't know what that is, is your, your job is essentially, you know, managing scale. Some say you the dev ops manager, but that's not really right answer. What is the SRE role at patriotics share with folks out there who are either having an SRE. They don't even know it yet or need to have SRS because this is a huge transition that, and new, new and emerging must have role in companies, >>Right? Yeah. We're the history of Patrion covers a lot. We cover a wide swath of a wide swath of, of, of things that we work with and, and areas that we consider to be our, our purview. Not only are we working on working with our AWS environment, but we also are involved in how can we make the site more reliable or performance so that, so that creators fans have a good experience. So we work with our content delivery numbers or caching strategies for caching caching assets. We work inside the application itself for doing performance performance, a hassle. This is also in proving observability with distributed tracing and metrics on a lot of that stuff, but also on the build and deploy side, if we can, if we can get that deploy time faster, like give engineers faster feedback on features that they're working on or bug fixes and also being secure and knowing that the, the code that they're working on it gets delivered reliably. >>Yeah. I think I, you have the continuous delivery is always the, the, the killer killer workflow as both the Spinnaker question here. Well, how has Spinnaker, well, what, how, how does Spinnaker being an open source project help you guys? I mean, obviously open source code is great. How has that been significant and beneficial for both armory and Patrion? >>Yeah, I'll take the first stab at this one. And it starts at the beginning. Spinnaker was created by Netflix and since Netflix open source that four or five years ago, there have been countless and significant contributions from many other companies, including armory, including AWS and those contributions collectively push the industry forward and allow the, the companies that, you know, that use open-source Spinnaker or armory, they can now benefit from all of the collective effort together. So just that community aspect working together is huge. Absolutely huge. And, you know, open source, I guess on the go-to-market side is a big driver for us. You know, there's many, many companies using open-source Spinnaker in production that are not our customers yet. And we, we survey them. We want to know how they're using open-source Spinnaker so that we can then improve open-source Spinnaker, but also build features that are critical for large companies to run at scale, deploy at scale, deploy with velocity and with reliability. >>Yeah. What's your take on, on the benefits of Spinnaker being open source? >>A lot of what Ben, it's been really beneficial to be able to like, be able to go in and look at the source code for components. I've been wondering something like, why is this thing working like this? Or how did they solve this? It's also been useful for, I can go ask the community for, for advice on things. If armory doesn't has the, it doesn't have the time or bandwidth to work on some things I've been able to ask the special interest groups in the source community. Like, can we, can we help improve this or something like that. And I've also been able to commit simple bug fixes for features that I've, that I've needed. I was like, well, I don't need to, I don't need to go engage are very on this. I can just like, I can just write up a simple patch on and have that out for review. >>You know, that's the beautiful thing about open sources. You get the source code and that's, and some people just think it's so easy, Ben, you know, just, Hey, just give me the open source. I'll code it. I got an unlimited resource team. Not, not always the case. So I gotta ask you guys on Patrion. Why use a company like armory, if you have the open source code and armory, why did you build a business on the open source project? Like Spinnaker? >>Yeah. Like I see. Absolutely. Yeah. Like I, like I said earlier, the atrium, the Patrion SRE team was wasn't is fairly small. There's two people. Now we're six. People are still people down. We're six people now. So being sure we could run a Spinnaker on our own if we, if we wanted to. And, but then we'd have no time to do anything else basically. And that's not the best use of our, of our creators money. Our fans, the fans being the creators artists. We have obviously take a percentage on top of that. And we, we need to spend our, that money well, and having armory who's dedicated to the Spinnaker is dedicated, involved the open source project. But also there are experts on this Sunday. It was something that would take me like a week of stumbling around trying to find documentation on how to set this thing up. They done this like 15, 20 times and they can just go, oh yeah, this is what we do for this. And let me go fix it for you >>At score. You know, you've got a teammate. I think that's where, what you're getting at. I got to ask you what other things is that free you up? Because this is the classic business model of life. You know, you have a partner you're moving fast, it slows you down to get into it. Sure. You can do it yourself, but why it's faster to go with it, go together with a partner and a wing man as we will. What things did does that free you up to work on as an SRE? >>Oh, that's freed me up to work on a bigger parts of our build and deploy pipeline. It's freed me up to work on moving from a usage based deploys onto a containerization strategy. It's freed me up to work on more broader observability issues instead of just being laser-focused on running an operating spending. >>Yeah. And that really kind of highlights. I'm glad you said that because it highlights what's going on. You had a lot of speed and velocity. You've got scale, you've got security and you've got new challenges you got to fix in and move fast. It's a whole new world. So again, this is why I love cloud native. Right? So you got open source, you got scale and you guys are applying directly to the, to the infrastructure of the business. So Ben, I got to ask you armory. Co-founder why did you guys build your business on an open source project? Like Spinnaker? What was the mindset? How did you attack this? What did you guys do? Take us through that piece because this is truly a great entrepreneurial story about open source. >>Yeah. Yeah. I'll give you the abridged version, which is that my co-founders and I, we solved the same problem, which is CD at a previous company, but we did it kind of the old fashioned way we home role. We handled it ourselves. We built it on top of Jenkins and it was great for that company, but, and that was kind of the inspiration for us to then ask questions. Hey, is this bigger? We, when at the time we found that Spinnaker had just been, you know, dog food inside of Netflix and they were open sourcing it. And we thought it was a great opportunity for us to partner. But the bigger reason is that Spinnaker is a platform that deploys to other platforms like AWS and Kubernetes and the sheer amount of surface area that's required to build a great product is enormous. And I actually believe that the only way to be successful in this space is to be open source, to have a community of large companies and passionate developers that contribute the roads if you will, to deploy into various targets. >>And so that's the reason, number one for it being open source and us wanting to build our business on top of open source. And then the second reason is because we focus almost exclusively on solving enterprise scale problems. We have a platform that needs to be extensible and open source is by definition extensible. So our customers, I mean, Ian just had a great example, right? Like he needed to fix something he was able to do so solve it in open source. And then, you know, shortly thereafter that that fix in mainline gets into the armory official build and then he can consume his fix. So we see a lot of that from our other customers. And then even, you know, take a very, very large company. They may have custom off that they need to integrate with, but that doesn't, that's not in open-source Spinnaker, but they can go and build that themselves. >>Yeah, it's real. It really is the new modern way to develop. And I, you know, last 80 with startup showcase last season, Emily Freeman gave a talk on, you know, you know, retiring, I call it killing the software, SDLC, the lifecycle of how software was developed in the past. And I got to ask you guys, and, and this cube conversation is that this is kind of like the, the kind of the big wave we're on now is cloud scale, open source, cloud, native data security, all being built in on this in the pipelines to your point is SRS enabling a new infrastructure and a new environment for people to build essentially SAS. So I got to ask you guys as, and you mentioned it Ben, the old way you hand rolled something, Netflix, open source, something, you got to look at Lyft with Envoy. I mean, large-scale comes, are donating their stuff into open source and people getting on top of it and building it. So the world's changed. So we've got to ask you, what's the difference between standing up a SAS application today versus say five to eight years ago, because we all see salesforce.com. You know, they're out there, they built their own data center. Cloud skills changed the dynamics of how software is being built. And with open-source accelerating every quarter, you're seeing more growth in software. How has building a platform for applications changed and how has that changed? How people build SAS applications, Ben, what's your take on this? It's kind of a thought exercise here. >>Yeah. I mean, I wouldn't even call it a thought exercise. We're seeing it firsthand from our customers. And then I'll, you know, I'll, I'll give my answer and you can weigh in on like practical, like what you're actually doing at Patrion with SAS, but the, the costs and the kind of entry fee, if you will, for building a SAS application has tremendously dropped. You don't need to buy servers and put them inside data centers anymore. You just spin up a VM or Kubernetes cluster with AWS. AWS has led the way in public cloud to make this incredible easy. And the tool sets being built around cloud native, like armory and like many other companies in the space are making it even easier. So we're just seeing the proliferation of, of software being developed and, and hopefully, you know, armory is playing a role in, in making it easier and better. >>So before we get to Unum for a second, I just want to just double down on it because there's great conversation that implies that there's going to be a new migration of apps everywhere, right. As tsunami of clutter good or bad, is that good or bad or is it all open source? Is it all good then? >>Absolutely good. For sure. There will be, you know, good stuff developed and not so good stuff developed, but survival of the fittest will hopefully promote those, the best apps with the highest value to the end user and, and society at large and push us all forward. So, >>And what's your take, obviously, Kubernetes, you seeing things like observability talking about how we're managing stateful and services that are being deployed and tear down in real time, automated, all new things are developing. How does building a true scalable SAS application change today versus say five, eight years ago? >>I mean, like you said, there's a, there's a lot, there's a lot of new, both open source. So SAS products available that you can use to build a scale stuff. Like if you're going to need that to build like secure authentication, instead of having to roll that out and you could go with something like Okta raw zero, you can just pull that off the shelf stuff for like managing push notifications before that was like something really hard to really hard to do. Then Firebase came on the scene and also for manic state and application and stuff like that. And also for like being, being able to deliver before >>You had Jenkins, maybe even for that, you didn't really have anything Jenkins came along. And then now you have open-source products like Spinnaker that you can use to deliver. And then you have companies built around that, that you can just go and say, Hey, can you please help us deliver this? Like you just help us, enable us to be able to build, build our products so that we can focus on delivering value to our creators and fans instead of having to focus on, on other things. >>So bill it builds faster. You can compose stuff faster. You don't have to roll your own code. You can just roll your own modules basically, and then exactly what prietary on top of it. Absolutely. Yeah. And that's why commercial open source is booming. Guys. Thank you so much, Ben, congratulations on armory and great to have you on from Patrion well-known success. So we'll accompany you congratulate. If we don't know patriarch, check it out, they have changed the game on creators and leading the industry. Ben. Great, great shot with armory and Spinnaker. Thanks for coming on. Thank you >>So much. Thank you >>So much. Okay. I'm Sean Ferrer here with the cube conversation with Palo Alto. Thanks for watching.

