>>Good morning fellow nerds and welcome back to AWS Reinvent. We are live from the show floor here in Las Vegas, Nevada. My name is Savannah Peterson, joined with my fabulous co-host John Furrier. Day two keynotes are rolling. >>Yeah. What do you thinking this? This is the day where everything comes, so the core gets popped off the bottle, all the announcements start flowing out tomorrow. You hear machine learning from swee lot more in depth around AI probably. And then developers with Verner Vos, the CTO who wrote the seminal paper in in early two thousands around web service that becames. So again, just another great year of next level cloud. Big discussion of data in the keynote bulk of the time was talking about data and business intelligence, business transformation easier. Is that what people want? They want the easy button and we're gonna talk a lot about that in this segment. I'm really looking forward to this interview. >>Easy button. We all want the >>Easy, we want the easy button. >>I love that you brought up champagne. It really feels like a champagne moment for the AWS community as a whole. Being here on the floor feels a bit like the before times. I don't want to jinx it. Our next guest, Scott Castle, from Si Sense. Thank you so much for joining us. How are you feeling? How's the show for you going so far? Oh, >>This is exciting. It's really great to see the changes that are coming in aws. It's great to see the, the excitement and the activity around how we can do so much more with data, with compute, with visualization, with reporting. It's fun. >>It is very fun. I just got a note. I think you have the coolest last name of anyone we've had on the show so far, castle. Oh, thank you. I'm here for it. I'm sure no one's ever said that before, but I'm so just in case our audience isn't familiar, tell us about >>Soy Sense is an embedded analytics platform. So we're used to take the queries and the analysis that you can power off of Aurora and Redshift and everything else and bring it to the end user in the applications they already know how to use. So it's all about embedding insights into tools. >>Embedded has been a, a real theme. Nobody wants to, it's I, I keep using the analogy of multiple tabs. Nobody wants to have to leave where they are. They want it all to come in there. Yep. Now this space is older than I think everyone at this table bis been around since 1958. Yep. How do you see Siente playing a role in the evolution there of we're in a different generation of analytics? >>Yeah, I mean, BI started, as you said, 58 with Peter Lu's paper that he wrote for IBM kind of get became popular in the late eighties and early nineties. And that was Gen one bi, that was Cognos and Business Objects and Lotus 1 23 think like green and black screen days. And the way things worked back then is if you ran a business and you wanted to get insights about that business, you went to it with a big check in your hand and said, Hey, can I have a report? And they'd come back and here's a report. And it wasn't quite right. You'd go back and cycle, cycle, cycle and eventually you'd get something. And it wasn't great. It wasn't all that accurate, but it's what we had. And then that whole thing changed in about two, 2004 when self-service BI became a thing. And the whole idea was instead of going to it with a big check in your hand, how about you make your own charts? >>And that was totally transformative. Everybody started doing this and it was great. And it was all built on semantic modeling and having very fast databases and data warehouses. Here's the problem, the tools to get to those insights needed to serve both business users like you and me and also power users who could do a lot more complex analysis and transformation. And as the tools got more complicated, the barrier to entry for everyday users got higher and higher and higher to the point where now you look, look at Gartner and Forester and IDC this year. They're all reporting in the same statistic. Between 10 and 20% of knowledge workers have learned business intelligence and everybody else is just waiting in line for a data analyst or a BI analyst to get a report for them. And that's why the focus on embedded is suddenly showing up so strong because little startups have been putting analytics into their products. People are seeing, oh my, this doesn't have to be hard. It can be easy, it can be intuitive, it can be native. Well why don't I have that for my whole business? So suddenly there's a lot of focus on how do we embed analytics seamlessly? How do we embed the investments people make in machine learning in data science? How do we bring those back to the users who can actually operationalize that? Yeah. And that's what Tysons does. Yeah. >>Yeah. It's interesting. Savannah, you know, data processing used to be what the IT department used to be called back in the day data processing. Now data processing is what everyone wants to do. There's a ton of data we got, we saw the keynote this morning at Adam Lesky. There was almost a standing of vision, big applause for his announcement around ML powered forecasting with Quick Site Cube. My point is people want automation. They want to have this embedded semantic layer in where they are not having all the process of ETL or all the muck that goes on with aligning the data. All this like a lot of stuff that goes on. How do you make it easier? >>Well, to be honest, I, I would argue that they don't want that. I think they, they think they want that, cuz that feels easier. But what users actually want is they want the insight, right? When they are about to make a decision. If you have a, you have an ML powered forecast, Andy Sense has had that built in for years, now you have an ML powered forecast. You don't need it two weeks before or a week after in a report somewhere. You need it when you're about to decide do I hire more salespeople or do I put a hundred grand into a marketing program? It's putting that insight at the point of decision that's important. And you don't wanna be waiting to dig through a lot of infrastructure to find it. You just want it when you need it. What's >>The alternative from a time standpoint? So real time insight, which is what you're saying. Yep. What's the alternative? If they don't have that, what's >>The alternative? Is what we are currently seeing in the market. You hire a bunch of BI analysts and data analysts to do the work for you and you hire enough that your business users can ask questions and get answers in a timely fashion. And by the way, if you're paying attention, there's not enough data analysts in the whole world to do that. Good luck. I am >>Time to get it. I really empathize with when I, I used to work for a 3D printing startup and I can, I have just, I mean, I would call it PTSD flashbacks of standing behind our BI guy with my list of queries and things that I wanted to learn more about our e-commerce platform in our, in our marketplace and community. And it would take weeks and I mean this was only in 2012. We're not talking 1958 here. We're talking, we're talking, well, a decade in, in startup years is, is a hundred years in the rest of the world life. But I think it's really interesting. So talk to us a little bit about infused and composable analytics. Sure. And how does this relate to embedded? Yeah. >>So embedded analytics for a long time was I want to take a dashboard I built in a BI environment. I wanna lift it and shift it into some other application so it's close to the user and that is the right direction to go. But going back to that statistic about how, hey, 10 to 20% of users know how to do something with that dashboard. Well how do you reach the rest of users? Yeah. When you think about breaking that up and making it more personalized so that instead of getting a dashboard embedded in a tool, you get individual insights, you get data visualizations, you get controls, maybe it's not even actually a visualization at all. Maybe it's just a query result that influences the ordering of a list. So like if you're a csm, you have a list of accounts in your book of business, you wanna rank those by who's priorities the most likely to churn. >>Yeah. You get that. How do you get that most likely to churn? You get it from your BI system. So how, but then the question is, how do I insert that back into the application that CSM is using? So that's what we talk about when we talk about Infusion. And SI started the infusion term about two years ago and now it's being used everywhere. We see it in marketing from Click and Tableau and from Looker just recently did a whole launch on infusion. The idea is you break this up into very small digestible pieces. You put those pieces into user experiences where they're relevant and when you need them. And to do that, you need a set of APIs, SDKs, to program it. But you also need a lot of very solid building blocks so that you're not building this from scratch, you're, you're assembling it from big pieces. >>And so what we do aty sense is we've got machine learning built in. We have an LQ built in. We have a whole bunch of AI powered features, including a knowledge graph that helps users find what else they need to know. And we, we provide those to our customers as building blocks so that they can put those into their own products, make them look and feel native and get that experience. In fact, one of the things that was most interesting this last couple of couple of quarters is that we built a technology demo. We integrated SI sensee with Office 365 with Google apps for business with Slack and MS teams. We literally just threw an Nlq box into Excel and now users can go in and say, Hey, which of my sales people in the northwest region are on track to meet their quota? And they just get the table back in Excel. They can build charts of it and PowerPoint. And then when they go to their q do their QBR next week or week after that, they just hit refresh to get live data. It makes it so much more digestible. And that's the whole point of infusion. It's bigger than just, yeah. The iframe based embedding or the JavaScript embedding we used to talk about four or five years >>Ago. APIs are very key. You brought that up. That's gonna be more of the integration piece. How does embedable and composable work as more people start getting on board? It's kind of like a Yeah. A flywheel. Yes. What, how do you guys see that progression? Cause everyone's copying you. We see that, but this is a, this means it's standard. People want this. Yeah. What's next? What's the, what's that next flywheel benefit that you guys coming out with >>Composability, fundamentally, if you read the Gartner analysis, right, they, when they talk about composable, they're talking about building pre-built analytics pieces in different business units for, for different purposes. And being able to plug those together. Think of like containers and services that can, that can talk to each other. You have a composition platform that can pull it into a presentation layer. Well, the presentation layer is where I focus. And so the, so for us, composable means I'm gonna have formulas and queries and widgets and charts and everything else that my, that my end users are gonna wanna say almost minority report style. If I'm not dating myself with that, I can put this card here, I can put that chart here. I can set these filters here and I get my own personalized view. But based on all the investments my organization's made in data and governance and quality so that all that infrastructure is supporting me without me worrying much about it. >>Well that's productivity on the user side. Talk about the software angle development. Yeah. Is your low code, no code? Is there coding involved? APIs are certainly the connective tissue. What's the impact to Yeah, the >>Developer. Oh. So if you were working on a traditional legacy BI platform, it's virtually impossible because this is an architectural thing that you have to be able to do. Every single tool that can make a chart has an API to embed that chart somewhere. But that's not the point. You need the life cycle automation to create models, to modify models, to create new dashboards and charts and queries on the fly. And be able to manage the whole life cycle of that. So that in your composable application, when you say, well I want chart and I want it to go here and I want it to do this and I want it to be filtered this way you can interact with the underlying platform. And most importantly, when you want to use big pieces like, Hey, I wanna forecast revenue for the next six months. You don't want it popping down into Python and writing that yourself. >>You wanna be able to say, okay, here's my forecasting algorithm. Here are the inputs, here's the dimensions, and then go and just put it somewhere for me. And so that's what you get withy sense. And there aren't any other analytics platforms that were built to do that. We were built that way because of our architecture. We're an API first product. But more importantly, most of the legacy BI tools are legacy. They're coming from that desktop single user, self-service, BI environment. And it's a small use case for them to go embedding. And so composable is kind of out of reach without a complete rebuild. Right? But with SI senses, because our bread and butter has always been embedding, it's all architected to be API first. It's integrated for software developers with gi, but it also has all those low code and no code capabilities for business users to do the minority report style thing. And it's assemble endless components into a workable digital workspace application. >>Talk about the strategy with aws. You're here at the ecosystem, you're in the ecosystem, you're leading product and they have a strategy. We know their strategy, they have some stuff, but then the ecosystem goes faster and ends up making a better product in most of the cases. If you compare, I know they'll take me to school on that, but I, that's pretty much what we report on. Mongo's doing a great job. They have databases. So you kind of see this balance. How are you guys playing in the ecosystem? What's the, what's the feedback? What's it like? What's going on? >>AWS is actually really our best partner. And the reason why is because AWS has been clear for many, many years. They build componentry, they build services, they build infrastructure, they build Redshift, they build all these different things, but they need, they need vendors to pull it all together into something usable. And fundamentally, that's what Cient does. I mean, we didn't invent sequel, right? We didn't invent jackal or dle. These are not, these are underlying analytics technologies, but we're taking the bricks out of the briefcase. We're assembling it into something that users can actually deploy for their use cases. And so for us, AWS is perfect because they focus on the hard bits. The the underlying technologies we assemble those make them usable for customers. And we get the distribution. And of course AWS loves that. Cause it drives more compute and it drives more, more consumption. >>How much do they pay you to say that >>Keynote, >>That was a wonderful pitch. That's >>Absolutely, we always say, hey, they got a lot of, they got a lot of great goodness in the cloud, but they're not always the best at the solutions and that they're trying to bring out, and you guys are making these solutions for customers. Yeah. That resonates with what they got with Amazon. For >>Example, we, last year we did a, a technology demo with Comprehend where we put comprehend inside of a semantic model and we would compile it and then send it back to Redshift. And it takes comprehend, which is a very cool service, but you kind of gotta be a coder to use it. >>I've been hear a lot of hype about the semantic layer. What is, what is going on with that >>Semantec layer is what connects the actual data, the tables in your database with how they're connected and what they mean so that a user like you or me who's saying I wanna bar chart with revenue over time can just work with revenue and time. And the semantic layer translates between what we did and what the database knows >>About. So it speaks English and then they converts it to data language. It's >>Exactly >>Right. >>Yeah. It's facilitating the exchange of information. And, and I love this. So I like that you actually talked about it in the beginning, the knowledge map and helping people figure out what they might not know. Yeah. I, I am not a bi analyst by trade and I, I don't always know what's possible to know. Yeah. And I think it's really great that you're doing that education piece. I'm sure, especially working with AWS companies, depending on their scale, that's gotta be a big part of it. How much is the community play a role in your product development? >>It's huge because I'll tell you, one of the challenges in embedding is someone who sees an amazing experience in outreach or in seismic. And to say, I want that. And I want it to be exactly the way my product is built, but I don't wanna learn a lot. And so you, what you want do is you want to have a community of people who have already built things who can help lead the way. And our community, we launched a new version of the SES community in early 2022 and we've seen a 450% growth in the c in that community. And we've gone from an average of one response, >>450%. I just wanna put a little exclamation point on that. Yeah, yeah. That's awesome. We, >>We've tripled our organic activity. So now if you post this Tysons community, it used to be, you'd get one response maybe from us, maybe from from a customer. Now it's up to three. And it's continuing to trend up. So we're, it's >>Amazing how much people are willing to help each other. If you just get in the platform, >>Do it. It's great. I mean, business is so >>Competitive. I think it's time for the, it's time. I think it's time. Instagram challenge. The reels on John. So we have a new thing. We're gonna run by you. Okay. We just call it the bumper sticker for reinvent. Instead of calling it the Instagram reels. If we're gonna do an Instagram reel for 30 seconds, what would be your take on what's going on this year at Reinvent? What you guys are doing? What's the most important story that you would share with folks on Instagram? >>You know, I think it's really what, what's been interesting to me is the, the story with Redshift composable, sorry. No, composable, Redshift Serverless. Yeah. One of the things I've been >>Seeing, we know you're thinking about composable a lot. Yes. Right? It's, it's just, it's in there, it's in your mouth. Yeah. >>So the fact that Redshift Serverless is now kind becoming the defacto standard, it changes something for, for my customers. Cuz one of the challenges with Redshift that I've seen in, in production is if as people use it more, you gotta get more boxes. You have to manage that. The fact that serverless is now available, it's, it's the default means it now people are just seeing Redshift as a very fast, very responsive repository. And that plays right into the story I'm telling cuz I'm telling them it's not that hard to put some analysis on top of things. So for me it's, it's a, maybe it's a narrow Instagram reel, but it's an >>Important one. Yeah. And that makes it better for you because you get to embed that. Yeah. And you get access to better data. Faster data. Yeah. Higher quality, relevant, updated. >>Yep. Awesome. As it goes into that 80% of knowledge workers, they have a consumer great expectation of experience. They're expecting that five ms response time. They're not waiting 2, 3, 4, 5, 10 seconds. They're not trained on theola expectations. And so it's, it matters a lot. >>Final question for you. Five years out from now, if things progress the way they're going with more innovation around data, this front end being very usable, semantic layer kicks in, you got the Lambda and you got serverless kind of coming in, helping out along the way. What's the experience gonna look like for a user? What's it in your mind's eye? What's that user look like? What's their experience? >>I, I think it shifts almost every role in a business towards being a quantitative one. Talking about, Hey, this is what I saw. This is my hypothesis and this is what came out of it. So here's what we should do next. I, I'm really excited to see that sort of scientific method move into more functions in the business. Cuz for decades it's been the domain of a few people like me doing strategy, but now I'm seeing it in CSMs, in support people and sales engineers and line engineers. That's gonna be a big shift. Awesome. >>Thank >>You Scott. Thank you so much. This has been a fantastic session. We wish you the best at si sense. John, always pleasure to share the, the stage with you. Thank you to everybody who's attuning in, tell us your thoughts. We're always eager to hear what, what features have got you most excited. And as you know, we will be live here from Las Vegas at reinvent from the show floor 10 to six all week except for Friday. We'll give you Friday off with John Furrier. My name's Savannah Peterson. We're the cube, the the, the leader in high tech coverage.