value in jobs is probably the most rewarding >>things I've ever been involved >>in And I bring that energy to the queue because the cube is where all the ideas are and where the experts are, where the people are And I think what's most exciting about the cube is that we get to talk to people who are making things happen, entrepreneurs ceo of companies, venture capitalists, people who are really on a day in and day out basis, building great companies and the technology business is just not a lot of real time live tv coverage and and the cube is a non linear tv operation. We do everything that the T. V guys on cable don't do. We do longer interviews. We asked tougher questions. We >>ask sometimes some light questions. We talked about the person and what >>they feel about it's not prompted and scripted. It's a conversation authentic and for shows that have the cube coverage and makes the show buzz that creates excitement. More importantly, it creates great content, great digital assets that can be shared instantaneously to the world. Over 31 million people have viewed the cube and that is the result of great content, great conversations and I'm so proud to be part of a Q with great team. Hi, I'm john barrier, Thanks for watching the cube boy. >>Okay, welcome back everyone cube coverage of AWS amazon web services public sector summit in person here in Washington D. C. I'm john Kerry host of the cube with Sandy carter and Lynn martin Vm ware Vice president of government education and healthcare. Great to see you both cube alumni's although she's been on since 2014 your first time in 2018 18 2018. Great to see you. Great to see you. Thanks for coming on. Yeah, thanks for having us. So VM ware and 80 of us have a huge partnership. We've covered that announcement when Andy and Pat nelson was the Ceo. Then a lots happened, a lot of growth. A lot of success. Congratulations. Thank you. What's the big news with AWS this year in >>public sector. So we just received our authorization to operate for Fed ramp high. Um and we actually have a lot of joint roadmap planning. You are kicking off our job today with the Department of Defense and I. L five for the defense customers is also in process. So um a lot of fruits of a long time of labor. So very excited, >>awesome. So explain what does the Fed ramp authority to operate mean? What is >>that all about? So I would say in a nutshell, it's really putting a commercial offering through the security protocols to support the federal government needs. Um and there's different layers of that depending on the end user customers. So Fed ramp i across this, across all the civilian and non classified workloads in the federal government. Um probably applicability for state, local government as well with the new state Gramp focus. Um Fed ramp. I will meet or exceed that. So it will be applicable across the other parts of the government as well and all operated, you know, in a controlled environment jointly. So you get the VM ware software stack on top of the platform from A W. S and all the services that is more VM >>ware, faster deployed usage, faster acceleration. >>Yeah, so I would say um today the government operates on VM ware across all of the government, state, local and federal, um some workloads are still on prem many and this will really accelerate that transformation journey to the cloud and be able to move workloads quicker onto the BMC on AWS platform without free architect in your >>application, without giving away any kind of VM World Secret because that's next week. What is the value proposition of VM ware cloud, on AWS? What is the, what is the, what is the main value proposition you guys see in the public >>sector? So I see three and then Sandy chime in their two, I would say, you know, the costs in general to operate In the Cloud vs on prem or significant savings, we've seen savings over 300% on some customers. Um the speed on the application movement I think is a >>huge >>unique benefit on BMC on AWS. So traditionally to move to native cloud, you have to really do a lot of application were to be able to move those workloads where on BMC on AWS to move them pretty fast. And it also leverages the investments that the government agencies have already made in their operational tools and things of that nature. So it's not like a full reinvestment for something new but really leveraging both the skill sets in the data center in the I. T. Shops and the tools and investments you've bought over the past. And then the third area I would say is really getting the agility and flexibility and speed of a cloud experience. >>What's your, what's your reaction to the partnership? >>You know, we were just talking uh in a survey to our customers and 67% of them said that the velocity of the migration really matters to them. And one of the things that we do really well together is migrate very quickly, so we have workloads that we've migrated that have taken you know weeks months uh as opposed to years as they go over, which is really powerful. And then also tomorrow VM ware is with us in a session on data led migration. We were talking about data earlier and VM ware cloud on Aws also helps to migrate over like sequel server, database oracle databases so that we can also leverage that data now on the cloud to make better decisions and >>real time decisions as >>well. It's been really interesting to watch the partnership and watching VM ware transform as well, not only the migrations are in play with the public sector, there's a lot of them, believe me, healthcare, you name every area. It's all, all those old systems are out there. You know, I'm talking about out there. But now with microservices and containers, you've got tansy and you got the whole cloud, native VM ware stack emerging that's going to allow customers to re factor This is a dynamic that is kind of under reported >>Migration is one thing. But I think, I think that the whole Tan Xue portfolio is one of the most interesting things going on in VM ware. And we also have some integration going on on D. M. C on AWS with tan to we don't have that pentagram. Yeah. For the government market, but it's on the road mapping plans and we have other customers And I would say, you know, some of my non federal government customers were able to move workloads in hours, not even days or weeks. There you go, literally back and forth. And very impressive on the BMC on AWS platform. So, um, as we expand things in with the Tan Xue platform is, you know, Sandy talked about this yesterday and our partners summit, Everyone's talking about containers and things like that. VM ware is doing a lot of investment around the cooper Netease plus the application migration work and things of that nature. >>I'd love to get you guys reaction to this comment because I've seen a lot of change. Obviously we're all seeing it. I've actually interviewed a bunch of aWS and VM ware customers and I would call um some of the categories skeptics the old school cloud holding the line. And then when the pandemic hit those skeptics flip over because they see the value. In fact I actually interviewed a skeptic who became an award winner who went on the record and said I love hey w I love the cloud. I was a skeptic because you saw the value the time to value. This is really a key dynamic. I know it's kind of thrown out a lot of digital transformation or I. T. Modernization but the agility and that kind of speed. It becomes the number one thing. What's your reaction to the skeptics converting? And then what happens >>next? Um So I think there's still a lot of folks in I. T. That our tree huggers or I call him several huggers uh um pick your term. And I think that um there is some concern about what their role will be. So I think one of the differences delivering cloud services to your internal constituents is really understand the business value of the applications and what that delivers from a mission perspective back to your client. And that's a shift for data center owners to really start thinking more from the customer mission perspective than or my servers running you know, do you have enough storage capacity blah blah blah. So I think that creates that skepticism and part of that's around what's my role going to be. So in the cloud transformation of a customer, there's all this old people part that becomes really the catalyst and I think the customers that have been very sad and really leverage that and then retool the business value back to the end users around the mission have done the best job. >>I mean we talk about this all the time, it's really hard to get the best debris partners together and then make it all work cloud, it becomes easier than doing it very bespoke or waterfall way >>Yeah, I have to say with the announcement yesterday, we're going to have a lot more partner with partners. So you and I have talked about this a few times where we bring partners together to work with each other. In fact, Lynn is going to go meet with one of those partners right after the interview um that want to really focus in on a couple of particular areas to really drive this and I think, you know, part of the, you know, as your re factoring or migrating VMro over the other big benefit is skills, people have really strong, these fear skills, the sand skills, >>operation >>operation tools Yeah. And so they want to preserve those, I think that's part of the beauty of doing VM ware cloud on Aws is you get to take those skills with you into the new world as well, >>you know, I was going to just ask the next question ai ops or day two operations, a big buzzword Yeah and that is essentially operation mindset, that devoPS DEVOps two is coming. Emily Freeman gave a keynote with our last event we had with with amazon public showcase revolution and devops devoPS 2.0 is coming which is now faster, security is built in the front end, so all these things are happening so now it's coming into the public sector with the GovCloud. So I have to ask you Lynn what are some of the big successes you've had with on the gulf cloudy, just Govcloud. >>So I would say we've had a lot of customers across the state local side especially um that weren't waiting for fed ramp and those customers were able to move like I mentioned this earlier and you guys just touched on it. So I think the benefit and the benefit, one of our best customers is Emmett Right? Absolutely mitt, God bless them. They've been on every cloud journey with VM ware since 2014 we moved in my three years now and talk about a skeptic. So although Mark is very revolutionary and tries new things, he was like oh who knows and literally when we moved those workloads it was minutes and the I. T shop day one there was no transformation work for them, it was literally using all the tools and things in that environment. So the progress of that and the growth of the applications that have been able to move their things. That took 2 to 3 years before we're all done within six months and really being able to expand those business values back out for the services that he delivers to the customers. So I think you'll see quite a bit across state, local federal government. You know, we have U. S. Marshals, thank them very much. They were our sponsor that we've been working with the last few years. We have a defense customer working with us around aisle five. >>Um you know, if we could also thank Coal Fire because Cold Fire is one of our joint partners talking about partner partners and they were played a critical role in helping BM We're cloud on AWS and get the fed ramp high certifications. >>They were R three p. O. We hired them for their exercise expertise with AWS as well as helping the BMR. >>Well the partnership with the war has been a really big success. Remember the naysayers when that was announced? Um it really has worked out well for you guys. Um I do want to ask you one more thing and we don't mind. Um One of the biggest challenges that you see the blockers or challenges from agencies moving to the cloud cover cloud because you know, people are always trying to get those blockers out of the way but it's an organizational culture is a process technology. What's your what's your take on that land. Um >>I think a lot does have to do with the people and the organizational history. I think somewhere you need a leader and a champion that really wants to change for good. I call Pat, used to call a tech for good. I love that. Right to really, you know, get things moving for the customers. I mean one of the things I'm most proud about supporting the government business in general though is really the focus on the mission is unparalleled, you know, in the sectors we support, you say, education or government or healthcare. Right? All three of those sectors, there's never any doubt on what that focuses. So I think the positives of it are like, how do you get into that change around that? And that could be systems, there's less what's VMC ON AWS as we mentioned, because the tools already in the environment so they know how to use it. But I do think there's a transformation on the data center teams and really becoming moving from technology to the business aspects a little bit more around the missions and things of that. >>What's interesting is that it's so, I mean, I actually love this environment even though it's kind of hard on everyone. Education and health care have been disrupted unprecedented ways and it's never gonna change back? Remember healthcare, hip data silos, silos, education don't spend on it. >>That education was the most remarkable part. Unbelievable. I started working in february before school started with one of the large cities everyone can guess and just the way they were able to pivot so fast was amazing and I don't think anybody, I think we did like five years of transformation in six months and it's never going to go back. >>I completely a great yes education. We just did a piece of work with CTS around the world and education is one of the most disrupted as you said health care and then the third one is government and all three of those are public sector. So the three most disruptive sectors or mission areas are in public sector which has created a lot of opportunity for us and our partnership to add value. I mean that's what we're all about right customer obsession working backwards from the customer and making sure that our partnership continues to add value to those customers >>while we love the tech action on the cube. Obviously we'd like to document and pontificate and talk about it. Digital revolution. Every application now is in play globally. Not just for I. T. But for society, public sector more than ever is the hottest area on the planet. >>Absolutely. And I would say that now our customers are looking at E. S. G. Environmental, they want to know what you're doing on sustainability. They want to know what you're doing for society. We just had a bid that came in and they wanted to understand our diversity plan and then open governance. They're looking for that openness. They're not just artificial intelligence but looking at explainable AI as well. So I think that we have a chance to impact environment societies and governance >>and you mentioned space earlier. Another way I talked with closure. I mean I'm an interview today too, but what's happening with space and what you can monitor disasters, understand how to deploy resources to areas that might have challenges, earthquakes or fires or other things. All new things are happening. >>Absolutely. And all that data people like to say, why are you spending money on space? There's so many problems here, but that data that comes from space is going to impact us here on earth. And so all the things that we're doing, all that data could be used with VM ware cloud on AWS as well. >>Well, you watch closely we got some space coverage coming. I got a big scoop. I'm gonna release soon about something behind the dark side of the moon on in terms of space sovereignty coming a lot of action, cybersecurity in space. That's really heavy right now. But >>aren't you glad that VMC cloud on AWS isn't hidden on the dark side of the moon. It's >>right on the congratulations. Thanks for coming on. You guys are doing great. Thanks for >>thanks for sharing. Congratulations. >>Okay, cube coverage here continues. AWS public sector summit in Washington D. C live for two days of coverage be right back. Thank you. Mhm. Mhm mm mm hmm.