(upbeat music) >> Welcome back, everyone, to theCUBE's live coverage, day one wrap-up. I'm John Furrier, with Dave Vellante. We have Jerry Chen, special guest who's been with us every year on theCUBE since inception. Certainly every AWS re:Invent, nine years straight. Jerry Chen, great to see you for our guest analyst's wrap up VC general partner, Greylock partners, good to see you. >> John, Dave, it's great to see you guys. Thanks for having me again. It wouldn't be re:Invent without the three of us sitting here and we missed last year, right, because of COVID. So we have to make up for lost time. >> John: We did a virtual one- >> Dave: we did virtual stuff= >> John: wasn't the same as in-person. >> Dave: Definitely not the same. >> Jerry: Not the same thing. So, it's good to see you guys again in person, and less than 6 feet apart. >> Cheers, yeah. >> And 7,000 people here, showing that the event's still relevant. >> Jerry: Yeah. >> Some people would kill for those numbers, it's a bad year for Amazon, down from 60,000. >> Jerry: Yeah. >> So, ecosystem's booming. Okay, let's get to it. Day one in the books, new CEO, new sheriff in town, his name's Adam Selipsky. Your take? >> Well, Adam's new, but he's old, right? Something, you know, like something new, something old, something blue, right? It's so, Adam was early Amazon, so he had that founding DNA. Left, you know, CEO of Tableau, acquired by Salesforce, came back few months ago. So I think it was a great move, because one, he's got the history and culture under Jassy, so he's definitely the Bezos Jassy tree of leadership, but yet he's been outside the bubble. Right? So he actually knows what it means to run a company not on the Amazon platform. So, I think Adam's a great choice to lead AWS for what we call it, like maybe act two, right? Act one, the first X years with Jassy, and maybe this is the second act under Adam. >> Yeah. And he's got- and he was very technical, hung around all the techies, James Hamilton, DeSantis, all the engineers, built that core primitives. Now, as they say, this cloud next gen's here, act two, it's about applications. >> Jerry: Yeah. >> Infrastructure as code is in place. Interesting area. Where's the growth come from? So, look, you know, the ecosystem has got to build these super clouds, or as you say, Castles on the Cloud, which you coined, but you brought this up years ago, that the moats and the value has to be in there somewhere. Do you want to revise that prediction now that you see what's coming from Selipsky? >> Okay, well, so let's refresh. Greylock.com/castles has worked out, like we did, but a lot of thought leadership and the two of you, have informed my thinking at Castles in the Cloud, how to compete against Amazon in the cloud. So you'd argue act one, the startup phase, the first, you know, X years at Amazon was from 2008 to, you know, 2021, the first X years, building the platform, digging the moats. Right? So what did you have? You have castle the platform business, economies of scale, which means decreasing marginal costs and natural network effects. So once the moat's in place and you had huge market share, what do you for act two, right? Now the moats are in place, you can start exploring the moats for I think, Adam talked about in your article, horizontal and verticals, right? Horizontal solutions up the stack, like Amazon Connect, CRM solutions, right? Horizontal apps, maybe the app layer, and verticals, industrials, financials, healthcare, et cetera. So, I think Jassy did a foundation of the castle and now we're seeing, you know, what Adam and his generation would do for act two. >> So he's, so there's almost like an act one A, because if you take the four hyperscalers, they're about, maybe do 120 billion this year, out of, I don't know, pick a number, it's many hundreds of billions, at least in infrastructure. >> Jerry: Correct. >> And those four hyperscalers growing at 35% collectively, right? So there's some growth there, but I feel like there's got to be deeper business integration, right? It's not just about IT transformation, it's about deeper- So that's maybe where this Connect like stuff comes, but are there enough of those? You know, I didn't, I haven't, I didn't hear a lot of that this morning. I heard a little bit, ML- >> Jerry: Sure. >> AI into Connect, but where's the next Connect, right? They've got to do dozens of those in order to go deeper. >> Either, Dave, dozens of those Connects or more of those premise, so the ML announcement was today. So you look at what Twilio did by buying Segment, right? Deconstruct a CRM to compete against Adam Selipsky's old acquire of Salesforce.com. They bought Segment, so Twilio now has communicates, like texting, messaging, email, but all the data come from Segment. >> Dave: With consumption-based pricing. >> With consumption-based pricing. So, right? So that's an example of kind of what the second act of cloud looks like. It may not look like full SaaS apps like Salesforce.com, but these primitives, both horizontally vertically, because again, what does Amazon have as an asset that other guys don't? Install based developers. Developers aren't going to necessarily build or consume SaaS apps, but they're going to consume things like these API's and primitives. And so you look around, what's cloud act two look like? It may not be VM's or containers. It may be API's like Stripe and Billing, Twilio messaging, right? Concepts like that. So, we'll see what the next act at cloud looks like. And they announced a bunch of stuff today, serverless for the data analytics, right? So serverless is this move towards not consuming raw compute and storage, but APIs. >> What about competition? Microsoft is nipping at the heels of AWS. >> Dave: John put them out of business earlier today. [John and Dave Laugh] >> No, I said, quote, I'll just- let me rephrase. I said, if Amazon goes unchecked- >> Jerry: Sure. >> They'll annihilate Microsoft's ecosystem. Because if you're an ISV, why wouldn't you want to run on the best platform? >> Jerry: Sure. >> Speeds and feeds matter when you have these shifts of software development. >> Jerry: You want them both. >> So, you know, I mean, you thought about the 80's, if you were at database, you wanted the best processor. So I think this Annapurna vertical integrated stacks are interesting because if my app runs better and I have a platform, prefabricated or purpose-built platform, to be there for me, I'm going to build a great SaaS app. If it runs faster and it cost less, I'm going to flop to Amazon. That's just, that's my prediction. >> So I think better changes, right? And so I think if you're Amazon, you say cheaper, better, faster, and they're investing in chips, proprietary silicon to run better, faster, their machine learning training chips, but if you're Azure or Google, you got to redefine what better is. And as a startup investor, we're always trying to do category definition, right? Like here's a category by spin. So now, if you're Azure or Google, there are things you can say that are better, and Google argued their chips, their TensorFlow, are better. Azure say our regions, our security, our enterprise readiness is better. And so all of a sudden, the criteria "what's better" changes. So from faster and cheaper to maybe better compliance, better visibility, better manageability, different colors, I don't know, right? You have to change the game , because if you play the same game on Amazon's turf, to your point, John, it- it's game over because they have economies of scale. But I think Azure and Google and other clouds, the superclouds, or subclouds are changing the game, what it means to compete. And so I think what's going on, just two more seconds, from decentralized cloud, being Web 3 and crypto, that's a whole 'nother can of worms, to Edge compute, what Cloudflare are doing with R2 and storage, they're trying to change the name of the game. >> Well, that's right. If you go frontal against Amazon, you're got to get decimated. You got to move the goalposts for better. And I think that's a good way to look at it, Dave. What does better mean? So that's the question that's on the table. What does that look like? And I think that's an unknown, that's coming. Okay, back to the start-ups. Category definition. That's an awesome term. That to me is a key thing. How do you look at what a category is on your sub- on your Castles of the Cloud, you brought up how many categories of- >> Jerry: 33 markets and a bunch of submarkets, yeah. >> Yeah. Explain that concept. >> So, we did Castle in the Clouds where my team looked at all the services offered at Azure, Google, and Amazon. We downloaded the services and recategorized them to like, 30 plus markets and a bunch of submarkets. Because, the reason why is apples to apples, you know, Amazon, Google, Azure all have databases, but they might call them different things. And so I think first things first is, let's give developers and customers kind of apples to apples comparisons. So I think those are known markets. The key in investing in the cloud, or investing in general, is you're either investing in budget replacement, replacing a known market, cheaper, better database, to your point, or a net new market, right? Which is always tricky. So I think the biggest threat to a lot of the startups and incumbents, the biggest threat by startups and incumbents, is either one, do something cheaper, better in a current market, or find a net new market that they haven't thought about yet. And if you can win that net new market before the rest, then that's unbelievable. We call it the, you know, the blue ocean strategy, >> Dave: Is that essentially what Snowflake has done, started with cheaper, better, and now they're building the data cloud? >> Jerry: I think there's- it's evolution, correct. So they said cheaper, better. And the Castle in the Cloud, we talked about, they actually built deep IP. So they went a known category, data warehouses, right? You had Teradata, Redshift, Snowflake cheaper, better, faster. And now let's say, okay, once you have the customers, let's change the name of the game and create a data cloud. And it's TBD whether or not Snowflake can win data cloud. Like we talked about Rockset, one of my investments that's actually move the goalpost saying, oh, data cloud is nice, but real time data is where it's at, and Snowflake and those guys can't play in real time. >> Dave: No, they're not in a position to play in real time data. >> Jerry: Right. >> Dave: I mean, that's right. >> So again, so that's an example of a startup moving the goalpost on what previously was a startup that moved the goalpost on an incumbent. >> Dave: And when you think about Edge, it's going to be real-time AI inferencing at the Edge, and you're right, Snowflake's not set up well at all for that. >> John: So competition wise, how do the people compete? Because this is what Databricks did the same exact thing. I have Ali on the record going back years, "Well, we love Amazon. We're only on Amazon." Now he's talking multicloud. >> So, you know, once you get there, you kind of change your tune cause you've got some scale, but then you got new potential entrants coming in, like Rockset. >> Jerry: Correct. >> So. >> Dave: But then, and if you add up the market caps of just those two companies, Databricks and Snowflake, it's much larger than the database market. So this, we're defining new markets now. >> Jerry: I think there's market cap, especially Snowflake that's in the public market, Databricks is still private, is optimism that there's a second or third act in the database space left to be unlocked. And you look at what's going on in that space, these real-time analytics or real-time apps, for sure there's optimism there. But, but to John's point, you're right, like you earn the right to play the next act, but it's tricky because startups disrupt incumbents and become incumbents, and they're also victims their own success, right? So you're- there's technical debt, there's also business model debt. So you're victims of your own business model, victims of your own success. And so what got you here may not get you to the next phase. And so I think for Amazon, that's a question. For Databricks and Snowflake, that's a question, is what got them here? Can they play to the next act? And look, Apple did it, multiple acts. >> John: Well, I mean, I think I- [Crosstalk] >> John: I think it's whether you take shortcuts or not, if you have debt, you make it a little bit of a shortcut bet. >> Jerry: Yeah. >> Okay. That's cool. But ultimately what you're getting at here is beachhead thinking. Get a beachhead- >> Jerry: Correct. >> Get in the market, and then sequence to a different position. Classic competitive strategy, 101. That's hard to do because you want to win the beachhead- >> I know. >> John: And take a little technical debt and business model debt, cheat a little bit, and then, is it not fortified yet? So beachhead to expansion is the question. >> Jerry: That's every board meeting, John and Dave, that we're in, right? It's called you need a narrow enough wedge to land. And it is like, I don't want the tip of the spear, I want the poison on the tip of a spear, right? [Dave and John Laugh] >> You want, especially in this cloud market, a super focused wedge to land. And the problem is, as a founder, as investor, you're always thinking about the global max, right? Like the ultimate platform winner, but you don't get the right to play the early- the late innings if you don't make it out of the early innings. And so narrow beachhead, sharp wedge, but you got to land in a space, a place of real estate with adjacent tan, adjacent markets, right? Like Uber, black cars, taxi's, food, whatever, right? Snowflake, data warehouse, data cloud. And so I think the key with all startups is you'll hit some ceiling of market size. Is there a second ramp? >> Dave: So it's- the art is when to scale and how fast to scale. >> Right. Picking when, how fast, in which- which best place, it was tough. And so, the best companies are always thinking about their second or third act while the first act's still going. >> John: Yeah. And leveraging cloud to refactor, I think that's the key to Snowflake, was they had the wedge with data warehouse, they saw the position, but refactored and in the cloud with services that they knew Teradata wouldn't use. >> Jerry: Correct. >> And they're in. From there, it's just competitive IP, crank, go to market. >> And then you have the other unnatural things. You have channel, you have installed base of customers, right? And then you start selling more stuff to the same channel, to the same customers. That's what Amazon's doing. All the incumbent's do that. Amazon's got, you know, 300 services now, launching more this week, so now they have channel distribution, right? Every credit card for all the developers, and they have installed base of customers. And so they will just launch new things and serve the customers. So the startups had to disrupt them somehow. >> Well, it's always great to chat with Jerry. Every year we discover and we riff and we identify, in real time, new stuff. We were talking about this whole vertical, horizontal scale and kind of castles early on, years ago. And now it's happened. You were right. Congratulations. That's a great thesis. There's real advantages to build on a cloud. You can get the- you can build a business there. >> Jerry: Right. >> John: That's your thesis. And by the way, these markets are changing. So if you're smart, you can actually compete. >> Jerry: I think you beat, and to Dave's earlier point, you have to adapt, right? And so what's the Darwin thing, it's not the strongest, but the most adaptable. So both- Amazon's adapt and the startups who are the most adaptable will win. >> Dave: Where are you, you guys might've talked about this, where do you stand on the cost of goods sold issue? >> Jerry: Oh, I think everything's true, right? I think you can save money at some scale to repatriate your cloud, but again, Wall Street rewards growth versus COGS, right? So I think you've got a choice between a dollar of growth versus a dollar reducing COGS, people choose growth right now. That may not always be the case, but at some point, if you're a company at some scale and the dollars of growth is slowing down, you definitely have to reduce the dollars in cost. And so you start optimizing cloud costs, and that could be going to Amazon, Azure, or Google, reducing COGS. >> Dave: Negotiate, yeah. >> John: Or, you have no visibility on new net new opportunities. So growth is about new opportunities. >> Correct. >> If you repatriating things, there's no growth. >> Jerry: It's not either, or- >> That's my opinion. >> Jerry: COGS or growth, right? But they're both valid, definitely, so you can do both. And so I don't think- it's what's your priorities, you can't do everything at once. So if I'm a founder or CEO or in this case investor, and I said, "Hey, Dave, and John, if you said I can either save you 25 basis points in gross margin, or I can increase another 10% top line this year", I'm going to say increase the top line, we'll deal with the gross margin later. Not that it's not important, but right now the early phase- >> Priorities. >> Jerry: It's growth. >> Yeah. All right, Jerry Chen, great to see you. Great to have you on, great CUBE alumni, great guest analyst. Thanks for breaking it down. CUBE coverage here in Las Vegas for re:Invent, back in person. Of course, it's a virtual event, we've got a hybrid event for Amazon, as well as theCUBE. I'm John Furrier, you're watching the leader in worldwide tech coverage. Thanks for watching. (Gentle music)