>>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.

>>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.

(upbeat music) >> Hello and welcome to this CUBE conversation here presenting the "AWS Startup Showcase: "New Breakthroughs in DevOps, Data Analytics "and Cloud Management Tools" featuring Opsani for the cloud management and migration track here today, I'm your host John Furrier. Today, we're joined by Patrick Conte, Chief Commercial Officer, Opsani. Thanks for coming on. Appreciate you coming on. Future of AI operations. >> Thanks, John. Great to be here. Appreciate being with you. >> So congratulations on all your success being showcased here as part of the Startups Showcase, future of AI operations. You've got the cloud scale happening. A lot of new transitions in this quote digital transformation as cloud scales goes next generation. DevOps revolution as Emily Freeman pointed out in her keynote. What's the problem statement that you guys are focused on? Obviously, AI involves a lot of automation. I can imagine there's a data problem in there somewhere. What's the core problem that you guys are focused on? >> Yeah, it's interesting because there are a lot of companies that focus on trying to help other companies optimize what they're doing in the cloud, whether it's cost or whether it's performance or something else. We felt very strongly that AI was the way to do that. I've got a slide prepared, and maybe we can take a quick look at that, and that'll talk about the three elements or dimensions of the problem. So we think about cloud services and the challenge of delivering cloud services. You've really got three things that customers are trying to solve for. They're trying to solve for performance, they're trying to solve for the best performance, and, ultimately, scalability. I mean, applications are growing really quickly especially in this current timeframe with cloud services and whatnot. They're trying to keep costs under control because certainly, it can get way out of control in the cloud since you don't own the infrastructure, and more importantly than anything else which is why it's at the bottom sort of at the foundation of all this, is they want their applications to be a really a good experience for their customers. So our customer's customer is actually who we're trying to solve this problem for. So what we've done is we've built a platform that uses AI and machine learning to optimize, meaning tune, all of the key parameters of a cloud application. So those are things like the CPU usage, the memory usage, the number of replicas in a Kubernetes or container environment, those kinds of things. It seems like it would be simple just to grab some values and plug 'em in, but it's not. It's actually the combination of them has to be right. Otherwise, you get delays or faults or other problems with the application. >> Andrew, if you can bring that slide back up for a second. I want to just ask one quick question on the problem statement. You got expenditures, performance, customer experience kind of on the sides there. Do you see this tip a certain way depending upon use cases? I mean, is there one thing that jumps out at you, Patrick, from your customer's customer's standpoint? Obviously, customer experience is the outcome. That's the app, whatever. That's whatever we got going on there. >> Sure. >> But is there patterns 'cause you can have good performance, but then budget overruns. Or all of them could be failing. Talk about this dynamic with this triangle. >> Well, without AI, without machine learning, you can solve for one of these, only one, right? So if you want to solve for performance like you said, your costs may overrun, and you're probably not going to have control of the customer experience. If you want to solve for one of the others, you're going to have to sacrifice the other two. With machine learning though, we can actually balance that, and it isn't a perfect balance, and the question you asked is really a great one. Sometimes, you want to over-correct on something. Sometimes, scalability is more important than cost, but what we're going to do because of our machine learning capability, we're going to always make sure that you're never spending more than you should spend, so we're always going to make sure that you have the best cost for whatever the performance and reliability factors that you you want to have are. >> Yeah, I can imagine. Some people leave services on. Happened to us one time. An intern left one of the services on, and like where did that bill come from? So kind of looked back, we had to kind of fix that. There's a ton of action, but I got to ask you, what are customers looking for with you guys? I mean, as they look at Opsani, what you guys are offering, what's different than what other people might be proposing with optimization solutions? >> Sure. Well, why don't we bring up the second slide, and this'll illustrate some of the differences, and we can talk through some of this stuff as well. So really, the area that we play in is called AIOps, and that's sort of a new area, if you will, over the last few years, and really what it means is applying intelligence to your cloud operations, and those cloud operations could be development operations, or they could be production operations. And what this slide is really representing is in the upper slide, that's sort of the way customers experience their DevOps model today. Somebody says we need an application or we need a feature, the developers pull down something from get. They hack an early version of it. They run through some tests. They size it whatever way they know that it won't fail, and then they throw it over to the SREs to try to tune it before they shove it out into production, but nobody really sizes it properly. It's not optimized, and so it's not tuned either. When it goes into production, it's just the first combination of settings that work. So what happens is undoubtedly, there's some type of a problem, a fault or a delay, or you push new code, or there's a change in traffic. Something happens, and then, you've got to figure out what the heck. So what happens then is you use your tools. First thing you do is you over-provision everything. That's what everybody does, they over-provision and try to soak up the problem. But that doesn't solve it because now, your costs are going crazy. You've got to go back and find out and try as best you can to get root cause. You go back to the tests, and you're trying to find something in the test phase that might be an indicator. Eventually your developers have to hack a hot fix, and the conveyor belt sort of keeps on going. We've tested this model on every single customer that we've spoken to, and they've all said this is what they experience on a day-to-day basis. Now, if we can go back to the side, let's talk about the second part which is what we do and what makes us different. So on the bottom of this slide, you'll see it's really a shift-left model. What we do is we plug in in the production phase, and as I mentioned earlier, what we're doing is we're tuning all those cloud parameters. We're tuning the CPU, the memory, the Replicas, all those kinds of things. We're tuning them all in concert, and we're doing it at machine speed, so that's how the customer gets the best performance, the best reliability at the best cost. That's the way we're able to achieve that is because we're iterating this thing in machine speed, but there's one other place where we plug in and we help the whole concept of AIOps and DevOps, and that is we can plug in in the test phase as well. And so if you think about it, the DevOps guy can actually not have to over-provision before he throws it over to the SREs. He can actually optimize and find the right size of the application before he sends it through to the SREs, and what this does is collapses the timeframe because it means the SREs don't have to hunt for a working set of parameters. They get one from the DevOps guys when they send it over, and this is how the future of AIOps is being really affected by optimization and what we call autonomous optimization which means that it's happening without humans having to press a button on it. >> John: Andrew, bring that slide back up. I want to just ask another question. Tuning in concert thing is very interesting to me. So how does that work? Are you telegraphing information to the developer from the autonomous workload tuning engine piece? I mean, how does the developer know the right knobs or where does it get that provisioning information? I see the performance lag. I see where you're solving that problem. >> Sure. >> How does that work? >> Yeah, so actually, if we go to the next slide, I'll show you exactly how it works. Okay, so this slide represents the architecture of a typical application environment that we would find ourselves in, and inside the dotted line is the customer's application namespace. That's where the app is. And so, it's got a bunch of pods. It's got a horizontal pod. It's got something for replication, probably an HPA. And so, what we do is we install inside that namespace two small instances. One is a tuning pod which some people call a canary, and that tuning pod joins the rest of the pods, but it's not part of the application. It's actually separate, but it gets the same traffic. We also install somebody we call Servo which is basically an action engine. What Servo does is Servo takes the metrics from whatever the metric system is is collecting all those different settings and whatnot from the working application. It could be something like Prometheus. It could be an Envoy Sidecar, or more likely, it's something like AppDynamics, or we can even collect metrics off of Nginx which is at the front of the service. We can plug into anywhere where those metrics are. We can pull the metrics forward. Once we see the metrics, we send them to our backend. The Opsani SaaS service is our machine learning backend. That's where all the magic happens, and what happens then is that service sees the settings, sends a recommendation to Servo, Servo sends it to the tuning pod, and we tune until we find optimal. And so, that iteration typically takes about 20 steps. It depends on how big the application is and whatnot, how fast those steps take. It could be anywhere from seconds to minutes to 10 to 20 minutes per step, but typically within about 20 steps, we can find optimal, and then we'll come back and we'll say, "Here's optimal, and do you want to "promote this to production," and the customer says, "Yes, I want to promote it to production "because I'm saving a lot of money or because I've gotten "better performance or better reliability." Then, all he has to do is press a button, and all that stuff gets sent right to the production pods, and all of those settings get put into production, and now he's now he's actually saving the money. So that's basically how it works. >> It's kind of like when I want to go to the beach, I look at the weather.com, I check the forecast, and I decide whether I want to go or not. You're getting the data, so you're getting a good look at the information, and then putting that into a policy standpoint. I get that, makes total sense. Can I ask you, if you don't mind, expanding on the performance and reliability and the cost advantage? You mentioned cost. How is that impacting? Give us an example of some performance impact, reliability, and cost impacts. >> Well, let's talk about what those things mean because like a lot of people might have different ideas about what they think those mean. So from a cost standpoint, we're talking about cloud spend ultimately, but it's represented by the settings themselves, so I'm not talking about what deal you cut with AWS or Azure or Google. I'm talking about whatever deal you cut, we're going to save you 30, 50, 70% off of that. So it doesn't really matter what cost you negotiated. What we're talking about is right-sizing the settings for CPU and memory, Replica. Could be Java. It could be garbage collection, time ratios, or heap sizes or things like that. Those are all the kinds of things that we can tune. The thing is most of those settings have an unlimited number of values, and this is why machine learning is important because, if you think about it, even if they only had eight settings or eight values per setting, now you're talking about literally billions of combinations. So to find optimal, you've got to have machine speed to be able to do it, and you have to iterate very, very quickly to make it happen. So that's basically the thing, and that's really one of the things that makes us different from anybody else, and if you put that last slide back up, the architecture slide, for just a second, there's a couple of key words at the bottom of it that I want to want to focus on, continuous. So continuous really means that we're on all the time. We're not plug us in one time, make a change, and then walk away. We're actually always measuring and adjusting, and the reason why this is important is in the modern DevOps world, your traffic level is going to change. You're going to push new code. Things are going to happen that are going to change the basic nature of the software, and you have to be able to tune for those changes. So continuous is very important. Second thing is autonomous. This is designed to take pressure off of the SREs. It's not designed to replace them, but to take the pressure off of them having to check pager all the time and run in and make adjustments, or try to divine or find an adjustment that might be very, very difficult for them to do so. So we're doing it for them, and that scale means that we can solve this for, let's say, one big monolithic application, or we can solve it for literally hundreds of applications and thousands of microservices that make up those applications and tune them all at the same time. So the same platform can be used for all of those. You originally asked about the parameters and the settings. Did I answer the question there? >> You totally did. I mean, the tuning in concert. You mentioned early as a key point. I mean, you're basically tuning the engine. It's not so much negotiating a purchase SaaS discount. It's essentially cost overruns by the engine, either over burning or heating or whatever you want to call it. I mean, basically inefficiency. You're tuning the core engine. >> Exactly so. So the cost thing is I mentioned is due to right-sizing the settings and the number of Replicas. The performance is typically measured via latency, and the reliability is typically measured via error rates. And there's some other measures as well. We have a whole list of them that are in the application itself, but those are the kinds of things that we look for as results. When we do our tuning, we look for reducing error rates, or we look for holding error rates at zero, for example, even if we improve the performance or we improve the cost. So we're looking for the best result, the best combination result, and then a customer can decide if they want to do so to actually over-correct on something. We have the whole concept of guard rail, so if performance is the most important thing, or maybe some customers, cost is the most important thing, they can actually say, "Well, give us the best cost, "and give us the best performance and the best reliability, "but at this cost," and we can then use that as a service-level objective and tune around it. >> Yeah, it reminds me back in the old days when you had filtering white lists of black lists of addresses that can go through, say, a firewall or a device. You have billions of combinations now with machine learning. It's essentially scaling the same concept to unbelievable. These guardrails are now in place, and that's super cool and I think really relevant call-out point, Patrick, to kind of highlight that. At this kind of scale, you need machine learning, you need the AI to essentially identify quickly the patterns or combinations that are actually happening so a human doesn't have to waste their time that can be filled by basically a bot at that point. >> So John, there's just one other thing I want to mention around this, and that is one of the things that makes us different from other companies that do optimization. Basically, every other company in the optimization space creates a static recommendation, basically their recommendation engines, and what you get out of that is, let's say it's a manifest of changes, and you hand that to the SREs, and they put it into effect. Well, the fact of the matter is is that the traffic could have changed then. It could have spiked up, or it could have dropped below normal. You could have introduced a new feature or some other code change, and at that point in time, you've already instituted these changes. They may be completely out of date. That's why the continuous nature of what we do is important and different. >> It's funny, even the language that we're using here: network, garbage collection. I mean, you're talking about tuning an engine, am operating system. You're talking about stuff that's moving up the stack to the application layer, hence this new kind of eliminating of these kind of siloed waterfall, as you pointed out in your second slide, is kind of one integrated kind of operating environment. So when you have that or think about the data coming in, and you have to think about the automation just like self-correcting, error-correcting, tuning, garbage collection. These are words that we've kind of kicking around, but at the end of the day, it's an operating system. >> Well in the old days of automobiles, which I remember cause I'm I'm an old guy, if you wanted to tune your engine, you would probably rebuild your carburetor and turn some dials to get the air-oxygen-gas mix right. You'd re-gap your spark plugs. You'd probably make sure your points were right. There'd be four or five key things that you would do. You couldn't do them at the same time unless you had a magic wand. So we're the magic wand that basically, or in modern world, we're sort of that thing you plug in that tunes everything at once within that engine which is all now electronically controlled. So that's the big differences as you think about what we used to do manually, and now, can be done with automation. It can be done much, much faster without humans having to get their fingernails greasy, let's say. >> And I think the dynamic versus static is an interesting point. I want to bring up the SRE which has become a role that's becoming very prominent in the DevOps kind of plus world that's happening. You're seeing this new revolution. The role of the SRE is not just to be there to hold down and do the manual configuration. They had a scale. They're a developer, too. So I think this notion of offloading the SRE from doing manual tasks is another big, important point. Can you just react to that and share more about why the SRE role is so important and why automating that away through when you guys have is important? >> The SRE role is becoming more and more important, just as you said, and the reason is because somebody has to get that application ready for production. The DevOps guys don't do it. That's not their job. Their job is to get the code finished and send it through, and the SREs then have to make sure that that code will work, so they have to find a set of settings that will actually work in production. Once they find that set of settings, the first one they find that works, they'll push it through. It's not optimized at that point in time because they don't have time to try to find optimal, and if you think about it, the difference between a machine learning backend and an army of SREs that work 24-by-seven, we're talking about being able to do the work of many, many SREs that never get tired, that never need to go play video games, to unstress or whatever. We're working all the time. We're always measuring, adjusting. A lot of the companies we talked to do a once-a-month adjustment on their software. So they put an application out, and then they send in their SREs once a month to try to tune the application, and maybe they're using some of these other tools, or maybe they're using just their smarts, but they'll do that once a month. Well, gosh, they've pushed code probably four times during the month, and they probably had a bunch of different spikes and drops in traffic and other things that have happened. So we just want to help them spend their time on making sure that the application is ready for production. Want to make sure that all the other parts of the application are where they should be, and let us worry about tuning CPU, memory, Replica, job instances, and things like that so that they can work on making sure that application gets out and that it can scale, which is really important for them, for their companies to make money is for the apps to scale. >> Well, that's a great insight, Patrick. You mentioned you have a lot of great customers, and certainly if you have your customer base are early adopters, pioneers, and grow big companies because they have DevOps. They know that they're seeing a DevOps engineer and an SRE. Some of the other enterprises that are transforming think the DevOps engineer is the SRE person 'cause they're having to get transformed. So you guys are at the high end and getting now the new enterprises as they come on board to cloud scale. You have a huge uptake in Kubernetes, starting to see the standardization of microservices. People are getting it, so I got to ask you can you give us some examples of your customers, how they're organized, some case studies, who uses you guys, and why they love you? >> Sure. Well, let's bring up the next slide. We've got some customer examples here, and your viewers, our viewers, can probably figure out who these guys are. I can't tell them, but if they go on our website, they can sort of put two and two together, but the first one there is a major financial application SaaS provider, and in this particular case, they were having problems that they couldn't diagnose within the stack. Ultimately, they had to apply automation to it, and what we were able to do for them was give them a huge jump in reliability which was actually the biggest problem that they were having. We gave them 5,000 hours back a month in terms of the application. They were they're having pager duty alerts going off all the time. We actually gave them better performance. We gave them a 10% performance boost, and we dropped their cloud spend for that application by 72%. So in fact, it was an 80-plus % price performance or cost performance improvement that we gave them, and essentially, we helped them tune the entire stack. This was a hybrid environment, so this included VMs as well as more modern architecture. Today, I would say the overwhelming majority of our customers have moved off of the VMs and are in a containerized environment, and even more to the point, Kubernetes which we find just a very, very high percentage of our customers have moved to. So most of the work we're doing today with new customers is around that, and if we look at the second and third examples here, those are examples of that. In the second example, that's a company that develops websites. It's one of the big ones out in the marketplace that, let's say, if you were starting a new business and you wanted a website, they would develop that website for you. So their internal infrastructure is all brand new stuff. It's all Kubernetes, and what we were able to do for them is they were actually getting decent performance. We held their performance at their SLO. We achieved a 100% error-free scenario for them at runtime, and we dropped their cost by 80%. So for them, they needed us to hold-serve, if you will, on performance and reliability and get their costs under control because everything in that, that's a cloud native company. Everything there is cloud cost. So the interesting thing is it took us nine steps because nine of our iterations to actually get to optimal. So it was very, very quick, and there was no integration required. In the first case, we actually had to do a custom integration for an underlying platform that was used for CICD, but with the- >> John: Because of the hybrid, right? >> Patrick: Sorry? >> John: Because it was hybrid, right? >> Patrick: Yes, because it was hybrid, exactly. But within the second one, we just plugged right in, and we were able to tune the Kubernetes environment just as I showed in that architecture slide, and then the third one is one of the leading application performance monitoring companies on the market. They have a bunch of their own internal applications and those use a lot of cloud spend. They're actually running Kubernetes on top of VMs, but we don't have to worry about the VM layer. We just worry about the Kubernetes layer for them, and what we did for them was we gave them a 48% performance improvement in terms of latency and throughput. We dropped their error rates by 90% which is pretty substantial to say the least, and we gave them a 50% cost delta from where they had been. So this is the perfect example of actually being able to deliver on all three things which you can't always do. It has to be, sort of all applications are not created equal. This was one where we were able to actually deliver on all three of the key objectives. We were able to set them up in about 25 minutes from the time we got started, no extra integration, and needless to say, it was a big, happy moment for the developers to be able to go back to their bosses and say, "Hey, we have better performance, "better reliability. "Oh, by the way, we saved you half." >> So depending on the stack situation, you got VMs and Kubernetes on the one side, cloud-native, all Kubernetes, that's dream scenario obviously. Not many people like that. All the new stuff's going cloud-native, so that's ideal, and then the mixed ones, Kubernetes, but no VMs, right? >> Yeah, exactly. So Kubernetes with no VMs, no problem. Kubernetes on top of VMs, no problem, but we don't manage the VMs. We don't manage the underlay at all, in fact. And the other thing is we don't have to go back to the slide, but I think everybody will remember the slide that had the architecture, and on one side was our cloud instance. The only data that's going between the application and our cloud instance are the settings, so there's never any data. There's never any customer data, nothing for PCI, nothing for HIPPA, nothing for GDPR or any of those things. So no personal data, no health data. Nothing is passing back and forth. Just the settings of the containers. >> Patrick, while I got you here 'cause you're such a great, insightful guest, thank you for coming on and showcasing your company. Kubernetes real quick. How prevalent is this mainstream trend is because you're seeing such great examples of performance improvements. SLAs being met, SLOs being met. How real is Kubernetes for the mainstream enterprise as they're starting to use containers to tip their legacy and get into the cloud-native and certainly hybrid and soon to be multi-cloud environment? >> Yeah, I would not say it's dominant yet. Of container environments, I would say it's dominant now, but for all environments, it's not. I think the larger legacy companies are still going through that digital transformation, and so what we do is we catch them at that transformation point, and we can help them develop because as we remember from the AIOps slide, we can plug in at that test level and help them sort of pre-optimize as they're coming through. So we can actually help them be more efficient as they're transforming. The other side of it is the cloud-native companies. So you've got the legacy companies, brick and mortar, who are desperately trying to move to digitization. Then, you've got the ones that are born in the cloud. Most of them aren't on VMs at all. Most of them are on containers right from the get-go, but you do have some in the middle who have started to make a transition, and what they've done is they've taken their native VM environment and they've put Kubernetes on top of it so that way, they don't have to scuttle everything underneath it. >> Great. >> So I would say it's mixed at this point. >> Great business model, helping customers today, and being a bridge to the future. Real quick, what licensing models, how to buy, promotions you have for Amazon Web Services customers? How do people get involved? How do you guys charge? >> The product is licensed as a service, and the typical service is an annual. We license it by application, so let's just say you have an application, and it has 10 microservices. That would be a standard application. We'd have an annual cost for optimizing that application over the course of the year. We have a large application pack, if you will, for let's say applications of 20 services, something like that, and then we also have a platform, what we call Opsani platform, and that is for environments where the customer might have hundreds of applications and-or thousands of services, and we can plug into their deployment platform, something like a harness or Spinnaker or Jenkins or something like that, or we can plug into their their cloud Kubernetes orchestrator, and then we can actually discover the apps and optimize them. So we've got environments for both single apps and for many, many apps, and with the same platform. And yes, thanks for reminding me. We do have a promotion for for our AWS viewers. If you reference this presentation, and you look at the URL there which is opsani.com/awsstartupshowcase, can't forget that, you will, number one, get a free trial of our software. If you optimize one of your own applications, we're going to give you an Oculus set of goggles, the augmented reality goggles. And we have one other promotion for your viewers and for our joint customers here, and that is if you buy an annual license, you're going to get actually 15 months. So that's what we're putting on the table. It's actually a pretty good deal. The Oculus isn't contingent. That's a promotion. It's contingent on you actually optimizing one of your own services. So it's not a synthetic app. It's got to be one of your own apps, but that's what we've got on the table here, and I think it's a pretty good deal, and I hope your guys take us up on it. >> All right, great. Get Oculus Rift for optimizing one of your apps and 15 months for the price of 12. Patrick, thank you for coming on and sharing the future of AIOps with you guys. Great product, bridge to the future, solving a lot of problems. A lot of use cases there. Congratulations on your success. Thanks for coming on. >> Thank you so much. This has been excellent, and I really appreciate it. >> Hey, thanks for sharing. I'm John Furrier, your host with theCUBE. Thanks for watching. (upbeat music)