>>Well, good day and welcome back to the cube as we continue our segment featuring AWS star showcase we're with now Mike Bilodeau, who's in corporate development and operations at Kong. Mike, uh, thank you for joining us here on the cube and particularly on the startup showcase. Nice to have you and pong represented here today. Thanks for having me, John. Great to be here. You bet. All right, first off, let's just tell us about pong a little bit and, and, uh, con cadet, which I know is your, your feature program, um, or, um, service. Oh, I love the name by the way. Um, but tell us a little bit about home and then what connect is all about to? Sure. So, uh, Kong as a company really came about in the past five years, our two co-founders came over from Italy in, uh, in the late, in the late aughts, early 20 teens and, uh, had a company called Mashape. >>And so what they were looking at and what they were betting on at that time was that API APIs, uh, were going to be the future of how software was built and how developers interacted with software. And so what came from that was a piece of, uh, they were running that shape as a marketplace at the time. So connecting developers sit in for an API so they can consume them and use them to build new software. And what they found was that actually the most valuable piece of technology that they created was the backbone for running that marketplace. And that backbone is what Kong is. And so they created it to be able to handle a massive amount of traffic, a massive amount of API APIs, all simultaneously. This is a problem that a lot of enterprises have, especially now that we've started to get some microservices, uh, started to, to have more distributed technologies. >>And so what Kong is really is it's a way to manage all of those different API APIs, all of the connections between different microservices, uh, through a single platform, which is called connect. And now that we've started to have Coobernetti's, uh, the, sort of the birth and the, the nascent space of service mesh con connect allows all of those connections to be managed and to be secured and made reliable, uh, through a single platform. So what's driving this right. I mean, um, you, you mentioned micro services, um, and Coobernetti's, and that environment, which is kind of facilitating, you know, this, uh, I guess transformation you might say. Um, but what's the big driver in your opinion, in terms of, of what's pushing this microservices phenomenon, if you will, or this revolution. Sure. And when I think it starts out at, at the simple active of technology acceleration in general, um, so when you look at just the, the real shifts that have come in enterprise, uh, especially looking, you know, start with that at the cloud, but you could even go back to VMware and virtualization is it's really about allowing people to build software more rapidly. >>Um, all of these different innovations that have happened, you know, with cloud, with virtualization now with containers, Kubernetes, microservices, they're really focused on making it, uh, so that developers can build software a lot more quickly, uh, develop the, the latest and greatest in a more rapid way. >>A huge driver out of this is just making it easier for developers, for organizations to bring new technologies to market. Uh, and we see that as a kind of a key driver in a lot of these decisions that are being made. I think another piece of it that's really coming about is looking at, uh, security, uh, as a really big component, you know, do you have a huge monolithic app? Uh, it can become very challenging to actually secure that if somebody gets into kind of that initial, uh, into the, the initial ops space, they're really past the point of no return and can get access to some things that you might not want them to similar for compliance and governance reasons that becomes challenging. So I think you're seeing this combination of where people are looking at breaking things into smaller pieces, even though it does come with its own challenges around security, um, that you need to manage, it's making it so that, uh, there's less ability to just get in and cause a lot of damage kind of all at once. Often Melissa malicious attackers. >>Yeah. You bring up security. And so, yeah, to me, it's almost, in some cases it's almost counterintuitive. I think about, I've got the, if I got this model, the gap and I've got a big parameter around it, right. And I know that I can confine this thing. I can contain this. This is good. Now microservices, now I got a lot of, it's almost like a lot of villages, right. They're all around. And, and uh, I don't have the castle anymore. I've got all these villages, so I have to build walls around all these villages. Right. But you're saying that there that's actually easier to do, or at least you're more capable of doing that now as opposed to living >>Three years ago. Well, you can almost think of it, uh, as if you have this little just right, and you might, um, if you have one castle and somebody gets inside, they're going to be able to find whatever treasury may have, you know, to extend the analogy here a bit, but now it's different, uh, 50 different villages that, you know, uh, an attacker needs to look in, it starts to become really time-consuming and really difficult. And now when you're looking at, especially this idea of kind of cybersecurity, um, the ability to secure a monolithic app is typically not all that different from what you can do with a microservice or with a once you get past that initial point, instead of thinking of it, you know, I have my one wall around everything, you know, think of it almost as a series of walls where it gets more and more difficult. Again, this all depends on, uh, that you're, you're managing that security well, which can get really time-consuming more than anything else and challenging from a pure management standpoint, but from an actual security posture, it is a way of where you can strengthen it, uh, because you're, you're creating more, um, more difficult ways of accessing information for attackers, as well as just more layers potentially of security. >>But what do you do to lift that burden then from, from the customer? Because like you said, that that that's a concern they really don't want to have. Right. They want, they want you to do that. They want somebody to do that for them. So what can, what do you do to alleviate those kinds of stress >>On their systems? Yeah, it's a great question. And this is really where the idea of API management and, um, in it's in its infancy came from, was thinking about, uh, how do we extract a way these different tasks that people don't really want to do when they're managing, uh, how API, how people can interact with their API APIs, whether that be a device or another human, um, and part of that is just taking away. So what we do and what API gate management tools have always done is abstract that into a, a new piece of software. So instead of having to kind of individually develop and write code for security, for logging, for, you know, routing logic, all these different pieces of how those different APIs will communicate with each other, we're putting that into a single piece of software and we're allowing that to be done in a really easy way. >>And so what we've done now with con connect and where we've extended that to you, is making it even easier to do that at a microservices level of scale. So if you're thinking about hundreds or thousands of different microservices that you understand and be able to manage, that's what we're really building to allow people to do. And so that comes with, you know, being able to, to make it extremely easy, uh, to, to actually add policies like authentication, you know, rate-limiting, whatever it may be, as well as giving people the choice to use what they want to use. Uh, we have great partners, you know, looking at the Datadog's, the Okta's of the world who provide a pretty, pretty incredible product. We don't necessarily want to reinvent the wheel on some of these things that are already out there, and that are widely loved and accepted by, uh, technology, practitioners and developers. We just want to make it really easy to actually use those, uh, those different technologies. And so that's, that's a lot of what we're doing is providing a, a way to make it easy to add this, you know, these policies and this logic into each one of these different services. >>So w if you're providing these kinds of services, right. And, and, and, and they're, they're, they're new, right. Um, and you're merging them sometimes with kind of legacy, uh, components, um, that transition or that interaction I would assume, could be a little complex. And, and you've, you've got your work cut out for you in some regards to kind of retrofit in some respects to make this seamless, to make this smooth. So maybe shine a little light on that process in terms of not throwing all the, you know, the bath out, you know, with, with the baby, all the water here, but just making sure it all works right. And that it makes it simple and, and, um, takes away that kind of complexity that people might be facing. >>Yeah, that's really the name of the game. Uh, we, we do not believe that there is a one size fits all approach in general, to how people should build software. Uh, there are going to need instances aware of building a monolithic app. It makes the most sense. There are going to be instances where building on Kubernetes makes the most sense. Um, the key thing that we want to solve is making sure that it works and that you're able to, to make the best technical decision for your products and for your organization. And so in looking at, uh, sort of how we help to solve that problem, I think the first is that we have first class support for everything. So we support, you know, everything down to, to kind of the oldest bare metal servers to NAMS, to containers across the board. Uh, and, and we had that mindset with every product that we brought to market. >>So thinking about our service mesh offering, for instance, um, Kula is the open source project that under tens now are even, but looking at Kumo, one of the first things that we did when we brought it out, because we saw this gap in space was to make sure that that adds first-class support for and chance at the time that wasn't something that was commonly done at all. Uh, now, you know, there there's more people are moving in that direction because they do see it as a need, which is great for the space. Um, but that's something where we, we understand that the important thing is making sure your point, you said it kind of the exact way that we like to, which is it needs to be reliable. It needs work. So I have a huge estate of, you know, older applications, older, uh, you know, potentially environments, even. I might have data centers that might've cloud being, trying to do everything all at once. Isn't really a pragmatic approach. Always. It needs to be able to support the journey as you move to, to a more modern way of building. So in terms of going from on-premise to the cloud, running in a hybrid approach, whatever it may be, all of those things shouldn't be an all or nothing proposition. It should be a phase approach and moving to, to really where it makes sense for your business and for the specific problem >>Talking about cloud deployments, obviously AWS comes into play there in a major way for you guys. Um, tell me a little bit about that, about how you're leveraging that relationship and how you're partnering with them, and then bringing the, the value then to your customer base and kind of how long that's been going on and the kinds of work that you guys are doing together, uh, ultimately to provide this kind of, uh, exemplary product or at least options to your customers. >>Yeah, of course. I think the way that we're doing it first and foremost is that, um, we, we know exactly who AWS is and the space and, and, you know, a great number of our customers are running on AWS. So again, I think that first class support in general for AWS environments services, uh, both from the container service, their, their Kubernetes services, everything that they can have and that they offer to their customers, we want to be able to support, uh, one of the first areas of really that comes to mind in terms of first-class integration and support is thinking about Lambda and serverless. Um, so at the time when we first came out, was that, again, it was early for us, uh, or early in our journey as product and as company, uh, but really early for the space. And so how we were able to support that and how we were able to see, uh, that it could support our vision and, and what we wanted to bring as a value proposition to the market has been, you know, really powerful. So I think in looking at, you know, how we work with AWS, certainly on a partnership level of where we share a lot of the same customers, we share a very similar ethos and wanting to help people do things in the most cost-effective rapid manner possible, and to build the best software. Uh, and, you know, I mean, for us, we have a little bit of a backstory with AWS because Jeffrey's us was a, an early investor in, in common. >>Yeah, exactly. I mean, the, the whole memo that he wrote about, uh, you know, build an API or you're fired was, was certainly an inspiration to, to us and it catalyzed, uh, so much change in, in the technology landscape in general, about how everyone viewed API APIs about building a software that could be reused and, and was composable. And so that's something that, you know, we, we look at, uh, kind of carrying forward and we've been building on that momentum ever since. So, >>Well, I mean, it's just kind of take a, again, a high level, look at this in terms of microservices. And now that it's changing in terms of cloud connectivity. Thank you. Actually, I have a graphic to that. Maybe we can pull up and take a look at this and let's talk about this evolution. You know, what's occurring here a little bit, and, and as we take a look at this, um, tell us what you think those, these impacts are at the end of the day for your customers and how they're better able to provide their services and satisfy their customer needs. >>Absolutely. So this is really the heart of the connect platform and of our vision in general. Um, we'd spoken just a minute ago about thinking how we can support the entire journey or, uh, the, the enterprise reality that is managing a, a relatively complex environment of modelists different services, microservices, you know, circle assumptions, whatever it may be, uh, as well as lots of different deployment methods and underlying tech platforms. You know, if you have, uh, virtual machines and Kubernetes, whatever, again, whatever it may be. But what we look at is just the different sort of, uh, design patterns that can occur in thinking about a monolithic application. And, um, okay. Mainly that's an edge concern of thinking about how you're going to handle connectivity coming in from the edge and looking at a Kubernetes environment of where you're going to have, you know, many Kubernetes clusters that need to be able to communicate with each other. >>That's where we start to think about, uh, our ingress products and Kubernetes ingress that allows for that cross applic, uh, across application communication. And then within the application itself, and looking at service mesh, which we talked a little bit about of just how do I make sure that I can instrument and secure every transaction that's happening in a, a truly microservices, uh, deployment within Kubernetes or outside of it? How do I make sure that that's reliable and secure? And so what we look at is this is just a, uh, part of it is evolution. And part of it is going to be figuring out what works best when it, um, certainly if you're, if you're building something from scratch, it doesn't always make sense to build it, your MDP, as, you know, microservices running on Kubernetes. It probably makes sense to go with the shortest path, uh, at the same time, if you're trying to run it at massive scale and big applications and make sure they're as reliable as possible, it very well does make sense to spend the time and the effort to, to make humanize work well for you. >>And I think that's, that's the, the beauty of, of how the space is shifting is that, uh, it's, it's going towards a way of the most practical solution to get towards business value, to, to move software quicker, to give customers the value that they want to delight them to use. Amazon's, uh, you know, phrase ology, if that's, uh, if that's a word, uh, it's, it's something of where, you know, that is becoming more and more standard practice versus just trying to make sure that you're doing the, the latest and greatest for the sake of, of, uh, of doing it. >>So we've been talking about customers in, in rather generic terms in terms of what you're providing them. We talked about new surfaces that are certainly, uh, providing added value and providing them solutions to their problems. Can you give us maybe just a couple of examples of some real life success stories, where, where you've had some success in terms of, of providing services that, um, I assume, um, people needed, or at least maybe they didn't know they needed until, uh, you, you provided that kind of development that, but give us an idea of maybe just, uh, shine, a little light on some success that you've had so that people at home watching this can perhaps relate to that experience and maybe give them a reason to think a little more about calm. >>Yeah, absolutely. Uh, there, there's a number that come to mind, but certainly one of the customers that I spent a lot of time with, uh, you know, become almost friends would be with, uh, with the different, with a couple of the practitioners who work there is company called Cargill. Uh, it's a shared one with us and AWS, you know, it's one we've written about in the past, but this is one of the largest companies in the world. Um, and, uh, the, the way that they describe it is, is that if you've ever eaten a Vic muffin or eaten from McDonald's and had breakfast there, you you've used a Cargill service because they provide so much of the, the food supply chain business and the logistics for it. They had a, uh, it's a, it's an old, you know, it's a century and a half old company. >>It has a really story kind of legacy, and it's grown to be an extremely large company that's so private. Uh, but you know, they have some of the most unique challenges. I think that I've, I've seen in the space in terms of needing to be able to ensure, uh, that they're able to, to kind of move quickly and build a lot of new services and software that touch so many different spaces. So they were, uh, the challenge that was put in front of them was looking at really modernizing, you know, again, a century and a half old company modernizing their entire tech stack. And, you know, we're certainly not all of that in any way, shape or form, but we are something that can help that process quite a bit. And so, as they were migrating to AWS, as they were looking at, you know, creating a CICB process for, for really being able to ship and deploy new software as quickly as possible as they were looking at how they could distribute the, the new API APIs and services that they were building, we were helping them with every piece of that journey, um, by being able to, to make sure that the services that they deployed, uh, performed in the way that they expected them to, we're able to give them a lot of competence and being able to move, uh, more rapidly and move a lot of software over from these tried and true, uh, you know, older or more legacy of doing things to a much more cloud native built as they were looking at using Kubernetes in AWS and, and being able to support that handle scale. >>Again, we are something that was able to, to kind of bridge that gap and make sure that there weren't going to be disruptions. So there, there are a lot of kind of great reasons of why they're their numbers really speak for themselves in terms of how, uh, how much velocity they were able to get. You know, they saying them saying them out loud on the sense fake in some cases, um, because they were able to, you know, I think like something, something around the order of 20 X, the amount of new API APIs and services that they were building over a six month period, really kind of crazy crazy numbers. Um, but it is something where, you know, the, for us, we, we got a lot out of them because they were open source users. So calling is first and foremost, an open source company. >>And so they were helping us before they even became paying customers, uh, just by testing the software and providing feedback, really putting it through its paces and using it at a scale that's really hard to replicate, you know, the scale of a, uh, a couple of hundred thousand person company, right? Yeah. Talking about a win-win yeah. That worked out well. It's certainly the proof is in the pudding and I'm sure that's just one of many examples of success that you've had. Uh, we appreciate the time here and certainly the insights and wish you well on down the road. Thanks for joining us, Mike. Thanks, Sean. Thanks for having me. I've been speaking with Mike Villa from Kong. He is in corporate development and operations there on John Walls, and you're watching on the cube, the AWS startup showcase.