(upbeat music) >> Hi, everyone. Welcome to today's session of theCUBE presentation of AWS Startup Showcase, the new breakthrough in DevOps, data analytics, cloud management tools, featuring OpsRamp for the cloud management migration track. I'm John Furrier, your hosts of theCUBE Today, we're joined by Jordan Sheer, vice president of corporate marketing and Michael Fisher, director of product management in OpsRamp. Gentlemen, thank you for joining us today for this topic of challenges of delivering availability for the modern enterprise. >> Thanks, John. >> Yeah, thanks for having us. >> Hey, so first of all, I have to congratulate you guys on the successful launch and growth of your company. You've been in the middle of the action of all this DevOps, microservices, cloud scale, and availability is the hottest topic right now. IT Ops, AI Ops, whoever you want to look at it, IT is automating a way in a lot of value. You guys are in the middle of it. Congratulations on that, and congratulations on being featured. Take a minute to explain what you guys do. What's the strategy? What's the vision? What's the platform. >> Yeah, I'll take that one. So I would just kind of take a step back and we look at the broader landscape of the ecosystem of tools that all sits in. There's a lot of promises and a lot of whats and features and functionality that are being announced. Three pillars of durability and all these tools are really trying to solve a fundamental problem we see in the market and this problem transcends the classic IT ops and it's really front and center, even in this modern DevOps market, this is the problem of availability. And so when we talk about availability, we don't just mean the four nines for an uptime metric, availability to the modern enterprise, is really about an application doing what it needs to do to serve the users in a way that works for the business. And I always like to have a classic example of an e-commerce site, right? So maybe you can get to an e-commerce sites online, but you can't add an item to a cart, right? Well, you can't do something that is a meaningful transaction for the business. And because of that, that experience is not available to you as a user and it's not available to the business because it didn't result in a positive outcome. So the promise of OpsRamp is really around this availability concept and the way we rationalize this as a three pillar formats. And so we think the three pillars of availability are the ability to observe data, this is the first piece of it all. And from a problem perspective, what we're really trying to say is do we have the right data at any given point in time to accurately diagnose, assess, and troubleshoot application behavior? And we see it as a huge problem with a lot of enterprises, because data that can be often siloed, too many tools, many teams, and each one has a slightly different understanding of application health. For example, the DevOps team may have a instance of Prometheus or they may have some other monitoring tool, or the IT team may have their own set, right? But when you have that kind of segmented view of the world, you're not really having the data in a central place to understand availability at the most holistic level, which is really from an end-user to that middleware, to the databases, to underlying microservices, which are really providing the end-user experience. So that observed problem is that first thing OpsRamp tries to solve. Secondly, this is the analyze phase, right? So analyze to us means are we giving the proper intelligence on top of the data to drive meaningful insights to this operator and user? And the promise here is that can we understand that baseline performance and potentially even mitigate future instance from happening? How often do we hear a cloud provider going down or some SaaS provider going down because of some microservice migration issue or some third party application or networking they're relying on? I can think of dozens on my head. So that's kind of the second piece. And then lastly is around this act. This is an area of a lot of investment for ops because we think this is the final pillar for nailing this availability problem. Because again, IT teams are not getting larger, they're getting smaller, right? Everyone's trying to do more with less. And so from a platform perspective, how do we enable teams to focus on the most business critical tasks, which are your cloud migrations, adopting microservices to run your modern applications, innovative projects. These are the things that IT and DevOps teams are tasked with. And maintaining availability is not something people want to do, that should be automated. And so when you think of automation, this is a big piece for us. So again, the key problem is how can we enable these IT or DevOps teams to focus on those business critical things, and automate it with the rest. And so this is the OpsRamp's three pillars of availability. >> John: Talk about the platform, if you don't mind. I know you've got a slide on this. I want to jump into it because this comes up a lot, availability's not just throughout uptime, because you know, uptime, five nine reliability is an old school concept. Now you have different kinds of services that might be up but slow, would cause some problems, as applications and this modern era have all these new sets of services. Can you go through and talked about the platform? >> Yeah, absolutely. So OpsRamp has a very... We address this availability problem pretty holistically, like I mentioned. From a platform perspective, there that two core lines that are comprising a product. One is this hybrid monitoring piece. This is that data layer. And the next one is event management, it's more of the we'll talk about that analysis. And so we treat the monitor as a direct feed into this event management. We're layering that on top, or layering machine learning and AI to augment the insights derived from that first pillar. And so this is where we see a really interesting intersection of data science and monitoring tools. We invest a lot in this area because there's a lot of meaningful problems to solve. In particular alert fatigue, or potentially root cause analysis, things that can take an operator or a developer a long time to do on their own, OpsRamp tries to augment that knowledge of your systems and applications so that you can get to the bottom of things faster and get on with your day. And so it's not just for the major outages, it's not just for the things that are on Twitter or CNN that's for daily things that can just distract you from the ability to do your job, which is to be a core innovator for a business. >> I will really say John, that we are already seeing some couple things here. Number one, we're already actually seeing fundamental transformations in the marketplace. Customers who have seen reduction in alert volumes of up to 95% in some cases, which is as you can imagine, that's completely transformational for these businesses. And number two, I think one of the promises of hybrid of observability working in tandem with event and incident management is the idea of finding unknown unknowns within your organization and being able to act upon them. All too many times nowadays, monitoring tools are there to just surface issues that you may know that you're looking for and then help you find it and then take action on them. But I think the idea of OpsRamp is that we really using that big data platform that Michael talks about is to really surface all the issues that you might not be able to see, identify the root cause, and then take action on those root causes. So in our world, application availability is a much more proactive activity where the IT operations team can actually be proactive about these incidents and then take action on them. >> Yes. Jordan, if you don't mind, I'm following up on that real quick. Talk about the difference uptime versus availability, because something could be up and reliable but not available and its services get flaky. Things may look like they're up and running. Can you just unpack that a little? >> So to me, I mean the really key aspect of availability that I think the old definition of uptime doesn't address is performance. That something can be up, but not performing, but still not really be available. And his e-commerce example, I think is a great one. Let's take, for example, you get on Amazon, right? The Amazon e-commerce experience is always available. And what that means is that at any given moment, when I want to click through the e-commerce experience, it performs. It's available. It's always there and I can buy it at any given time. If there's a latency issue, if the application has a lag, if it takes 30 seconds to really perform an activity on that application, in the alternative definition, that's not available anymore. Even though the application may be up, it's not performing, it's not providing a frictionless end customer experience, and it's not driving the business forward, and therefore it's not available. The definition of availability in OpsRamp is creating a meaningful customer experience that actually drives the business forward. So in that definition, if a service is up but it's latent, but it's not providing excellent customer experience that the business wants to promise to its end-user, it's not available. So that's really how we're redefining this whole notion of availability and we're urging our customers and people in the marketplace to do the same. Ask yourself the hard question, is your application available or is it just up? >> Yeah, and I think that the confluence of the business logic around what the outcome is, and I think this is the classic cliche, "Oh, it's all about outcomes." Here, you're saying that the outcome can be factored into the policy of the tech, meaning this is the experience we want for our users, our customers, and this is what we determined as acceptable and excellent. That's the new metric, so that's the new definition. You can almost flip the script. It feels like it's being flipped around. Is that the right way to think about it? >> Well, yeah, I think that's actually absolutely correct that an application needs to be business aware, especially in the modern day because all of the businesses that we work with, their applications are really the stock and trade of the business. And so if you create an application that is not business aware, that is just there for its own sake or is not performing according to the revenue goals or the targets of the business, then it's no longer available. >> I mean, it could be little things. It could be like an interface on the UI, it could be something really small or a microservice that's not getting to the database in time or some backup or some sort of high availability. Really interesting things could happen with microservices and DevOps, can you guys share some examples of what people might fall into from a trap standpoint or just from a bad architecture? What are some of the things that they might see in their environment that would say that they need help? >> Yeah, I can probably take that one. So there's a lot of, I call them symptoms of a bad availability experience. And I wouldn't even say it's a pure microservice specific thing. I would say it's really any application that's end-user phasing. I see similar pitfalls. One is a networking issue. I see the number one thing usually with these kinds of issues that networking or config changes that can cause environments to go down. And so when we talk to organizations get to the bottom of this is usually a config wasn't thought through thoroughly, or it was a QAed, they didn't have the proper controls in place. I would say that's probably the number one reasons I see applications go unavailable. I think that's some majority of DevOps teams that can empathize with that is someone did something and I didn't know, and it caused some applications servers go down and it causes cascading event of issues. That's like modern paradigm of issues. On old school days, it's a layer zero issue, someone unplugged something. Well, modern times it's someone pushed something I don't have an idea of what we're doing opposing a downstream effect it would have been and therefore my application went unavailable. So that's again, probably the number one pitfall. And again, I think the hardest problem in microservices still around networking, right? Enterprise level networking and connecting that with many data center applications. For example, Kubernetes, which is the provider or the opera orchestrator of any microservice is still getting to the level, many organizations are still getting a level of comfort with trusting production applications to run on it because one is a skill gap. There's not many large organizations have a huge Kubernetes application team, usually they're fairly small agile units. And so with that, there's a skill gaps, right? How do you network in Kubernetes? How do you persist in storage? How to make sure that your application has the proper security built into it, right? Because that these are all legacy problems kind of catching up with the modern environments, because just because you're modernizing, it doesn't mean these old problems go away. It just take a different form. >> Yeah. That's a great point. Modernization. You guys, can you guys talk about this modern application movement in context to how DevOps has risen really into providing value there? Certainly with cloud scale and how companies are dealing with the old legacy model of centralized IT or security teams who slow things down? Because one of the things that we're seeing in this market is speed, faster developer time to market, time to value. Especially if you're an e-commerce site, you're seeing potentially real-time impact. So you have the speed game on the application side that's actually good, being slowed down by lack of automation or just slow response to a policy or a change or an incident. I mean, this seems to be a big discussion. Can you guys share your thoughts on this and your reaction to that? >> I can tell you that one of the places that we are displacing, one of the markets that we are displacing is the legacy ITOM market, because it can't provide the speed that you're talking about, John. I think about a couple of specific examples. I won't necessarily name the providers, but there are several legacy item providers that for example, require an appliance. They require an appliance for you to administer IT operations management services. And that in and of itself is a much slower way of deploying item. Number two, they require this customized proof of value, proof of concept operation, where companies, enterprise organizations need to orchestrate the customization of the item platform for their use. You buy separate management packs that would integrate with different existing applications on your stack. To us, that's too slow. It means you have to make a bunch of decisions upfront about your item practice and then live with those decisions for years to come, especially with software licenses. So by even moving that entire operation to SaaS, which is what the OpsRamp platform has done, has accelerated the ability to drive availability for applications. Number two, and I'd like to pitch this over to Michael, because I think this is really fundamental to how OpsRamp is driving availability, is the use of artificial intelligence. So when we think about being proactive and we think about moving more quickly, it takes machine learning to do a lot of that work to be able to monitor alert streams and alert floods, especially with the smaller scale down IT teams that Michael has mentioned before. You need to harness the power of artificial intelligence to do some of that work. So those are two key ways that I see the platform driving additional speed, especially in a DevOps environment. And I'd love to hear as well from Michael, additional enhancements. >> Michael, if you don't mind, I'll add one thing. First of all, great call out there, Jordan. Yeah. So the legacy slow down, it's like say appliance or whatever that also impacts potentially the headroom on automation. So if you could also talk about the AI machine learning, AI piece, as well as how that impacts automation, because the end of the day automation is going to have to be lock step in with the AI. >> Yeah. And this kind of goes back to that OpsRamp three pillars of availability, right? So that's the what we do, but again, it's all goes back to the availability problem. But we see that observe, analyze, and act as a seamless flow, right? To have it under the same group or the same tent provides tremendous opportunity and value for our DevOps or IT Ops teams that trust the OpsRamp platform because I'm a big believer that garbage in, garbage out. Having the monitoring data in native or having this data native to your tool provides a lot of meaningful value for customers because they have their monitoring data, which is coming from the OpsRamp tool. They have the intelligence, which is being provided by their ops cube machine learning. And they have our process automation and workflow to feed off that directly. And so when I think of this modernization problem, I really think about modern DevOps teams and the problems they face, which is around doing more with less, that's kind of the paradigm of many teams, each one is trying to learn, how do I do security for Kubernetes? How do I observe my security in the Kubernetes' cluster? How do I make sure my CI/CD pipeline is set up in such a way that I don't need to monitor it, or I don't need to give it attention? And so having a really seamless flow from that observe, analyze, act enables those problems to be solved in a much more seamless way that I don't see many legacy providers be able to keep up with. >> Awesome. Jordan, if you don't mind, I'd love to get your definition of what modern availability means. >> Yeah. So, you know, as I've gone through a little bit previously, so modern availability to me is availability uptime. It's also performance, right? Is the app location marks set down by both the application team, but also by the business. And number three is it business aware. So a truly modern available application is being able, is driving an excellent customer experience according to the product roadmap, but it's also doing it in a way that moves the business forward. Right? And if your applications today are not meeting those benchmarks, if they're performing but they're not driving the business forward, if they're not performing, if they're not up, if they don't meet any one of those three core tenants, they're not truly available. And I think that what's most impactful to me about what the platform, what OpsRamp in particular does in today's environment is operating under that modern definition of available is more difficult than ever. It is more difficult because we are living in a hybrid, distributed, multi-cloud world with tons of software vendors that are being sold into these organizations today that are promising similar results. So when you're an IT operator, how do you drive availability in light of that kind of environment? You have reduced budget. You have greater complexity, you have more tools than ever, and yet your software is more impactful to the bottom line than ever before. It's in this environment that we took a hard look at what's going on in the world, and we say these operators need help driving availability. That's the germination of the OpsRamp platform. >> That's a great point. We're going to come into the culture. And the second Emily Freeman's keynote about the revolution in DevOps talks about this, multiple personas and multiple tools that drive specialism, specialties that actually don't help in the modern era. So I'm going to hold that for a second. We'll come to the cultural question in a minute. Michael, if you don't mind to pivot off that definition, what are the metrics? With all those tools out there, all these new things, what are the new metrics for modern availability? It's more than MTTR. >> Yeah. This whole metrics that I think people spend a lot of time on, I think it's actually people thinking in the wrong direction if you ask me. So I've seen a lot of work. People say that the red metrics, that rate error duration or its views, utilization, saturation errors, or it's these other more contrived application metrics. I think they're looking at a piece of the stack, they're not looking at the right things. Even things like mean time to resolve and critical and server response time, mean time to tech, those are all downstream indicators. I like to look at much more proactive signals. So things like app deck score, your application index, or application performance index, these are things that are much more end-user facing or even things like NPS score, right? This has never really been a classic metric for these operations teams, but what a NPS score shows you is are your users happy using your applications? Is your experience giving what they expect it to be? And usually when you ask these two questions, even if you ask the DevOps team do you know what your Atlas score is? And you use NPS score, but what are those, right? Because it's just never been in that conversation. Those have been more maybe on the business side or maybe on the product management side. But I think that as organizations modernize, we see a much more homogenous group forming among these DevOps and product units to answer these kinds of questions. That's something we focus a lot on OpsRamp it's not seeing the silo of DevOps product or Ops. We're each thinking of how do you have a better NPS and how do we drive a better app decks? Because those are our leading indicators of whether or not our applications available. >> So I want to ask you guys both before, again, back to the own cultural question I really want to get into, but from a customer standpoint, they're being bombarded with sales folks, "Hey, buy my tool. I got some monitoring over a year. I got AI ops. I got observability." I mean, there's a zillion venture back companies that just do observability, just monitoring, just AI Ops. As the modern error is here, what's going on in the psychology of the customer because they want to like clear the noise. We saw it in cybersecurity years ago. Right? They buy everything, and next thing you know, they're going to fog of tools. What's the current state of the customer? What do they need right now as to be positioned for the automation, for the edge, all these cool cloud-scale next gen opportunities? >> Yeah. So in my mind, it's basically three things, right? Customers, number one, they want a vision. They want a vision that understands their position in the enterprise organization and what the vision for application development is going to be moving forward. Number two, they don't want to be sold anymore. You're absolutely right. It's harder and harder to make a traditional enterprise sale nowadays. It's because there's a million vendors. They're just like us. They're trying to get people on the phone and it can be tough out there. And number three, they want to be able to validate on their own with their own time. So in light of that, we've introduced a free trial of our cloud monitoring. It's a lightweight version of the OpsRamp platform, but it is a hundred percent free right now. It is available for two weeks with an unlimited number of users and resource count. And you come in and you can get started on your own using preloaded infrastructure from us if you want, or you could bring your own infrastructure. And we can tell you that customers who onboard through the free trial can see insights on their infrastructure within 20 minutes of onboarding. And that experience in and of itself is a differentiator and it allows our customers to buy on their own terms and timelines. >> Sure. And that's a great point. We brought this up last quarter in the showcase, one of the VCs brought up and says he was an old school VC, kind of still in the game, but he was saying in the old days in shelf where you didn't know if it was going to be successful until like downstream, now it's SaaS. If a customer doesn't see the value immediately. It's there. I mean, there's no hiding. You cannot hide from the truth of value here in the modern era. That's a huge impact on how customers now are evaluating and making decisions. >> Absolutely. And you know, I don't think any customer out there wants to read it on the white paper on the state of enterprise IT anymore. We recognize that and so we are hyper-focused on driving value for our customers and prospects as fast as possible, and still providing them the control that they need to make decisions on their own terms. >> Michael, I've got to ask you, since you have the keys to the kingdom on the product management side, what's the priorities on your side for customers, obviously the pressure's there, you guys are doing great, customers try it out for free. They can get, see the value and then double down on it. That's the cloud way. That's what's DevOps all about. You have to prioritize the key things, what's going on with your world. >> Yeah. And I would say of course prod has their own perspective on this. Our number one goal right now is to accelerate that time to value. And so when we look at one who we're targeting, right? So there's DevOps user, this modern application of operator, what are their core concerns in the world? One is, again, that data problem. Are we bringing the right type of data to solve meaningful problems? And two, are we making insights out of that? So from my priority's perspective, we're really driving more focus on this time to value problem and reduced time to there's some key value metrics we have and I'll go to that, but it's all an effort to make sure that when they hit our platform and they use our platform, we're showing them their return on investment as fast as possible. And so, what a return on investment means (indistinct) can slightly vary, but we try to narrow focus on our key target persona and market and focused on them. So right now it definitely is on that modern DevOps team enterprise, looking to provide modern application availability. >> Awesome. Hey guys, for the last two minutes, I'd love to shift now to the culture. So Jordan, you mentioned that appliance, the item example, which is I think indicative of many scenarios in the legacy old world, old guard school, where there's a cultural shift where some people are pissed off, they're going to go and they slowing things down, right? So you see people that are unhappy, the sites having performance of an e-commerce sites, having five second delays or some impact to the business, and the developers are moving fast with DevOps. The DevOps has risen up now where it's driving the agenda. Kind of impacting the old school departments, whether it's security or IT, central groups that are responding in days and weeks to requests, not minutes. This is a huge cultural thing. What's your thoughts on this? >> I absolutely think it's true. I think the reason were options differ slightly on that is we do see the rise of DevOps culture and how it starts to take control and rest the customer experience back from the legacy providers within the organization, but we still see that there's value in having a foot in the old and a foot in the new, and it's why that term hybrid, we talked about hybrid observability is really important to us. It's true, DevOps culture has a lot of great reasons why it's taken over, right? Increases in speed, increases in quality, increases in innovation, all of that. And yet the enterprise is still heavily invested in the old way. And so what they are looking for is a platform to get them from the old way to the new way fast. And that's where we really shine. We say we can enable, we can work with the existing tool set that you have, and we can move you even more in the future of this new definition of availability. And we can get you that DevOps state of play even quicker. And so you don't have to make a heavy lift and you don't have to take a big gamble right now. You can still provide this kind of slow moving migration plan that you need to feel comfortable, and it doesn't force you to throw away a bunch of stuff. >> And if you guys can comment on whole day two operations, that's where the whole ops reliability thing comes in, right? This is kind of where we're at right now, Dev and Ops. Ops