(upbeat music) >> Well, good day and welcome back to the Cube as we continue our segment, featuring AWS Startup Showcase and we're with now Mike Bilodeau who's in corporate development and operations at Kong. Mike, thank you for joining us here on the Cube and particularly on the Startup Showcase. Nice to have you and Kong represented here today. >> Thanks for having me, John. Great to be here. >> You better and first off let's just tell us about Kong a little bit and column cadet which I know is your feature program or service. I love the name by the way, but tell us a little bit about Kong and then what Kong is all about too? >> Sure, so Kong as a company really came about in the past five years. Our two co-founders came over from Italy in the late aughts early to 20 teens and had a company called Mashape. And so what they were looking at and what they were betting on at that time, was that APIs were going to be the future of how software was built and how developers interacted with software. And so what came from that was a piece of they were running Mashape as a marketplace at the time. So connecting developers to different APIs so they can consume them and use them to build new software. And what they found was that actually the most valuable piece of technology that they had created was the backbone for running that marketplace. And that backbone is what Kong is. And so they created it to be able to handle a massive amount of traffic, a massive amount of APIs, all simultaneously. This is a problem that a lot of enterprises have especially now that we've started to get some microservices, started to have more distributed technologies. And so what Kong is really is, it's a way to manage all of those different APIs. All of the connections between different microservices through a single platform which is Kong connect. And now that we've started to have Kubernetes the sort of the birth and the nascent space of service mesh. Kong connect allows all of those connections to be managed and to be secured and made reliable through a single platform. >> So what's driving this, right? I mean you mentioned microservices and Kubernetes and that environment which is kind of facilitating this, I guess transformation you might say. But what's the big driver in your opinion in terms of what's pushing this microservices phenomenon if you will or this revolution? >> Sure, and when I think it starts out at the simple active of technology acceleration in general. So when you look at just the real shifts that have come in enterprise to hack especially looking, you know start with that at the cloud but you could even go back to VMware and virtualization is it's really about allowing people to build software more rapidly. All of these different innovations that have happened with cloud, with virtualization, now with containers, Kubernetes, microservices they're really focused on making it so that developers can build software a lot more quickly. Develop the latest and greatest in a more rapid way. I think a huge driver out of this is just making it easier for developers, for organizations to bring new technologies to market. And we see that as a key driver in a lot of these decisions that are being made. I think another piece of it that's really coming about is looking at security as a really big component. You know we have a huge monolithic app. It can become very challenging to actually secure that. If somebody gets into the initial Ops space they're really past the point of no return and can get access to some things that you might not want them to. Similar for compliance and governance reasons, that becomes challenging. So I think you're seeing this combination of where people are looking at breaking things into smaller pieces, even though it does come with its own challenges around security that you need to manage. It's making it so that there's less ability to just get in and cause a lot of damage all at once from malicious attackers. >> Yeah, you bring up security and so, yeah to me it's almost in some cases it's almost counterintuitive. I think about if I got this model to gap and I've got a big parameter around it, right. And I know that I can confine this thing. I can contain this, this is is good. Now microservices, now got a lot of, it's almost like a lot of villages, right? They're all around. And I don't have the castle anymore. I've got all these villages. So I have to build walls around all these villages. But you're saying that that's actually easier to do or at least you're more capable of doing that now as opposed to maybe where we were two, three years ago. >> Well you can almost think of it as if you have those villages, right. And if you have one castle and somebody gets inside they're going to be able to find whatever treasure you may have you know, to extend the analogy here a bit. But now if you have 50 different villages that an attacker needs to look in it starts to become really time consuming and really difficult. And now when you're looking at, especially this idea of cybersecurity, the ability to secure a monolithic app is typically not all that different from what you can do with a microservice or once you get past that initial point. Instead of thinking of it as, you know I have my one wall around everything you now think of it almost as a series of walls where it gets more and more difficult. Again this all depends on that you're managing that security well which can get really time-consuming more than anything else and challenging from a pure management standpoint. But from an actual security posture it is a way of where you can strengthen it because you're you're creating more difficult ways of accessing information for attackers as well as just more layers potentially of security that they need to get them. >> But what do you do to lift that burden then from the customers because like you said that's a concern they really don't want to have. They want you to do that. They want somebody to do that before them. So what do you do to alleviate those kinds of stresses on their systems? >> Yeah, it's a great question. And this is really where the idea of API management in its infancy came from. Was thinking about, how do we abstract a way these different tasks that people don't really want to do when they're managing how people can interact with their APIs whether that be a device or another human? And part of that is just taking away. So what we do and what API game management tools have always done is abstract that into a new piece of software. So instead of having to kind of individually develop and write code for security, for logging, for routing logic, all these different pieces of how those different APIs will communicate with each other we're putting that into a single piece of software, And we're allowing that to be done in a really easy way. And so what we've done now with Kong connect and where we've extended that to is making it even easier to do that at a microservices level of scale. So if you're thinking about hundreds or thousands of different microservices that you need to understand and be able to manage that's what we're really building to allow people to do. And so that comes with being able to make it extremely easy to actually add policies like authentication, rate limiting whatever it may be as well as giving people the choice to use what they want to use. We have great partners looking at the Datadog's, the Okta's of the world who provide a pretty, pretty incredible product. We don't necessarily want to reinvent the wheel on some of these things that are already out there and that are widely loved and accepted by technology practitioners and developers. We just want to make it really easy to actually use those different technologies. And so that's a lot of what we're doing is providing a a way to make it easy to add these policies and this logic into each one of these different services. >> So what if you're providing these kinds of services and they're new and you're merging them sometimes with kind of legacy components? That transition or that interaction I would assume could be a little complex. And you've got your work cut out for you in some regards to kind of retrofit, right? In some respects to make this seamless, to make this smooth. So maybe you shine a little light on that process in terms of not throwing all the bath out with the baby or the water here, but just making sure it all works. And that it makes it simple and takes away that kind of complexity that people might be facing. >> Yeah, that's really the name of the game. We do not believe that there is a one size fits all approach in general to how people should build software. There are going to be instances of where building a monolithic app makes the most sense. There are going to be instances where building a Kubernetes makes the most sense. The key thing that we wonna solve is making sure that it works and that you're able to make the best technical decision for your products and for your organization. And so in looking at how we help to solve that problem, I think the first is that we have first-class support for everything. So we support everything down to kind of the oldest bare metal servers, to IBMs to containers across the board. And we've had that mindset with every product that we brought to market. So thinking about our service mesh for instance Kuma is the open-source project that all depends now on an enterprise one. But looking at Kuma, one of the first things that we did when we brought it out because we saw this gap in the space was to make sure that they have first-class support for virtual machines. At the time that wasn't something that was commonly done at all. Now more people are moving in that direction because they do see it as it need which is great for the space. But that's something where we understand that the important thing is making sure your point you said it kind of the exact way that we like to which is it needs to be reliable. It needs to work. So I have a huge estate of older applications, older potentially environments even I might have data centers, I might have cloud been trying to do everything all at once. Isn't really a pragmatic approach always. It needs to be able to support the journey as you move to a more modern way of building. So in terms of going from on-premise to the cloud, running in a hybrid approach, whatever it may be, all of those things shouldn't be an all-or-nothing proposition. It should be a phased approach and moving to really where it makes sense for your business and for the specific product. >> You've been talking about cloud deployments obviously. AWS comes into play there in a major way for you guys. Tell me a little bit about that. About how you're leveraging that relationship and how you're partnering with them and then bringing the value then to your customer base. And how long that's been going on and the kinds of work that you guys are doing together ultimately to provide this kind of exemplary product or at least options to your customers. >> Yeah, of course. I think the way that we're doing it first and foremost is that we know exactly who AWS is in the space. And great number of our customers are running on AWS. So again, I think that first-class support in general for AWS environments, services both from the container service, their Kubernetes services, everything that they can have and that they offer to their customers we wonna be able to support. One of the first areas really that comes to mind in terms of first-class integration and support is thinking about Lambda and serverless. So at the time when we first came out with that, again it was early for us or early in our journey as product and as company, but really early for the space. And so how we were able to support that and how we were able to see that it could support our vision and what we wanted to bring as a value proposition to the market has been really powerful. So I think in looking at how we work with AWS certainly on a partnership level of where we share a lot of the same customers we share a very similar ethos and wanting to help people do things in the most cost-effective rapid manner possible and to build the best software. And I mean for us we have a little bit of a backstory with AWS 'cause Jeff Bezos was an early investor in Kong. >> That didn't hurt really. Yeah exactly, I mean the whole memo that he wrote about build an API or you're fired was certainly an inspiration to us. And just it catalyzed so much change in the technology landscape in general about how everyone viewed APIs about building a software that could be reused and and was composable. And so that's something that we look at and kind of carry it forward and we've been building on that momentum ever since. >> So I'm going to just kind of take, again a high level. Look at this in terms of microservices and how that's changing in terms of cloud connectivity. Think you actually have a graphic too that maybe we can pull up and take a look at this and let's talk about this evolution. What's occurring here a little bit and as we take a look at this tell us what you think these impacts are at the end of the day for your customers and how they're better able to provide their services and satisfy their customer needs. >> Absolutely, so this is really the heart of the connect platform and of our vision in general. We've spoken just a minute ago about thinking how we can support the entire journey or the enterprise reality that is managing a relatively complex environment of monoliths, different services, microservices, serverless functions, whatever it may be as well as lots of different deployment methods and underlying tech platforms. If you have virtual machines and Kubernetes whatever it may be. But what we look at is just the different design patterns that can occur in thinking about a monolithic application. Okay, mainly that's an edge concern of thinking about how you going to handle connectivity coming in from the edge in looking at a Kubernetes environment of where you going to have many Kubernetes clusters that need to be able to communicate with each other. That's where we start to think about our ingress products and Kubernetes ingress that allows for that cross application communication. And then within the application itself and looking at service mesh which we talked a little bit about of just how do I make sure that I can instrument and secure every transaction that's happening in a truly microservices deployment within Kubernetes or outside of it? How do I make sure that that's reliable and secure? And so what we look at is part of it is evolution. And part of it is going to be figuring out what works best when. Certainly if you're building something from scratch it doesn't always make sense to build it. Your MDP as microservices running on Kubernetes it probably makes sense to go with the shortest path. At the same time if you're trying to run it at massive scale and big applications and make sure they're as reliable as possible it very well does make sense to spend the time and the effort to make Kubernetes work well for you. And I think that's the beauty of how the space is shifting is that it's going towards a way of the most practical solution to get towards business value to move software quicker to give customers the value that they want to delight them to use Amazon's phraseology if that's a word. It's something that is becoming more and more standard practice versus just trying to make sure that you're doing the latest and greatest for the sake of doing it. >> So we've been talking about customers in rather generic terms in terms of what you're providing them. We've talked about new services that are certainly providing added value and providing them with solutions to their problems. Can you give us maybe just a couple of examples of some real life success stories where you've had some success in terms of providing services that I assume people needed or at least maybe they didn't know they needed until you provided that kind of development. But give us an idea, maybe just shine a little light on some success that you've had so that people at home and are watching this can perhaps relate to that experience and maybe give them a reason to think a little more about Kong and Kong connect. >> Yeah, absolutely, there's a number that come to mind but certainly one of the customers that I have spent a lot of time with, become almost friends with a couple of the practitioners who work there, is company called Cargill. It's a shared one with us and AWS. It's one we've written about in the past but this is one of the largest companies in the world. And the way that they describe it as is that if you've ever eaten a McMuffin or eaten from McDonald's and had breakfast there, you've used a Cargill service because they provide so much of the food supply chain business and the logistics for it. You know, it's a century and a half old company. It has a really story and a legacy and it's grown to be an extremely large company that's still private. But they have some of the most unique challenges, I think that I've seen in the space in terms of needing to be able to ensure that they're able to kind of move quickly and build a lot of new services and software that touch so many different spaces. So the challenge that was put in front of them was looking at really modernizing a century and a half old company. Modernizing their entire tech stack. And we're certainly not all of that in any way shape or form but we are something that can help that process quite a bit. And so as they were migrating to AWS as they were looking at creating a CICB process for really being able to shape and deploy new software as quickly as possible. As they were looking at how they could distribute the new APIs and services that they were building, we were helping them with every piece of that journey by being able to to make sure that the services that they deployed performed in the way that they expected them to. We're able to give them a lot of confidence in being able to move more rapidly and move a lot of software over from these tried and true older or more legacy ways of doing things to a much more cloud native build. As they were looking at using Kubernetes in AWS and being able to support that handle scale, again we're something that was able to kind of bridge that gap and make sure that there weren't going to be disruptions. So there are a lot of great reasons of why their numbers really speak for themselves in terms of how much velocity they were able to get. Saying them out loud will sound fake in some cases because they were able to, you know, I think like something around the order of 20 X the amount of new APIs and services that they were building over a six month period. Really kind of crazy, crazy numbers. But it is something where, for us we got a lot out of them because they were open-source users. So Kong is first and foremost an open-source company. And so they were helping us before they even became paying customers. Just by testing the software, providing feedback, really putting it through its paces and using it at a scale that's really hard to replicate. You know the scale of a couple of hundred thousand person company, yeah. >> Talk about a win-win. That worked out well. Certainly the proof is in the pudding and I'm sure that's just one of many examples of success that you've had. We appreciate the time here and certainly the insights and I wish you well on down the road. Thanks for joining us Mike. >> Thanks John, thanks for having me. >> Been peaking with Mike Bilodeau from Kong. He is in corporate development and operations there. I'm John Walls and you're watching "On the Cube" the AWS Startup Showcase. (soft music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Hey, is Keith Townsend, principal at CTO Adviser, and you're watching the Cube virtual coverage of AWS reinvent 2020. I'm really excited whenever we get toe talk to actual end users. Builders. The conversation is dynamic. This is no exception. Back on the show, Al Vanish despondent head off architectures at logic I've been ish. Welcome back to the show. >>Thanks, Kate. Good to be here >>and on the other side of my screen or how you depend on how you're looking at it is Anthony Brooks Williams C E O off HBR Anthony, Welcome