really driving the quality and reliability, availability and your definition. This is key, right? This is where we're starting to see the materialization of DevOps. >> It's why we have guys like Michael Fisher who are really driving our agenda forward, right? Because I think he represents the vision of the future that we all want to get to. And the platform that the product team in OpsRamp is building is there, right? But we also want to provide a path for day two, right? There are still some companies are living in day one and they want to get to day two. And so that's where we drive out here. >> And Michael, the platform with the things like containers really helps people get there. They don't have to kill the old to bring in the new, they can coexist. Can you quickly comment your reaction to that? >> Yeah, absolutely. And I talked to a lot of, I won't name any but large scale web companies, and they're actually balancing this today. They have some infrastructure or applications running on bare metal that somebody's got Kubernetes, and there's actually, it's not so much, everything has to go one direction. It actually is what makes the business, right? Even for migrating to the cloud, there has to be a compelling business reason to do so. And I think a lot of companies are realizing that for the application side as well. What runs where and how do we run it? Do we migrate a legacy monolith to a microservice? How fast do we do it? What's the business impact of doing it? These are all critical things that DevOps teams are engaged with on a daily basis as part of the core workflows, so that's my take on that. >> Guys. Great segment. Thanks for coming on and sharing that insight. Congratulates the OpsRamp, doing really extremely well, right in the right position on ramp for operations to be DevOps, whatever you want to call it, you guys are in the center of it with a platform. I think that's what people want, delivering on these availability, automation, AI. Congratulations and thanks for coming on theCUBE for the Showcase Summit. >> Thanks so much. >> Thank you so much, John. >> Okay, theCUBE's coverage of AWS showcase hottest startups in cloud. I'm John Furrier, your host. Thanks for watching. (relaxing music)

(upbeat music) >> Hello and welcome to today's session, theCUBE's presentation of the AWS Startup Showcase, New Breakthroughs in DevOps, Data Analytics, Cloud Management Tools, featuring Ascend.io for the data and analytics track. I'm your host, John Furrier with theCUBE. Today, we're proud joined by Sean Knapp, CEO and founder of Ascend.io and Jason Robinson who's the VP of Data Science and Engineering at Steady. Guys, thanks for coming on and congratulations, Sean, for the continued success, loves our cube conversation and Jason, nice to meet you. >> Great to meet you. >> Thanks for having us. >> So, the session today is really kind of looking at automating analytics workloads, right? So, and Steady as a customer. Sean, talk about the relationship with the customer Steady. What's the main product, what's the core relationship? >> Yeah, it's a really great question. when we work with a lot of companies like Steady we're working hand in hand with their data engineering teams, to help them onboard onto the Ascend platform, build these really powerful data pipelines, fueling their analytics and other workloads, and really helping to ensure that they can be successful at getting more leverage and building faster than ever before. So we tend to partner really closely with each other's teams and really think of them even as extensions of each other's own teams. I watch in slack oftentimes and our teams just go back and forth. And it's like, as if we were all just part of the same company. >> It's a really exciting time, Jason, great to have you on as a person cutting your teeth into this kind of what I call next gen data as intellectual property. Sean and I chat on theCUBE conversation previous to this event where every company is a data company, right? And we've heard that cliche. >> Right. >> But it's true, right? It's going to, it's getting more powerful with the edge. You seeing more diverse data, faster data, small, big, large, medium, all kinds of different aspects and patterns. And it's becoming a workflow kind of intellectual property paradigm for companies, not so much. >> That's right. >> Just the tech it's the database is you can, it's the data itself, data in flight, it's moving around, it's got value. What's your take-- >> Absolutely. >> On this trend? >> Basically, Steady helps our members and we have a community of members earn more income. So we want to help them steady their financial lives. And that's all based on data, so we have a web app, you could go to the iOS Store, you could go to the Google Play Store, you can download the app. And we have a large number of members, 3 million plus, who are actively using this. And we also have a very exciting new product called income passport. And this helps 1099 and mixed wage earners verify their income, which is very important for different government benefits. And then third, we help people with emergency cash grants as well as awards. So all of that is built on a bedrock of data, so if you're using our apps, it's all data powered. So what you were mentioning earlier from pipelines that are running it real time to yeah, anything, that's a kind of a small data aggregation, we do everything from small to real-time and large. >> You guys are like a multiple sided marketplace here, you've got it, you're a FinTech app, as well as the future of work and with virtual space-- >> That's right. >> Happening now, this is becoming, actually encapsulates kind of the critical problems that people trying to solve right now, you've got multiple stakeholders. >> That's right. >> In the data. >> Yes, we absolutely do. So we have our members, but we also, within the company, we have product, we have strategy, we have a growth team, we have operations. So data engineering and data science also work with a data analytics organization. So at Steady we're very much a data company. And we have a data organization led by our chief data officer and we have data engineering and data science, which are my teams, but also that business insights and analytics. So a lot of what we're building on the data engineering side is powering those insights and analytics that the business stakeholders use every day to run the organization. >> Sean, I want to get your thoughts on this because we heard from Emily Freeman in the keynote about how this revolution in DevOps or for premiering her talk around how, it's not just one persona anymore, I'm a release engineer, I'm this kind of engineer, you're seeing now all engineering, all developers are developers. You have some specialty, but for the most part, the team makeups are changing. We touched on this in our cube conversation. The journey of data is not just the data people, the data folks. It's like there's, they're developers too. So the confluence of data science, data management, developing, is changing the team and cultural makeup of companies. Could you share your thoughts on this dynamic and how it impacts customers? >> Absolutely, I think the, we're finding a similar trend to what we saw a number of years ago, when we talked about how software was eating the world and every company was now becoming a software company. And as a result, we saw this proliferation and expansion of what the software roles look like and thought of a company pulled through this entire new era of DevOps. We were finding that same pattern now emerging around data as not only is every company a software company, every company is a data company and data really is that field, that oil that fuels the business and in doing so, we're finding that as Jason describes it's pervasive across the team, it is no longer just one team that is creating some insights and reports around operational analytics, or maybe a team over here doing data science or machine learning. It is expensive. And I think the really interesting challenges that start to come with this too, are so many data teams are so over capacity. We did a recent study that highlighted that 96% of data teams are at, or over capacity, only 4% had spare capacity. But as a result, the net is being cast even wider to pull in people from even broader and more adjacent domains to all participate in the data future of their organization. >> Yeah, and I think I'd love to get your guys react to this conversation with Andy Jassy, who's now the CEO of Amazon, but when he was the CEO of AWS last year, I talked with him about how the old guard and new guard are thinking around team formations. Obviously team capacity is growing and challenged when you've got the right formula. So that's one thing, right? But what if you don't have the right formula? If you're in the skills gap, problem, or team formation side of it, where you maybe there was two years ago where the mandate came down? Well, we got to build a data team even in two years, if you're not inquisitive. And this is what Andy and I were talking about is the thinking and the mindset of that mission and being open to discovering and understanding the changes, because if you were deciding what your team was two, three years ago, that might have changed a lot. So team capacity, Sean, to your point, if you got it right, and that's a challenge in and of itself, but what if you don't have it, right? What do you guys think about this? >> Yeah, I think that's exactly right. Basically trying to see, look and gaze into the crystal ball and see what's going to happen in a year or two years, even six months is quite difficult. And if you don't have it right, you do spend a lot of time because of the technical debt that you've amassed. And we certainly spend quite a bit of time with technical debt for things we wanted to build. So, deconvolving that, getting those ETLs to a runnable state, getting performance there, that's what we spend a bit of time on. And yeah, it's something that it's really part of the package. >> What do you guys see as the big challenge on teams? The scaling challenge okay. Formation is one thing, Sean, but like, okay, getting it right, getting it formed properly and then scaling it, what are the big things you're seeing? >> One of the, I think the overarching management themes in general, it is the highest out by the highest performing teams are those where the individual with the context and the idea is able to execute as far and as fast and as efficiently as possible, and removing a lot of those encumbrances and put it a slightly different way. If DevOps was all basically boiled down to, how do we help more people write more software faster and safely data ops would be very similarly, how do we enable more people to do more things with data faster and safely? And to do that, I think the era of these massive multi-year efforts around data are gone and hopefully in the not too distant future, even these multi-quarter efforts around data are gone and we get into a much more agile, nimble methodology where smaller initiatives and smaller efforts are possible by more diverse skillsets across the business. And really what we should be doing is leveraging technology and automation to ensure that people are able to be productive and efficient and that we can trust our data and that systems are automated. And these are problems that technology is good at. And so in many ways, how in the early days Amazon would described as getting people out of the muck of DevOps. I think we're going to do the same thing around getting people out of the muck of the data and get them really focused on the higher level aspects. >> Yeah, we're going to get into that complexity, heavy lifting side muck, and then the heavy lifting taking away from the customers. But I want to go back to real quick with Jason while we're on this topic. Jason, I was just curious, how much has your team grown in the recent year and how much could've, should've grown, what's the status and how has Ascend helped you guys? What's the dynamic there? ' Cause that's their value proposition. So, take us through that. >> Absolutely, so, since the beginning of the year data engineering has doubled. So, we're a lean team, we certainly use the agile mindset and methodologies, but we have gone from, yeah, we've essentially doubled. So a lot of that is there's just so much to do and the capacity problem is certainly there. So we also spend a lot of time figuring out exactly what the right tooling is. And I was mentioning the technical debt. So you have those, there's the big O notation of whatever that involves technical debt. And when you're building new things, you're fixing old things. And then you're trying to maintain everything. That scaling starts to hit hard. So even if we continue to double, I mean, we could easily add more data engineers. And a lot of that is, I mean, you know about the hiring cycles, like, a lot of of great talent, but it's difficult to make all of those hires. So, we do spend quite a bit of time thinking about exactly what tools data engineering is using day-to-day. And what I mentioned, were technologies on the streaming side all the way to like the small batch things, but, like something that starts as a small batch getting grow and grow and grow and take, say 15 hours, it's possible, I've seen it. But, and getting that back down and managing that complexity while not overburdening people who probably don't want to spend all their waking hours building ETLs, maintaining ETL, putting in monitoring, putting in alerting, that I think is quite a challenge. >> It's so funny because you mentioned 18 hours, you got to kind of being, you didn't roll your eyes, but you almost did, but this is, but people want it yesterday, they want real time, so there's a lot of demand-- >> Yes. >> On the minds of the business outcome side of it. So, I got to ask you, because this comes up a lot with technical debt, and now we're starting to see that come into the data conversation. And so I always curious, is there a different kind of technical debt with data? Because again, data is like software, but it's a little bit of more elusive in the sense it's always changing. So is there, what kind of technical debt do you see in the data side that's different than say software side? >> Absolutely, now that's a great question. So a lot of thinking about your data and structuring your data and how you want to use that data going into a particular project might be different from what happens after stakeholders have a new considerations and new products and new items that need to be built. So thinking about how that, let's say you have a document store, or you have something that you thought was going to be nice and structured, how that can evolve and support those particular products can essentially, unless you take the time and go through and say, well, let's architect it perfectly so that we can handle that. You're going to make trade-offs and choices, and essentially that debt builds up. So you start cutting corners, you start changing your normalization. You start essentially taking those implicit schema that then tend to build into big things, big implicit schema. And then of course, with implicit schema, you're going to have a lot of null values, you're going to have a lot of items to deal with. So, how do you deal with that? And then you also have the opportunity to create keys and values and oops, do we take out those keys that were slightly misspelled? So, I could go on for hours, but basically the technical debt certainly is there with on data. I see a lot of this as just a spectrum of technical debt, because it's all trade-offs that you made to build a product, and the efficiency has start to hit you. So, the 15 hour ETL, I was mentioning, basically you start with something and you were building things for stakeholders and essentially you have so much complex logic within that. So for the transforms that you're doing from if you're thinking of the bronze, silver, gold, kind of a framework, going from that bronze to a silver, you may have a massive number of transformations or just a few, just to lightly dust it. But you could also go to gold with many more transformations and managing that, managing the complexity, managing what you're spending for servers day after day after day. That's another real challenge of that technical debt stuff. >> That's a great lead into my next question, for Sean, this is the disparate system complexity, technical debt and software was always kind of the belief was, oh yeah, I'll take some technical debt on and work it off once I get visibility and say, unit economics or some sort of platform or tool feature, and then you work it off as fast as possible. I was, this becomes the art and science of technical debt. Jason, what you're saying is that this can be unwieldy pretty quickly. You got state and you got a lot of different inter moving parts. This is a huge issue, Sean, this is where it's, technical debt in the data world is much different architecturally. If you don't get it right, this is a huge, huge issue. Could you aluminate why that is and what you guys are doing to help unify and change some of those conditions? >> Yeah, absolutely. When we think about technical debt and I'll keep drawing some parallels between DevOps and data ops, 'cause I think there's a tremendous number of similarities in these worlds. We used to always have the saying that "Your tech debt grows manually across microservices, "but exponentially within services." And so you want that right level of architecture and composibility if you will, of your systems where you can deploy changes, you can test, you can have high degrees of competence and the roll-outs. And I think the interesting part in the data side, as Jason highlighted, the big O-notation for tech debt in the data ecosystem, is still fairly exponential or polynomial in nature. As right now, we don't have great decomposition of the components. We have different systems. We have a streaming system, we have a databases, we have documents, doors and so on, but how the whole data pipeline data engineering part works generally tends to be pretty monolithic in nature. You take your whole data pipeline and you deploy the whole thing and you basically just cross your fingers, and hopefully it's not 15 hours, but if it is 15 hours, you go to sleep, you wake up the next morning, grab a coffee and then maybe it worked. And that iteration cycle is really slow. And so when we think about how we can improve these things, right? This is combinations of intelligent systems that do instantaneous schema detection, and validation, excuse me, it's combinations of things that do instantaneous schema detection and validation. It's things like automated lineage and dependency tracking. So you know that when you deploy code, what piece of data it affects, it's things like automated testing on individual core parts of your data pipelines to validate that you're getting the expected output that you need. So it's pulling a lot of these same DevOps style principles into the data world, which is really designed to going back to how do you help more people build more things faster and safely really rapid iterations for rapid feedback. So you know if there's breaks in the system much earlier on. >> Well, I think Sean, you're onto something really big there. And I think this is something that's emerging pretty quickly in the cloud scale that I called, 2.0, whatever, what version we're in, is the systems thinking mindset. 'Cause you mentioned the model that that was essentially a silo or subsystem. It was cohesive in it's own way, but now it's been monolithic. Now you have a broken down set of decomposed sets of data pieces that have to work together. So Jason, this is the big challenge that everyone, not really people are talking about, I think most these guys are, and you're using them. What are you unifying? Because this is a systems operating systems thinking, this is not like a database problem. It's a systems problem applied to data where databases are just pieces of it, what's your thoughts? >> That's absolutely right. And I would, so Sean touched on composibility of ETL and thinking about reusable components, thinking about pieces that all fit together, because as you're building something as complex as some of these ETS are, we do think about the platform itself and how that lends to the overarching output. So one thing, being able to actually see the different components of an ETL and blend those in and you as the dry principal, don't repeat yourself. So you essentially are able to take pieces that one person built, maybe John builds a couple of our connectors coming in, Sean also has a bunch of transforms and I just want this stuff out, so I can use a lot of what you guys have already built. I think that's key, because a lot of engineering and data engineering is about managing complexity. So taking that complexity and essentially getting it out fast and getting out error free, is where we're going with all of the data products we're building. >> What are some of the complexity that you guys have that you're dealing with? Can you be specific and share what these guys are doing to solve that problem for you? That's, this is a big problem everyone's having, I'm seeing that all over the place. >> Absolutely, so I could start at a couple of places. So I don't know if you guys are on the three Vs, four Vs or five Vs, but we have all of those. And if you go to that five, four or five V model, there is the veracity piece, which you have to ask yourself, is it true? Is it accurate when? So change happens throughout the pipeline, change can come from web hooks, change can come from users. You have to make sure that you're managing that complexity and what we we're building, I mentioned that we are paying down a lot of tech debt, but we're also building new products. And one pretty challenging, quite challenging ETL that we're building is something going from a document store to an analytical application. So in that document store, we talked about flexible schema. Basically, you don't really know exactly what you're going to get day to day, and you need to be able to manage that change through the whole process in a way that the ultimate business users find value. So, that's one of the key applications that we're using right now. And that's one that the team at Ascend and my team are working hand in hand going through a lot of those challenges. And it's, I also watch the slack just as Sean does, and it's a very active discussion board. So it is essentially like they're just partnering together. It's fabulous, but yeah-- >> And you're seeing kind of a value on this too, I mean, in terms of output what's the business results? >> Yes, absolutely. So essentially this is all, so yes, the fifth V value. So, getting to that value is essentially, there were a few pieces of the, to the value. So there's some data products that we're building within that product and their data science, data analytics based products that essentially do things with the data that help the user. There's also the question of exactly the usage and those kinds of metrics that people in ops want to understand as well as our growth team. So we have internal and external stakeholders for that. >> Jason, this is a great use case, a great customer, Sean, you guys are automating. For the folks watching, who were seeing their peer living the dream here and the data journey, as we say, things are happening. What's the message to customers that you guys want to send because you guys are really cutting your teeth into a whole another level of data engineering, data platform. That's really about the systems view and about cloud. What's the pitch, Sean? What should people know about the company? >> Absolutely, yeah, well, so one, I'd say even before the pitch, I would encourage people to not accept the status quo. And in particular, in data engineering today, the status quo is an incredibly high degree of pain and discomfort. And I think the important part of why Ascend exists and why we're so helpful for our customers, there is a much more automated future of how we build data products, how we optimize those and how we can get a larger cohort of builders into the data ecosystem. And that helps us get out of the muck as we talked about before and put really advanced technology to work for more people inside of our companies to build these data products, leveraging the latest and greatest technologies to drive increased business value faster. >> Jason, what's your assessment of these guys, as people are watching might say, hey, you know what, I'm going to contact them, I need this. How would you talk about Ascend into your peers? >> Absolutely, so I think just thinking about the whole process has been a great partnership. We started with a POC, I think Ascend likes to start with three use cases, I think we came out with four and we went through the ones that we really cared about and really wanted to bring value to the company with. So we have roadmaps for some, as we're paying down technical debt and transitioning, others we can go directly to. And I think that thinking about just like you're saying, John, that systems view of everything you're building, where that makes sense, you can actually take a lot of that complexity and encapsulate it in a way that you can essentially manage it all in that platform. So the Ascend platform has the composibility piece that we touched on. It also, not only can you compose it, but you can drill into it. And my team is super talented and is going to drill into it. So basically loves to open up each of those data flows each of the components therein and has the control there with the combination of Spark Sequel, PI Spark SQL Scala and so on. And I think that the variety of connections is also quite helpful. So thinking about the dry principle from a systems perspective is extremely useful because it's dry, you often get that in a code review, right? I think you can be a little bit more dry here. >> Yeah. >> But you can really do that in the way that you're composing your systems as well. >> That's a great, great point. One quick thing for the folks that they're watching that are trying to figure this out, and a lot of architecture is going on. A lot of people are looking at different solutions. What things have you learned that you could give them a tip like to avoid like maybe some scar tissue or tips of the trade, where you can say, hey, this way, be careful, what's some of the learnings? Could you give a few pointers to folks out there, if they're kicking tires on the direction, what's the wrong direction? What's the right direction look like? >> Absolutely, I think that, I think it through, and I don't know how much time we have that, that feels like a few days conversation as far as ways to go wrong. But absolutely, I think that thinking through exactly where want to be is the key. Otherwise it's kind of like when you're writing a ticket on Jarrah, if you don't have clear success criteria, if you don't know where you going to go, then you'll end up somewhere building something and it might work. But if you think through your exact destination that you want to be at, that will drive a lot of the decisions as you think backwards to where you started. And also I think that, so Sean also mentioned challenging the status quo. I think that you really have to be ready to challenge the status quo at every step of that journey. So if you start with some particular service that you had and its legacy, if it's not essentially performing what you need, then it's okay to just take a step back and say, well, maybe that's not the one. So I think that thinking through the system, just like you were saying, John, and also I think that having a visual representation of where you want to go is critical. So hopefully that encapsulates a lot of it, but yes, the destination is key. >> Yeah, and having an engineering platform that also unifies the multiple components and it's agile. >> That's right. >> It gets you out of the muck and on the last day and differentiate heavy lifting is a cloud plan. >> Absolutely. >> Sean, wrap it up for us here. What's the bumper sticker for your vision, share your founding principles of the company. >> Absolutely, for us, we started the company as a former in recovery and CTO. The last company I founded, we had nearly 60 people on our data team alone and had invested tremendous amounts of effort over the course of eight years. And one of the things that I've learned is that over time innovation comes just as much from deciding what you're no longer going to do as what you're going to do. And focusing heavily around, how do you get out of that muck? How do you continue to climb up that technology stack? Is incredibly important. And so really we are excited to be a part of it and taking the industry is continuing to climb higher and higher level. We're building more and more advanced levels of automation and what we call our data awareness into the automated engine of the Ascend platform that takes us across the entire data ecosystem, connecting and automating all data movement. And so we have a very exciting vision for this fabric that's emerging over time. >> Awesome, Sean, thank you so much for that insight, Jason, thanks for coming on customer of Ascend.io. >> Thank you. >> I appreciate it, gentlemen, thank you. This is the track on automating analytic workloads. We here at the end of us showcase, startup showcase, the hottest companies here at Ascend.io, I'm John Furrier, with theCUBE, thanks for watching. (upbeat music)