back to the Cube. I know your kind of tired of seeing us, but the conversation is gonna be good, I promise. >>Thanks very much. Look forward to being here and great as you said to talk about a use case for the customer in the real world. >>So I'll be let's start off by talking about lodge attacking. What are you guys doing in a W s in general? I mean, e no. Every company has public cloud, but Logitech and AWS and Public Cloud doesn't naturally come to mind. Help educate the audience. What do you guys doing? >>Sure, so traditionally, audience knows Logitech as the Mice and keyboard company, but we do have a lot of brands which are cool brands off logic tech If you know about gaming, Logitech G is a huge brand for us. We are in video collaboration space. We compete with the likes off Ciscos of the world, where we have hardware that goes on bond works with Zoom Google as well as Microsoft ecosystems. That has been a huge success in a B two b well for us. Beyond music industry gaming as an Astro gaming Jay Bird head phones for athletes. We are also in security system space. On top of that were also in the collaboration space off streaming as in stream labs so a Z can see logic has grown toe where that a lot off use cases, apart from just peripherals, is out there. We connected devices, so we're also looking to move towards a cloud ecosystem where we could be in on on our toes, toe provisioning information on DNA, make sure we are computing to the best of the world. So we are in AWS. We do a lot more in AWS now, compared to what we used to do in the past last five years has seen a change and a shift towards more cloud public cloud usage pure SAS environments in the ws as well And we provisioned data for analysis and essentially a data driven enterprise. Now more so on V as we move towards more future >>and Anthony talked to me about not necessarily just largest heck, but the larger market. How are you seeing companies such as logic? Heck take advantage off A W s and Public cloud. >>Yeah, but I think you mean ultimately we've seen it accelerated the show. Me Castle's just looking for a better way to connect with their prospects, you know, and leverage data in doing so. And we've seen this this driver around digital transformation and that's just being sped up the shirt, given what we've seen around covert and so a lot more companies have really pushed forward and adopting, you know, the infrastructure and the availability off systems and solutions that you find in a platform such as AWS on bets that we've seen grand deduction from our side of customers doing that, we provide the most efficient way of protesters to move data to so platforms such as I don't yes, and that's what we've seen. A big uptick picture. >>So let's focus the conversation around data data, the new oil. We've heard the taglines. Let's put some meat on the bone, so to speak and talk through How are you at logic Tech using real time data in the public cloud? >>Sure, Yeah. I mean, traditionally, if you look at it, uh, logic could selling hardware. Andi hope it >>works for >>the end consumer. Uh, we would not necessarily have an insight into how that product is being used. I think come fast forward. Today's world. It's a connected devices environment. You want to make sure when you sell something, it is working for that consumer. You would want them to be happy about that product, ensuring a seamless experience. Eso customer experience is big. You might want to see a repeat customer come about right. So So the intent is to have a lot off. It is connected experience where you could provisional feedback loop to the engineering team toe to ensure stability off the product, but also enhancements around that product in terms off usage patterns. And and we play a big role with hardware in what you're gaming, for example. And as you can see, that whole industry is growing toe where everything is connected. Probably people do not buy anything, which is a static discussing thing. It's all online gaming. So we want to ensure we don't add Leighton. See in the hardware that we have, ensuring a successful experience and repeat customers right? The essential intent is at the end of the day, to have success with what you sell because there's obviously other options on the market and you want to make sure our customers are happy with the hardware they are investing. Maurin that hardware platform and adding different, very fills along with it so that seamless experiences where we wanna make sure it's connected devices to get that insight. We also look at what people are saying about our products in terms off reviews on APS are on retail portals to ensure we we hear the wise off customer on channel. How's that energy in a positive way to improve the products as well as trying to figure out if there are marketing opportunities were you could go across sailing up cells, so that's essentially driving business towards that success, and at the end of it, that would essentially come up with a revenue generation model >>for us. So Anthony talked to me about how HBR fits into this, because when I look at cloud big, that can be a bit overbearing, like, where's where's the starting point? >>So I mean, for us, you mean the starting point Answer questions around. Acquiring the data data is generated in many places across organizations in many different platforms and many systems. And so we have the ability to have a very efficient technique in the way we go acquire data the way we capture data through this technique called CBC Chinese share the capture where you're feeding incremental updates off off the data across the network. That's the most efficient way to move this data. Firstly, across a wide area network cloud is an endpoint. Uh, you mean off that, And so, firstly, we specializing in supporting many different source systems and so we can acquire that data very efficiently, put it into our into a very scalable, flexible architecture that we have. That's that's a great foot for this modern world of great foot for the cloud. So not only can we capture data from many different source systems, their complexities and a lot of these type of the moments that customers have, we could take the data and move it very efficiently across that network at scale. Because we know, as you've said, data is the new oil that's the lifeblood of organizations today. So we can move that data efficiently at scale across the network and then put it into a system such a snowflake running in AWS like we do for a hobby and a larger taken. So that's really where we fit. I mean, we can, you know, we support data taken from many sources, too many different target systems. We make sure that data is highly accurate. When we move that data across that matches what was in the source of matches, what's in the in the target system. And we do that in this particular use case and what we see predominantly today, the source systems are capturing the data typically today. Still generated on Prem could be data that's sitting in an SFP environment. Unpack that data. Decode that data is to be complex to get out and understand it on moving across and put it in their target system, that predominance sitting in the cloud for all the benefits that we see that the cloud brings around elasticity and efficiency and operational costs the most type of things. And that's probably human in where we fit into this picture. >>You know, I think if I add a little bit there, right, So to Anthony's point for us, we generate a lot of data. You're looking at billions off rolls a day from the edge where people like you and I are using logic devices and we also have a lot off prp transactions That going so the three V s Typically that they call about big data is like the variety off data volume of data at velocity that you want to consume it. So the intent is if you need to be data driven, the data should be available for business consumption as it is being generated very near real time, and that the intent for some of these platforms like H we are, is How efficiently could you move that data, whether it's on Prem or a different cloud into AWS on giving it for business consumption of business analysis in near real time. So you know we strive, Toby Riel time. Whether it's data from China in our factory, on the shop floor, whether it's being generated from people like you and I playing a game for eight hours on generating so many events, we're gonna ensure all that data is being available for business analysis and gone out of those days where we would load that data once a day. And in the hope that we do a weekly analysis right today, we do analysis on make business decisions on that data as the data is being generated. And that's the key to success with such platforms, where we want to make sure we also look at build vs buy rather than us doing all that core and trying toe in just that data we obviously partner with which we are in certain application platforms to ensure stability off it. And they have proven with their experience the I P or the knowledge around how to build those platforms, which even if we go build it, we might need bigger teams to build that. I would rather rely on partners for that capability. And I bring more business value by enabling and implementing such solutions. >>So let's put a little color around that skill whenever I talk to CDOs. Chief data officers, data architects One of the biggest problems that they have in these massive systems you're talking about getting data from E. AARP uh, Internet of things devices, etcetera is simple data transformation. E t l data scientists spend a good droid at a time, maybe sometimes 80% of their time on that data transformation process that slows down the ability to get answers to critical business. Analytic questions. How is HBR assistant you guys and curling down at time for detail? >>Absolutely. So we we do not. We went to cloud about five years back, and the methodology that you talk about e t. L is sort of a point back in the day when you would do, you know, maybe a couple of times a day ingestion. So it's like in the the transition off the pipeline. As you are ingesting data, you would transform and massage the return, enhance the data and provisioned it for business consumption. Today we do lt we extract loaded into target and natively transform it as needed from business consumption. So So we look at each. We are, for example, is, uh, we're replicating all off our e r P data into snowflake in the cloud for real time ingestion and consumption. Uh, if you do all of this analysis on article side to it, typically you would have ah, processing where you would put put in a job toe, get that data out, and analysis comes back to you in a couple of hours out here, you could be slicing and dicing the data as needed on it's all self serve on provisioning. We do not build analysis foreign users. Neither do we do a lot off the data science. But we want to make sure when businesses using that data they can act on that as it's available on the example is we had a processing back in the day with demand forecasting, which we do for every product off logic for 52 weeks, looking ahead for for every week, right, and it will run for a couple of days that processing today with such platforms on in public Lot. We do that in an hour's time. Right now That's critical for business success because you want to know the methodologies you feel need Tofail or have challenges. You probably wanna have them now rather than wait a couple of days for that process in the show up, and then you do not have enough time to, at just the parameters are bringing back some other business process toe augmented. So that's what we look at. The return on investment for such investment are essentially ensuring business continuity and success outfront on faster time to deliver. >>Yeah, >>so, Anthony, this seems like this would really change the conversation within enterprises. The target customer or audience really changes from kind of this IittIe centric movement tome or strategic move. We talked to me about the conversations you've had, what customers and how this has transformed their business. >>Yeah, a few things to unpack there, um, one. You mean, obviously, customs wanna make decisions on the freshest data, so they typically relied on in the past on these batch orientated tough data movement techniques, which which will be touched on there and how we're able to reduce that that time window. Let them make decisions on the freshest data where that takes, you, choose into other parts of organizations. Because, Azzawi said, already, I mean, we know that is the lifeblood of them. There was many, I would say, Typically, I t semi, but let's call it data. Seven people sitting in the both side of organizations, if not Mawr, than used to sit in the legacy I t side. They want access to this data. They want to be able to access their daily easy. And so one of these things cloud based system SAS based systems have made that a lot easier for them. And the conversations. We have a very much driven from not only the chief data officers, but the CEOs. Now they know in order to get the advantage to win. To survive in today's times, they need to be data driven organizations, and it sounds cliche. We hear these digital transformation stories and data driven taglines. They get thrown out there, but what we've seen is where it's really it's been put to toss this year it is happening. Projects that would happen 9 12 months have been given to month Windows to happen because it's a matter of survival and so that's what's really driven. And then you also have the companies that benefit as well. You mean we're fortunate that we are able as a company globally, with composer of all to work from her very efficiently. But then support customers like Obvious who or providing these work from home technology systems that can enable another? The semester It's really moved. That's driven down from being purely I t driven to its CEO, CEO, CEO driven because its's what they've got to do. It z no longer just table stakes. >>I >>think the lines are great, right way we roll up into CEO and like I work for the CEO at at large detect. But we strive to be more service oriented than support. So I t was traditionally looked at as a support our right. But we obviously are enabling the enterprise to be data driven, so we strive to be better at what we do and how we position ourselves. As as more off service are connected to business problem, we understand the business problem and the challenge that they have on and ensuring we could find solutions and solution architectures around that problem to ensure success for that, right? And that's the key to it. Whether we build, vs, buy it. It's all about ensuring business doesn't have toe find stopgap solutions to be successful in finding a solution for their problem. >>Avi Anthony, I really appreciate you guys taking the time to peel back the layers and help the audience understand how to take thes really abstract terms and make them rial for getting answers on real time data and kind of blowing away these concepts of E t l and data transformations and how toe really put data toe work using public cloud resource sources against their real time data assets. Thank you for joining us on this installment of the Cube virtual as we cover A W s re event, make sure to check out the portal and Seymour great coverage off this exciting area off data and data analysis

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hello everyone welcome to the cube conversation here in Palo Alto our studios of the cube I'm showing for your host we're here during the crisis of Cove in nineteen doing remote interviews I come into the studio we've got a quarantine crew or here getting the interviews getting the stories out there and of course the story we continue to talk about is the impact of Kovan 19 and how we're all getting back to work either working at home or working remotely and virtually certainly but as things start to change we can start to see events mostly digital events and we're here to talk about an event that's coming up called the failover conference from gremlin which is now gone digital because it's April 21st but I think what's important about this conversation that I want to get into is not only talk about the event that's coming up but talk about these scale problems that are being highlighted by this change in work environment working at home we've been talking about the at scale problems that we're seeing whether it's a flood of surge of traffic and the chaos that's ensuing across the world with this pandemic so I'm excited have two great guests Alberto Ferran auto senior vice president marketing gremlin and Tammy Bhutto principal site reliability engineer or SRE guys thanks for coming on appreciate it thank you Thank You Alberto I want to get to you first you know we've known each other before you've been in this industry we all we've been all been talking about the cloud native cloud scale for some time it's kind of inside the ropes it's inside baseball Tami your site reliability engineer everyone knows Google knows how well cloud works this is large-scale stuff now with The Cove in 19 we're starting to see the average person my brother my sister our family members and people around the world go oh my god this is really a high impact this change of behavior the surge of you know whether whether it's traffic on the internet or work at home tools that are inadequate you start to see these statistical things that were planned for not working well and this actually Maps the things that we've been talking about it in our industry Alberto you've been on this how you guys doing and what's your what's your take on this situation we're in right now yeah yeah we're we're doing pretty well as a company we were born as a distributed organization to begin with so for us working in a distributed environment from all over the world is is common practice day-to-day personally you know I'm originally from Italy my parents my family is Milan and Bergen audible places so I have to follow the news with extra care and so much in me it becomes so much clearer nowadays that technology is not just a powerful tool to enable our businesses but it also is so critical for our day-to-day life and thanks to you know video calls I can easily talk to my family back there every day Wow so that's that's really important so yes we've been talking for a long time as you mentioned about complex systems at scale and reliability often in the context of mission-critical applications but more and more these systems need to be reliable also when it comes to back office systems that enable people to continue to work on a daily basis yeah well our hearts go out to your family and your friends in Italy and hope everyone's stay safe there no that was a tough situation continues to be a challenge Tammy I want to get your thoughts how is life going for you you're a sight reliable engineer what you deal with on the tech side is now happening in the real world it's it's almost it's mind-blowing and to me that we're seeing these these things happen it's it's a paradigm that needs attention and whew look at it as a sre dealing a most from a tech side now seeing it play out in real life it's such an interesting situation really terrible so one of the things that I specialize in as a site reliability engineer is incident management and so for example I previously worked at Dropbox where I was you know the incident manager on call for 500 million customers you know it's like 24/7 and these large-scale incidents you really need to be able to act fast there are two very important metrics that we track and care about as a site reliability engineer the first one is mean time to detection how fast can you detect what something is happening obviously if you detect an issue faster and you've got a better chance of making the impact lower so you can contain the blast radius I like to explain it to people like if you have a fire in your sauce bin in your kitchen and you put it out that's way better than waiting until your entire house is on fire and the other metric is mean time to resolution so how long does it take you to recover from the situation so yeah this is a large-scale global incident right now that we're in yeah I know you guys do a lot of talk about chaos theory and that applies a lot of math involved we all know that but I think when you go look at the real world this is gonna be table stakes and you know there's now a line in the sand here you know pre-pandemic post pandemic and i think you guys have an interesting company gremlin in the sense that this is this is a complex system and if you think about the world we're going to be living in whether it's digital events that you guys are have one coming up or how to work at home or tools that humans are going to be using it's going to be working with systems right so you have this new paradigm gonna be upon us pretty quickly and it's not just buying software mechanisms or software it's a complex system it's distributed computing and operating so I mean this is kind of the world can you guys talk about the gremlin situation of how you guys are attacking these new problems and these new opportunities that are emerging one of the things that I've always specialized in over the last 10 years is chaos engineering and so the idea of chaos engineering is that you're injecting failure on purpose to uncover weaknesses so that's really important in distributed systems with distributed you know cloud computing all these different services that you're kind of putting together but the idea is if you can inject failure you can actually figure out what happens when I inject that small failure and then you can actually go ahead and fix it one of the things I like to say to people is you know focus on what your top 5 critical systems are let's fix those first don't go for low-hanging fruit fix the biggest problems first get rid of the biggest amount of pain that you have as a company and then you can go ahead and like actually if you think about Pareto principle the 80/20 rule if you fix 20% of your biggest problems you actually solve 80% of your issues that always works something that I've done while working at National Australia Bank doing chaos engineering also what gremlin at Dropbox and I help a lot of our customers do that to albariño talk about the mindset involved it's almost counterintuitive whoa-oh-oh risk the biggest system and I don't want to touch those there working fine right now and then these problems just gestate they kind of hang around to the bin in the kitchen fire you know mist okay I don't want to touch it the house is still working so this is kind of a new mindset could you talk about what your take is on that is the industry there I mean oh it was a kind of a corner case you know you had Netflix you had the chaos monkey those days and then now it's the DevOps practice for a lot of folks you guys are involved in that what's the what's the appetite what's the progress of chaos engineering and mainstream yeah it's interesting that you mentioned DevOps and you know recently Gartner came up with a new revisited devil scream work that has chaos engineering in the middle of the lifecycle of your application and the reality is that systems have become so complex in infrastructure so many layers of abstractions you have hundreds of services if you're doing micro services but even if you're not doing micro services you have so many applications connected to each other build really complex workflows and automation flows it's impossible for traditional QA to really understand well the vulnerability are in terms of resiliency in terms of quality too often the production environment is also too different from the staging environment and so you need a fundamentally different approach to go and find where your weaknesses are and find them before they happen before you end up finding yourself in a situation like the one we're in today and you're not prepared and so much of what we talk about is giving it >> and the methodology for people to go and find these vulnerabilities not so much about creating cause chaos but it's about managing sales that is built into our current system and exposing those vulnerabilities before they create problem and so that's a very scientific methodology and and and tooling that we would bring to market and we help customers with Tammy I want to get your thoughts on so you know we used to riff a lot of to our 10th you know cube we've had a lot of conversation we've ripped over the over the years but you know when the surge of Amazon Web Services came out as pretty obvious the clouds amazing and look at the startups that were born you mentioned Dropbox you work there these comings and all these born in the cloud these hyper scale comes built from scratch great way to scale up and we used to joke about Google people say I would like a cloud like Google but no one has Google's use cases and Google really pioneered the sre concept and you gotta give them a lot of props for that but now we're kind of getting to a world where it's becoming Google like there's more scale now than ever before it's not a corner case it's becoming more popular and more of a preferred architecture this large scale what's your assessment of the of the mainstream enterprises how far are they did in your mind our way are they there with Castle they clothed how they doing it how does someone take how does someone develop an SRE practice to get the Google like scale because Google has an amazing network they got large-scale cloud they have sres they've been doing it for years how does a company that's transforming their IT have expertise it's a great question I get asked this a lot as well one of our goals at Bremen is to help make Internet more reliable for everybody everyone using the Internet all of the engineers who are trying to build reliable services and so I'm often asked by you know companies all over the world how do we create an SRE practice and how do we practice chaos engineering and so actually how you can get started actually rolling out your sre program based on my experiences I've done it so when I worked at Dropbox I worked with a lot of people who had been at Google they've been at YouTube they were there when was rolled out across those companies and then they brought those learnings to Dropbox and I learned from them but also the interesting thing is if you look at enterprise companies so large banks say for example I worked at a National Australia Bank for six years we actually did a lot of work that I would consider chaos engineering and sre practices so for example we would do large-scale disaster recovery and that's where you fail over an entire data center to a secret data center in an unknown location and the reason is because you're checking to make sure that everything operates okay if there's a nuclear blast that's actually what you have to do and you have to do that practice every quarter so but but if you think about it it's not very good to only do it once a quarter you really want to be practicing chaos engineering and injecting failure on this I think actually my I prefer to do it three times a week do I do it a lot but I'm also someone who likes to work out a lot and be fit all the time so I know that do something regularly you get great results so that's what I always tell us yeah I get the reps in as we say you know get get stronger at the muscle memory guys talk about the event that's coming up you got an event that was schedules physical event and then you were right in the planning mode and then the crisis hits you going digital going virtual it's really digital but it's digital that's on the internet so how are you guys thinking about this I know I it's out there it's April 21st can you share some specifics around the event well who should be attending and how they get involved online yeah yeah they vent really came about about together about a month ago when we started to see all the cancellations happening across the industry because of code 19 and we are extremely engaged with in the community and we have a lot of talks and we are seeing a lot of conferences just dropping and so speakers losing their opportunity to share their knowledge with respect to how you do reliability and topics that we focus on and so we quickly people it as a company and created a new online event to give everyone in the community the opportunity to you know they'll over to a new event as the president as a as the conference name says and and have those speakers will have lost their speaking slots have a new opportunity to go share their knowledge and so that came together really quickly we share the idea with a dozen of our partners and everyone liked it and all the sudden this thing took off like crazy in just a month where we are approaching you know four thousand registrations we have over 30 partners signed up and supporting the initiative a lot of a lot of past partners as well covering the event so it was impressive to see the amount of interest that that we were able to generate in such a short amount of time and really this is a conference for anybody who is interested in resilience and if you want to know from the best on how to build business continuity of persistence people and processes this is a great opportunity at no cost we need some free conference and the target persona and the audience you want to have a ten is what Sree Zoar folks doing architectural work and what's that that's the target yes and to attend our cadets s Ari's developers business leaders who care about the quality and reliability of their applications who need to help create a framework and a mindset for their organization that speaks to what Tammy was saying a minute ago having that constant crap is on a daily basis about who and finding how to improve things you know Tammy we've been doing going to physical events with the cube and extracting the signal of the noise and distributing it digitally for ten years and I got to ask you because now that those are those events have gone away you talk about chaos and injecting failure these doing these digital events is not as easy it's just live streaming it's it's hard to replicate the value of a physical event years of experience and standards roles and responsibilities to digital different consumption environments a synchronous you're trying to create a synchronous environment it's its own complex system so I think a lot of people are experimenting and learning from these events because it's pretty chaotic so I'd love to get your thoughts on how you look at these digital events as a chaos engineer how should people be looking at these events how are you I was looking at it you know I also want to get the program going get people out there get the content but you have to iterate on this how do you view this it is really different so I actually like to compare it to fire drills in SRA so often what you do there is you actually create a fake incident or a fake issue so you just you know you're saying let's have a fire drill similar to like you know when you're in a building and you have a fire drill that goes off you have wardens and everything and you all have to go outside so we can do that in this new world that we're all in all of a sudden you know a lot of people have never run an online event and now all of a sudden they have to so what I would say is like do a fire drill um run up you know a baked one before you do the actual on one to make sure that everything does work okay my other tip is make sure that you have backup plans backup plans on backup plans on backup plans like as in SRA I always have at least three to five backup plans like I'm not just saying plan a and Plan B but there's also a C D and E and I think that's very important and you know even when you're considering technology one of the things we say with chaos engineering is you know if you're using one service inject failure and make sure that you can fail over to a different alternative service in case something goes wrong yeah hence the failover conference which is the name of the conference yeah yeah well we certainly are gonna be sending a digital reporter there virtually if you need any backup plans obviously we have the remote interviews here if you need any help let us know really appreciate it I'll great to see you guys and thanks for sharing any final thoughts on the conference how what what happens when we get through the other side of this I'll give you guys a final word we'll start with Alberto with you first yeah I think one when we are on the other side of this will will understand even more the importance of effective resilience architecting and and and testing I think you know as a provider of tools and methodologies for that we we think we will be able to help customers do we do a significant leap forward on that side and the conference is just super exciting I think it's going to be a great I encourage everyone to participate we have tremendous lineup of speakers that have incredible reputation in their fields so I'm really happy and and excited about the work that the team has being able to do with our partners put together this type of event okay Tammy yes ma'am I'm actually going to be doing the opening keynote for the conference and the topic that I'm speaking about is that reliability matters more now than ever and I'll be sharing some you know bizarre weird incidents that I've worked on myself that I've experienced you know really critical strange issues that have come up but yeah I just I'm really looking forward to sharing that with everybody else so please come along it's free you can join from your own home and we can all be there together to support each other you got a great community support and there's a lot of partners press media and an ecosystem and customers so congratulations gremlin having a conference on April 21st called the failover conference the qubits look at angle we'll have a digital reporter there we covering the news thanks for coming on and sharing and appreciate the time I'm Jeff we're here in the Palo Alto series with remote interview with gremlin around there failover conference April 21st it's really demonstrating in my opinion the at scale problems that we've been working on the industry now more applicable than ever before as we get post pandemic with kovin 19 thanks for watching be back [Music]

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host Stu minimun hi I'm Stu minimun and this is a special cube conversation from our Boston area studio following up from the Cronus global cyber summit it happened recently down in Miami Beach Florida John Fourier was a host there you can always go to the cube net to get all of the content here happy to welcome to the program first I've got Pat Hurley who's the vice president and general manager of Americas for a Cronus and joining him as one of his partners Jason Nolan who's the vice president of business development at S Castle both you locally-based thank you so much for joining us great to be here thanks for having us - all right so Pat why don't we start with you we talked a little bit earlier with William tall about some of the announcements give us some of the things and that specifically might be it'd be important to to the partners like Jason well first of all was a fantastic event was our inaugural cyber summit we had great attendance from our partners and getting a lot of feedback about the content that was there actually Jason was one of our panel speakers we got a lot of very positive feedback there as well fantastic event for us the the food was even great so we enjoyed that it was on Miami Beach fantastic location so from our side we thought was a very successful event now the biggest challenge we will have is making even that much better next year yeah did you get the stone crab while you were down there Jason who is unbelievable huh yeah so you were out the show you got to sit on some panels you know you were feeling the energy it was great to interact for the audience and kind of hear the questions that they had and the excitement and the energy around the messaging was really really powerful all right so bring us a little bit into the solutions how are they benefiting you know all of your partners absolutely so for those of you guys who don't know really who Acronis does a lot of people know us really as a backup company from back in the day maybe consumer backup maybe small medium-sized business on-premise backup solutions we've completely transformed the company over the last few years and how we talk about cyber protection which is the combination of cybersecurity and and and data protection we frame that in some tenants that we call sabes so safety accessibility privacy authenticity and security we take those solutions delivering the partners like as cast so that they can then wrap additional services around their customer base to increase the ARPU that they're getting there increase the margin that they're collecting from their customers and obviously deliver an end-to-end complete cyber protection solution all right so Jason you're here is the voice of the customer so as Castle what are your customers telling you and how does that resonate with them so for our customers data protection has always been important they've had to address the number one rule is never lose the data and with the cyber threats today always changing they're not sure what to do so they turn to us as their service provider to help guide them through you know to make sure that they're not one of the next companies on the news and it's nice as a service provider to be able to combine those those services and products with a vendor like a Cronus so that we can provide more value we can strengthen relationships and not have 300 vendors that we have to work with all right my understanding you spend a lot of time with the financial institutions absolutely they don't want to be the next one you know on the front page of the paper in the news on the radio and the like so anything specifically for them that that's worth calling out so I think with the financial services companies having the ability to protect their data their portfolio that they hold you know so important to their business they don't want anyone to have access to that and if any of their so they have to meet the requirements of the investors they have to meet the requirements of the financial institutions and make sure that they're following all of the different guidelines and depending on which markets are in what countries are in they all have different data sovereignty rules they have to deal with gdpr and so there's a lot of different areas that they need to navigate and so they as castle as a service provider we help them understand you know and kind of build that in as a standard and that's what we've done with the Cronus is we've built in the data protection strategy and now we can look at adding in the cybersecurity components to our portfolio to help give them that comprehensive suite and then I you can imagine how it takes a lot of different solutions to pack those together to provide an end band solution for their customers I think one of the beauties of recurrence is that we allow you to provide multiple services in a single pane of glass so you get a lot of very smart people on your team that have to manage multiple solutions what we try to provide is that single opportunity that single solution they learn one thing where they can be backup disaster recovery secure files things are all in one platform allow them to kind of minimize the number of solutions they need to be experts on to provide their customers the highest level service all right Jason security is a very much a multi-faceted you know ever-growing landscape out there tell us how is castle partners with the Cronus and how it fits into your your overall services so our partnership with the Cronus first started with data protection it was one of the first solutions that we were able to find that was able to fit every use case so as a platform as a service provider we're supporting on-premise legacy equipment our hosted VMware cloud infrastructure multi-tenant and infrastructure as your every flavor of cloud services you could imagine because we want the customer to have the solution that fits their needs the best and what we were looking for and a Cronus was able to provide for us was one platform of data protection that was able to be universal across all the different use cases so that's where it starts as a foundation always protecting the data always having a backup in multiple locations and all of our data centers worldwide and now to be able to layer on top of that some of the cybersecurity components in one single pane of glass is only going to allow us to give a better level of service to our customers and Panna I expect that a lot of stuff that we talked about with the financial services translate to many other industries yeah I mean the of the day data's data right and you could talk about different verticals how they use that data the other day it's all about protecting the data making sure your data is secure making sure you have an authentic copy of your data making sure that everything is secure so for us you know we we are known as a backup company but backup is kind of going away you need a more complete solution so one of the things that all these guide bad bad doers out they're doing is they're really trying to go after your backups and trying to lock them down because they understand that that's a first place you're gonna go to try to recover from a ransomware attack our solutions are based on artificial intelligence allowing the machine learning capabilities within our solutions to detect those from from the beginning from to prevent our customers from a zero-day attack so that you're not relying on that one backup to make sure your infrastructure can get back up and running you know and Jason maybe just frame for us the relationship between you and your customers and security you hear everything from you know certain cloud providers are like you know well you know we're like your landlord you know you made her lock your doors and take care of all that stuff and others are more you know hey we're gonna you know really go belly to belly with you and make sure that we've done everything bulletproof with you but what do you hear these days and what we're hearing from the customers is that they're looking to everyone is looking to migrate either start their cloud strategy if they haven't if they've been you know behind the curve if they've had a cloud strategy they were looking to increase we've actually had some customers want to maybe come out of the hyperscale as already so there's a lot of different use cases a lot of different journeys that the customers run and I think helping them navigate so what we've been able to do is as part of our services is wrap around the different cloud services a layer of security at each component so there's that perimeter network the you know there's all of the firewalls next-gen firewalls are now are a requirement they're no longer optional mobile devices endpoint protection network security fishing spearfishing user education there's so many different things that that their own employees need to be aware of that they never had to worry about before and it's it's almost you know like 20 years ago when disaster recovery emerged on the market cybersecurity now is front and center and if you're not paying attention to it at some point it's gonna come up and bite them so we're working with our customers to make sure they never have to deal with that yeah and I think an important part of that it's no longer just the data center right it's all those edge devices right we live in a very connect world data is transferred across multiple devices every day so there's different points where there's a vulnerability that could be identified and you can't just rely on an end user to make sure that they're protecting me well and especially if I know when I was having the earlier conversation with William we're talking about the smbs you know you know if the enterprise I've got my C so and I've got my team and I'm gonna work on that if I'm the SMB well it might be a generalist that security is under the bucket of all the other things that they need to do and therefore they're going to need to turn to their platforms and their partners to help them with a lot of this I mean to say they go to the IT guy right who say well he resolves everything at the end of the day enterprises have big budgets to spend on the stuff I heard something for the analysts reports that you know they're talking about high-level guy at Bank America so what's your budget for cybersecurity I have a budget that ever needs to be spent we're gonna spend on that to make sure that our customers data is secure what we really try to do is package lot of that stuff together to make it affordable complete secure for any customers no I absolutely think most of your customers don't have the billions of dollars to be able to say that they've at least done what they needed to do to make sure that they've they've done all they can so Jason I'll give you the final word first and Pat for you know things that you took away from the show and bring in to your customers so a in the panel discussion we had at the show we were asked to talk about different experiences as a service provider and one of the things that was really important for us that came from the audience was you know what does it take to switch how do you select your vendors and I think what's often overlooked by service riders is the cost of choosing a vendor and what we mean by that is if we were to choose the wrong vendor there is a huge cost of operations to switch from one vendor to the other where you're taking a very limited resource pool of the people on the operations team that are usually focused on on boarding new customers servicing the existing customer base generating revenue who now have to go to non revenue operations just to make that heavy-lift of a transition so picking the partnership with the Kronus was really important to us we made that change and it's been the best decision we've ever made yeah just to piggyback off of that we're not someone that our partners right so we considered as Castle be very strong channel partner of ours they give us reach into that mm custer community the other day they're really the experts we're providing some technology they can rely upon upon to provide a secure complete solution for their customers but that was really the key takeaway for me as you're able to interact face-to-face with your partners directly you're able to hear some of the pain points that they deal with on a daily basis it's not over email so I don't know phone calling on a zoom or WebEx you know you're talking face-to-face these guys understand those real-time problems and working toward solutions together at one big event so that's been fantastic we hope to double attendance for the next event and bring even more partners into the fold pen Jason thank you so much for sharing your takeaways from the Acronis global cybersecurity summit I'm Stu Mittleman and thanks as always for watching the cute

>> Narrator: From Seattle, Washington it's the CUBE covering AWS Imagine non-profit. Brought to you by Amazon web services. >> Hey welcome back everybody, Jeff Frick here with the CUBE. We're on the waterfront in Seattle, it's an absolutely gorgeous couple of days here at the AWS Imagine Nonprofit Conference. We went to the AWS Imagine Education Conference, this is really all about nonprofits and we're hearing all kinds of interesting stories about how these people are using AWS to help conquer really big problems. We're going to shift gears a little bit from the two footed problems to the four footed problems and that's animals and everybody likes animals but nobody likes animal shelters and nobody likes the ultimate solution that many animal shelters used to use to take care of problems. But thank you to our next guest, that is not quite the case so much anymore. So we're really happy to have Angie Embree on. She is the CIO of Best Friends Animal Society, Angie great to see you. >> It's great to see you as well and thank you for having me. >> Oh absolutely! So before we got on I just heard this crazy, crazy statistic that when your organization started in 1984 approximately 17 million animals were killed in US shelters per year. That number is now down to 700 thousand, that is a giant, giant reduction. And yet you, with big audacious goals really are looking to get that to zero. So, that's a giant goal, give us a little bit of background on the organization and how you decided to go after a goal like that and some of the ways you are actually going to achieve it. >> Well, the organization started in 1984 and it started with a group of friends in Southern Utah who decided that, you know the killing in America's shelters just had to go. So really the Best Friends founders started the no-kill movement along with a gentleman in San Francisco by the name of Rich Avanzino. And as you said, they took you know the killing down from 17 million in 1984 to approximately 733 thousand now. The organization started as just the sanctuary, we have the largest no-kill companion animal sanctuary in the country where we hold about 17 hundred animals every day. And we also have, you know, knowing that we needed to help out the rest of the country we have built life saving centers in Houston, Texas. Or we're working on Houston, Texas but Los Angels, California, New York City, Salt Lake City, Atlanta, Georgia, it seems like I've left somebody out but, >> Probably, but that's okay. >> We have life saving centers all over the country. So it was really, you know, when they realized what was going on in America's shelters it was really the idea that we should not be killing animals for space. So, just recently in fact, I will say recently but in the last few years, Julie Castle our CEO put kind of, did our moon shot, put that stake in the ground and said we're going to take this country no-kill by the year 2025. >> Right. >> So it's super exciting. >> So it's really interesting because you guys are trying to execute your vision, and it's easy to execute your own vision, but it's a whole different thing when you're trying to execute your vision through this huge infrastructure of shelters that have been around forever. So, I wonder if you can explain kind of what's your relationship with shelters that you don't own. I guess, I think you said before we turned on the cameras there are affiliates, so how does that relationship work? How do you help them achieve your goal which is no-kill. >> Yeah, so we have over 27 hundred network partners around the country. And what we do is we help to educate them on, you know we understand their problems, we have creative programs to solve those problems. So we help to educate them on, you know, how they can implement these programs within their shelters. We provide them grant funding, we have an annual conference every year where they can come and learn. But they're really our partners and you know we know we can't do it alone. It's going to take us, it's going to take them and it's going to take everybody in every community to really step up and help solve the problem. >> Right, and what was the biggest thing that changed in terms of kind of attitude in terms of the way they operate the shelter because I think you said before that a lot of the killing was done to make room. >> Right, killing is done usually for space. >> So what do they do know? Clearly the space demands probably haven't changed so what are they doing alternatively where before they would put the animal down? >> Well alternatively we're doing transport programs. So there are areas in the country that actually have a demand for animals. So instead of killing the animals, we put them on some sort of transport vehicle and we take them to the areas that are in demand. We also do what's called a trap-neuter-return program. So one of the biggest problems across the country are community cats so those, a lot of people call them feral cats but they're community cats and usually have a caretaker. But what we do is we trap those cats, we take them into the shelter, we neuter them and vaccinate them and then return them to their home. That keeps them from making a lot of other little cats. >> Making babies (laughs) >> So yeah, cat's are one of the biggest problems in shelters today because of the community cats, they're feral cats and they're not adoptable. So if we can, we don't have to kill them. We can, you know, we can keep them from reproducing as I said and then we can put them back in their habitat where they live a long healthy life, happy life. >> Right, so you said you've joined the organization 5 years ago, 5 and 1/2 years ago and you're the CIO, first ever CIO. >> I am (laughs) >> What brought you here and then now that you're here with kind of a CIO hat, what are some of the new perspective that you can bring to the organization that didn't necessarily, that they had had before from kind of a technical perspective? >> Well, what brought me here was, I never expected to be here, if you would have told me I would be the CIO at Best Friends Animal Society you know 10 years ago I would have said you're kidding because I didn't really realize that there were professional positions in organizations like Best Friends. But I, you know, my journey begins the same as, began the same as a lot of peoples did. I was that little kid always bringing home animals and you know my mother hated it. You know it was always something showing up at our doorstep with me, you know. And I just loved animals all my life and as I went through college and got my degree and started my professional career, then I thought well I'm going to of course have animals because I can have as many as I want now, right! (laughs) So I started adopting, and I didn't even realize until I was in my 30s that they were killing in shelters and I learned that in Houston, Texas when I lived there. I was working for IBM at the time, and one day a lady came on the television and she said they were doing a new segment and she said we're a no-kill shelter and I thought oh my god if there are no-kill shelters then there are kill shelters, right? >> There must be the other. >> Yeah so, to make a long story short then I started not working in animal welfare but doing more to support the movement and donating. Adopting from shelters and fostering animals and then one day I had been to Best Friends as a visitor vacationing in this beautiful part of Utah. But I saw the CIO ... >> Position. >> position open and I said I'm going for it. >> Good for you. >> Yeah. >> Good for you, so now you're there so what are some of the things you've implemented from kind of a techy, you know kind of data perspective that they didn't have before? >> Well, they didn't have a lot. >> They probably didn't have a lot, besides email and the obvious things. >> Being the first CIO I don't know that I knew what I was walking into at the time because I got to Kanab, and Kanab Utah where the sanctuary is, is the headquarters. And Kanab is very infrastructure challenged. >> (laughs) Infrastructure challenged, I like that. >> There is one ISP in Kanab and there is no redundancy in networks so we really don't have, you know, you come from the city and you think, you take these things for granted and you find out oh my god, what am I going to do? And Kanab is you know the hub of our network, so if Kanab goes down, you know the whole organization is down so one of the first decisions I made was that we were going to the cloud. >> Right, right. >> Because we had to get Kanab out of that position and that was one of our, one of the first major decisions I made and we chose AWS as our partner to do that so that was very very exciting. We knew that they had infrastructure we couldn't dream of providing. >> Right, right. >> And, you know we could really make our whole network more robust, our applications would be available and we could really do some great things. >> You're not worried about the one ISP provider in Kanab because of an accident that knocks a phone pole down. >> Yeah, yeah. >> All right but then you're talking about some new things that you're working on and a new thing you talked about before we turned the cameras on community lifesaving dashboards, what is that all about? >> Okay, so a couple of years ago the community lifesaving dashboard is the culmination of two years of work. From all across the Best Friends organization not just the IT department, in fact it was the brainchild of our Chief Mission Officer Holly Sizemore. But it's really, in animal welfare there's never been a national picture of what the problem really is regarding killing animals in shelters. So we did this big. >> Because they're all regional right? They're all regional shelters, very local. >> They're all local community shelters, yes. And transparency isn't forced, so you know some states force transparency, they reinforce in the report numbers but a lot of states don't. >> At the state level. >> Yeah, a lot of states don't, so. You know when you're killing animals in shelters you really don't want people to know that. >> Yeah, yeah it's not something you want to advertise. >> Because the American public doesn't believe in it. So anyway we worked really hard to collect all this data from across the country and we put it all into this dashboard and it is now a tool where anybody in the public, it's on our website, can look at it and they can see that where we're at from a national level. They can see where they're at from a state level, they can drill down into their community and they can drill down to an individual shelter. >> Wow. >> And the idea behind the dashboard is to really, is to get communities behind helping their shelters. Because as I said earlier, it's going to take us all. >> Right. >> And not only Best Friends and our partners but the public plays a big part of this. >> Right, and so when did that roll out? Do you have any kind of feedback, how's it working? >> It's working wonderfully, we rolled it out at our conference in July. >> So recently, so it's a pretty new initiative. >> Yeah it's just a few weeks old. >> Okay. >> We rolled it out at our national conference and we were all a bit nervous about it, you know especially from a technology perspective. >> Right, right. >> We knew that being the first of it's kind ever in animal welfare that you know it was going to get a lot of publicity both inside and outside the movement. >> (laughs) How you want to say both pro and con. >> Yeah, and it's sitting on our website, well really pro and con. >> Right, right. >> But it's sitting on our website and we're like okay, we don't know what kind of traffic we're going to get, you know what are we going to do about this? So we spent a lot of time with Amazon prior to the launch, you know having them look at our environment and getting advice, discussing it with them. >> Not going to bring down that ISP in Utah. >> No, thank god! (laughs) >> (laughs) >> No it wasn't, thank god we were in the cloud. So Amazon really helped us prepare and then the day of the launch, we knew the time of the launch. So we actually had a war room set up, a virtual war room and we had Amazon employees participating in our war room. We watched the traffic and we did get huge spikes in traffic at all times through the day when certain things were happening. And I'm happy to say from a technology perspective it was a non-event because we did not crash we stayed up, we handled all the traffic, we scaled when we needed to, and we did it you know, virtually at the press of a button. >> Awesome. >> Or the flick of a switch, whatever you want to say. >> That's what you want right? >> Yeah, exactly. >> You just don't want anyone to know, I was like give a good ref, nobody's talking about you you probably did a good job. >> Yeah, exactly yeah. >> Good, so before I let you go so what are some of your initiatives now looking forward. You've got this great partner in AWS, you have basically as much horsepower as you need to get done what you need to get done. What are some of the things that you see, you know kind of next for your roadmap? >> Well, we have a lot. >> Don't give me the whole list (laughs) >> No I'm just going to hit on a few key points. I think, you know we used Amazon initially as our cloud infrastructure but I think the biggest thing we're looking at is platform as a service. There is so much capability out there with predictive analytics, machine learning, artificial intelligence, ARVR, you name it facial recognitions, so we're really investigating those technologies because we think they have you know they could have a huge impact on our movement and really help us achieve life saving. >> Right, right. >> And, I think that, you know we're starting we have our fledgling data science program. We're using the Amazon data lake technology, Athena, Glue, they were just telling me about data lake formation which I just a few minutes ago emailed my data guy and said start looking at data lake formation. >> Right, right. >> So, I mean we're really investing in the platform as a service. The other thing I see is that we're, animal welfare is sort of broken from a technology perspective and a data perspective. In that we have no interoperability and you know we don't have the data available. So lets say you want to adopt a 5-year old animal. Well, you go to a shelter you can't get 5 years of history on a 5 year old animal. So it's really starting to fix the foundation for the movement as a whole, not just Best Friends. So, making sure that you know the veterinary data is there, all the data from the pet ecosystem is there. So we're investigating with AWS they're actually coming to our sanctuary in a couple of months, we're going to do a workshop to figure out how we do this, how we really fix it so that we have interoperability between every shelter when an animal moves from shelter to rescue or whatever so that their data follows them wherever they go. So adopters are fully informed when adopting an animal. >> Because you're in a pretty interesting position, because you're not with any one particular shelter you kind of cross many many boundaries. So you're in a good position to be that aggregator of that data. >> Yeah, I don't know that we want to be the aggregator but we want to lead the movement towards doing that. Just getting the technology players, the shelter management systems, the other people who play a role in technology for animal welfare, getting them in a room and talking and figuring out this problem is huge. >> Right. >> And with a partner like Amazon we feel it can be solved. >> Right. Well Angie thank you for taking a few minutes and sharing your story, really really enjoyed hearing it. >> All right thank you so much. >> All right, she's Angie, I'm Jeff you're watching the CUBE we're at AWS Imagine in Seattle, thanks for watching we'll see you next time. (upbeat music)

>> Announcer: Live, from San Francisco. It's The Cube, covering Oracle Open World 2016. Brought to you by Oracle. Now, here's your host, John Furrier and Peter Burris. >> Hey, welcome back everyone, we are live here in San Francisco. This is SiliconANGLE Media's The Cube. It's our flagship program, we go out to the events and extract the signal from the noise. I'm John Furrier, the CEO of SiliconANGLE Media, joined by co-host Peter Burris all week. Three days of wall-walk of day three. He's the head of research at SiliconeANGLE Media Inc., as well as the general manager of Wikibon research. Our next guest is Roddy Martin, VP of SC Supply Chain Cloud Product Marketing at Oracle. Welcome to The Cube. >> Thank you very much for the opportunity. I look forward to the discussion. >> Thanks for coming on. Really want to hear your thought leadership around the supply chain transformation, because it might be a little bit bumpy depending upon your perspective. But is a huge opportunity going on in every single theater of where software used to be a point solution. The cloud is now an opportunity for customers to think differently, and is a catalyst for essentially a business model change as well as a fundamental data-driven change. Your thoughts on this? What do you see going on? What are the key inflection points? >> So a very interesting part of my background is I came out of the brewing industry in South Africa. and then I led the supply chain practice at AMR Research, which today is Gartner. And we did a lot of studies on, what are companies doing to lead this transformation? Because it's a transformation of the interim business operating model of a company. This is not stitching data together in the traditional supply chain system sense. So one of the very first foundations that is really fundamental, and Gartner has done a great job of carrying the search forward, is the idea that every company progresses to an interim operating model in five stages of capability, and every one of those builds on the other. So they're either reacting in stage one's problem and never saw the shortage coming and ran out of product. Stage two is I performance improve around projects. Stage three is I drive functional excellence. And stage four I start working as an engine outside an operating model. In other words, I'm driving the business from what's happening in the market and I'm making sure that supply is matching demand. So it's very interesting and it's very important to consider that as the base foundation for this whole discussion. >> So that outside is interesting, we've heard this before, a lot of people are going that way, but there's no shortcuts. Can you talk about, cause you talk about the endpoint is then outside-in. >> Right, when you're operating as a demand-driven interim supply channel operating model, you can't run out of supply, right? So if you saw a change happening in the marketplace but there's nothing to supply, you've really just messed up the business. And so, each of these stages builds on every other stage. So functional excellence is: Am I good at planning? Am I good at product management? Am I good at logistics? Because those are the foundations for operating in the interim business model. This is why the Oracle's blanching in the cloud, in fact all of Oracle's developments in the cloud are so important because you're effectively building a new process oriented operating model that spins the entire business. If I started off with ERP systems and then I put logistics in place and tied it together, there's all sorts of disconnects in the business. When you pick it up in cycle times, you pick it in disconnect sometimes, they don't see changes to the marketplace for weeks. So, this overarching end to end supply chain operating model in the cloud is a fundamental enabler. >> So how do you gauge a customer? First of all, I buy everything that you said, but I want to bring up a point, because it seems to me that the theme of Oracle OpenWorld that traditional applications and I won't say, I'll just say the word Silo just to use it as a point, has been a specific domain specific thing. But to be end to end and be outside-in, which is the end game, you have to know how to talk and integrate with other systems which might have been a problem if you built the most badass end to end system. >> That is a part of the challenge and in fact, a lot of companies that I've worked with over the 15 years I've been researching this, they get stuck for that very reason. In other words, this is a re-engineering of the whole IT infrastructure versus having a thousand consultants come in and tie all my data together over a question of four years and move 15 instances of whatever system you want to one. >> So, if I question on the journey thing, you mentioned thousands of consultants, which customers are now seeing. They want faster mile posts, they want to see faster agility but a lot of the customers actually outline the journey for the customer. So they're saying, here's your journey and they shorten the mile posts for the deliverables. But they're the one getting paid for it so is that the right model, should they be outlining the journey for the customer? >> And they are. It's been very interesting because I was a partner with a major global consulting company for four years and I've been mixing with them here, they suddenly recognizing that this path to the cloud is something they've better get on the bandwagon because they're not going to have a thousand consultants deploying whatever ERP system you talk about as the future of IT. So, what's happening is the business is having much more of a say in this fast deployment, fast time to value, putting these new-- >> So they're driving the journey for parameters? >> They are gearing up for this new journey, the consultants are. >> So, let's get to the fundamentals behind all this and ask a question about it. At the end of the day, digital technologies give customers an option to do their journeys very differently whether in a B2B sense or a consumer sense. And as they use digital technologies, they're also giving data up and so we have now a combination where customers are getting something out of digital, they are demanding it as part of the engagement model. They are giving up data along the way, and the technologies for sensing and doing something with that data in business are now, we're not figuring out how that impacts business design, process design, and offering design. >> So, that's stage 4S, what we talk about is people, process, and technology versus, in the past, when you had stage one, two, and three. People as one set of projects, process as another set of projects, and technology as another set of projects. >> Yeah, I may or may not take some middlings with the model you put out, but it does matter. At the end of the day, what is driving this increasingly is that it used to be that the dominant consideration in, I think, and I'm testing you, the dominant consideration was assets. Where is the physical asset, where are the materials, where is the machine, and we'll focus our returns on this things and then presume that there's a demand for it and now we're getting all this data about demand and that is having an impact on how we talk about arranging the assets. >> That is the inside-out to outside-in. So, let me give you an example without mentioning companies. A major retailer and a major pharmaceutical company. They share pollen data, they share weather data, they mine Facebook to find out what are people saying about allergies, let's say in New England. And the ragweed's busting and they say, do we have the right levels of inventory, and they're moving inventory to make sure that people who aren't on Facebook are saying we can't buy this particular product. They're moving inventory, that's the difference. >> So, they're sharing data amongst themselves. >> Yes, and they're collaborating between retailers. >> Arguably a similar example, and a retailer that's actually not moving inventory but moving pointers and offering new channel options so that someone decides may not, that they know somebody's going to come into the store, the size may not be there but they can still get it to them that day. >> So, it's very interesting, Procter and Gamble, who I did a lot of work with, and this is public domain information, the CEO drove two fundamental transformation messages in the business. And they called it the two moments of truth. He said, we will always have our product when we say we've got a product. So, if we promote a new product, the consumer goes to the shelf, it will be there. Moment of truth number two, we understand why consumers choose and use our products. And you don't fix number two until you fix number one because if I wanted a small tube of toothpaste and I went in and there were only big ones, it's the wrong buying signal. So, what you're seeing is that whole flip to measuring what the market's looking for and shaping their demand and then making sure that the assets and the supply system is geared to deliver. >> Right, I want to ask you a question. First of all, I love that point, I love your point about the data, but here's the question: cause supply chain has been very instrumentation drive, okay, and that certainly is transforming but now you mention Procter and Gamble. We are living in an era where, in the history of business, you can actually now potentially measure everything. So how does that impacting the reconfiguration of the business model? I mean, Procter and Gamble has those moments of truth, every company will have a moment of truth which is, everything is now measurable so, advertising to employee things and everything. >> So let's take the asset story versus the on shelf thing, right, so when I have assets and I'm getting all the data out of my assets, what am I doing with all of that data, right? Because it's not connected to demand. What I got to know is what demand data do I really want to be able to move my assets to the right place. >> Peter: By the way, the shelf is an asset. >> Of course it is, yes. It's a sensing point and it's an asset. They own it, they replenish that shelf. So the point is, data is everywhere and now these, the consulting and the BPM organizations supporting and companies doing their own business process manner, they got to know what data is really important and what data from the outside-in is going to allow me to leverage a new operating model for my business and become digital. >> So, this is really awesome, I was talking with an Oracle executive last night at one of their customer parties and we had a conversation around this data sharing. This is a new, different behavior. This is a theme of the show that no one's really talking about but it's in plain sight which is there is a data sharing aspect of systems and vendors and companies. >> Roddy: That's why the cloud is so important. >> John: This is now impacting everything. >> Everything. >> How do companies go forward and do this? What are you seeing, is there a best practice, is there a starting point? Is there a five step process on that? >> Well, first of all, these transformations are being lead by the C level executive team in a business. This is now longer somebody who decides to buy a new IT system and plug it in to the business. So, the business is saying, how do we change the operating model of the way we work, right? So, and then, what are the capabilities, and this is where that five stage model comes in, what capabilities do we need to look at building over the next three years so that we can operate in this intent way because you can't wake up tomorrow and go from an inside-out asset driven business to an outside-in demand driven business in two weeks. It ain't going to happen. >> So what's the progression? What's the progress bar look like when you have that moment of an epiphany and say, you know, I'm the CEO-- >> What's the earning point of the business? If it's Procter and Gamble, I want X number of one billion dollars brands. If you're a pharmaceutical company, you want to launch brand new drugs and you want to do it at half the price and half the speed that you're used to. It's the business articulating, this is why the leadership teams are so fundamental, articulating what's the burning platform and then translating that back into the capabilities-- >> So you get a reverse engineer. >> Outside-In. >> Outside-In, I love it. >> The way our research says it, and it's very similar but I want to test this because it's, we say start with context. >> Yes. >> What are you going to do with your customer that you have to do better than everybody else? And then identify the community that you're going to do it with and identify the capabilities that are going to delight that community. So it's context, community, and capabilities. >> Now here's the context, further piece to context. If context changes, how quickly do I sense that change and how fast can I respond to that change? Because if I've got all my asset capabilities and my supply capabilities locked into one set of context and that changes and I now have to re-engineer my whole business, I may lose the whole show in the process. I got to see those changes as they are happening, literally in real time. This is where the internet of things, this is where demand shaping, demand sensing, retailers collaborating, supplies connected into supply chain, everybody sharing that information and the fact that not many people, they don't know how to do it. The culture of business is not yet at the points-- >> That's why the measurement thing I brought up, I mean Procter and Gamble, they used to say to their agencies, we know that 50% of our advertising is good, we don't know which half. So now they can measure it all just like in every other aspect so this is where the business model-- >> You also have to be careful about whether or not, again going back to context changes, measurements change, data can blow you away. You have to be very smart about how you do it so a lot of these intelligent things, machine learning, how the models get built, how the insides get delivered, all become very very important. Very quickly, I have two quick questions for you. One is really approximate to the conversation, one less so but the approximate one: IOT. IOT is, has many many applications. Certainly turning analogue data into digital data so you can build models is a crucial piece of it. But it also has another implication in how you enact the output of that model back into the real word. How does supply chain and IOT come together? >> So if you look at the studies that are being done by Oracle and Gartner et cetera on what's important to the supply chain, two things come up. One is visibility and the other is analytics. Right, so there's tons of data available, to your point just now. That data could cause massive noise to the business unless you know what you're looking at. I know companies that will say, 95% visibility of changes on their demand side is good enough but I'm good enough on the supply side to be able to adjust. But you got to know which data to look at. So I'm looking at on shelf. I'm looking at what consumers are choosing and using, I'm looking to see what of my contract manufacturers-- >> Peter: Analyze key constraints. >> Bingo, so it's not about, I think what we're all going to have to learn in the internet of things is we need, again, a cloud based internet of things platform that does the analytics. >> Because we can rewire things faster. >> Exactly, you can adjust the business to new scenarios based on what you're reading from the demand side and what you're reading from the supply side. >> So you're a great foil for my second question. My second question is you look back at the history, or the recent history let's call it, of strategy, very asset based, Porter said pick the industry that has the best returns, pick your position in that industry, then choose your games based on the five factor analysis that you want to play to get to that position. Very asset oriented, we're in control, that's going to dictate how things change. What you just suggested was a very very different way of thinking about strategy. >> Same fundamentals. It's the same fundamentals but it's allowing yourself to adjust those fundamentals based on what's happening in the market place. >> Peter: But you're not going to base it on just the assets. >> No, we're not going to base it on the assets unless you've focused on, like if you're an engineering company and that's all you make is machines, you can't suddenly start producing toothpaste, for example. There are, that's why I say it's a reconfiguration of those same principles but flexible enough to meet demand. >> So how does, how does the world of design and the world of strategy start to come together in C suite? >> Fundamentally, because it's the voice of the customer that starts to count. It's the voice of the customer that dictates the strategy. So if my customers don't want green Guinness for Saint Patrick's Day, don't make any, because it's going to hang around and get thrown away, right? So, the voice of the customer determines what's happening on the demand side and the supply side has to be agile enough to meet that need. >> So, I would suggest keep Guinness the way it is because it's damn good the way it is, so personally I would agree on the Guinness comment. No green Guinness. >> So, what's the South Africa beer? >> Castle Lager. Well, SAB, South African Brewery, has been bought by Anheuser-Busch InBrev, a massive big giant. >> We love beer and if there's any beer sponsors out there, we're happy looking for our Budweiser. We want a, maybe an IPA in there. Roddy, thanks for spending the time, coming in with you, appreciate it. Some thought leadership here on Reconfiguration and looking at some of the nuances that are really going to impact the buyers here on The Cube. Oracle Open will be back with more live coverage from SiliconANGLE's The Cube after this short break.