>> Announcer: Please welcome Vice President of Worldwide Marketing, Amazon Web Services, Ariel Kelman. (applause) (techno music) >> Good afternoon, everyone. Thank you for coming. I hope you guys are having a great day here. It is my pleasure to introduce to come up on stage here, the CEO of Amazon Web Services, Andy Jassy. (applause) (techno music) >> Okay. Let's get started. I have a bunch of questions here for you, Andy. >> Just like one of our meetings, Ariel. >> Just like one of our meetings. So, I thought I'd start with a little bit of a state of the state on AWS. Can you give us your quick take? >> Yeah, well, first of all, thank you, everyone, for being here. We really appreciate it. We know how busy you guys are. So, hope you're having a good day. You know, the business is growing really quickly. In the last financials, we released, in Q four of '16, AWS is a 14 billion dollar revenue run rate business, growing 47% year over year. We have millions of active customers, and we consider an active customer as a non-Amazon entity that's used the platform in the last 30 days. And it's really a very broad, diverse customer set, in every imaginable size of customer and every imaginable vertical business segment. And I won't repeat all the customers that I know Werner went through earlier in the keynote, but here are just some of the more recent ones that you've seen, you know NELL is moving their their digital and their connected devices, meters, real estate to AWS. McDonalds is re-inventing their digital platform on top of AWS. FINRA is moving all in to AWS, yeah. You see at Reinvent, Workday announced AWS was its preferred cloud provider, and to start building on top of AWS further. Today, in press releases, you saw both Dunkin Donuts and Here, the geo-spatial map company announced they'd chosen AWS as their provider. You know and then I think if you look at our business, we have a really large non-US or global customer base and business that continues to expand very dramatically. And we're also aggressively increasing the number of geographic regions in which we have infrastructure. So last year in 2016, on top of the broad footprint we had, we added Korea, India, and Canada, and the UK. We've announced that we have regions coming, another one in China, in Ningxia, as well as in France, as well as in Sweden. So we're not close to being done expanding geographically. And then of course, we continue to iterate and innovate really quickly on behalf of all of you, of our customers. I mean, just last year alone, we launched what we considered over 1,000 significant services and features. So on average, our customers wake up every day and have three new capabilities they can choose to use or not use, but at their disposal. You've seen it already this year, if you look at Chime, which is our new unified communication service. It makes meetings much easier to conduct, be productive with. You saw Connect, which is our new global call center routing service. If you look even today, you look at Redshift Spectrum, which makes it easy to query all your data, not just locally on disk in your data warehouse but across all of S3, or DAX, which puts a cash in front of DynamoDB, we use the same interface, or all the new features in our machine learning services. We're not close to being done delivering and iterating on your behalf. And I think if you look at that collection of things, it's part of why, as Gartner looks out at the infrastructure space, they estimate the AWS is several times the size business of the next 14 providers combined. It's a pretty significant market segment leadership position. >> You talked a lot about adopts in there, a lot of customers moving to AWS, migrating large numbers of workloads, some going all in on AWS. And with that as kind of backdrop, do you still see a role for hybrid as being something that's important for customers? >> Yeah, it's funny. The quick answer is yes. I think the, you know, if you think about a few years ago, a lot of the rage was this debate about private cloud versus what people call public cloud. And we don't really see that debate very often anymore. I think relatively few companies have had success with private clouds, and most are pretty substantially moving in the direction of building on top of clouds like AWS. But, while you increasingly see more and more companies every month announcing that they're going all in to the cloud, we will see most enterprises operate in some form of hybrid mode for the next number of years. And I think in the early days of AWS and the cloud, I think people got confused about this, where they thought that they had to make this binary decision to either be all in on the public cloud and AWS or not at all. And of course that's not the case. It's not a binary decision. And what we know many of our enterprise customers want is they want to be able to run the data centers that they're not ready to retire yet as seamlessly as they can alongside of AWS. And it's why we've built a lot of the capabilities we've built the last several years. These are things like PPC, which is our virtual private cloud, which allows you to cordon off a portion of our network, deploy resources into it and connect to it through VPN or Direct Connect, which is a private connection between your data centers and our regions or our storage gateway, which is a virtual storage appliance, or Identity Federation, or a whole bunch of capabilities like that. But what we've seen, even though the vast majority of the big hybrid implementations today are built on top of AWS, as more and more of the mainstream enterprises are now at the point where they're really building substantial cloud adoption plans, they've come back to us and they've said, well, you know, actually you guys have made us make kind of a binary decision. And that's because the vast majority of the world is virtualized on top of VMWare. And because VMWare and AWS, prior to a few months ago, had really done nothing to try and make it easy to use the VMWare tools that people have been using for many years seamlessly with AWS, customers were having to make a binary choice. Either they stick with the VMWare tools they've used for a while but have a really tough time integrating with AWS, or they move to AWS and they have to leave behind the VMWare tools they've been using. And it really was the impetus for VMWare and AWS to have a number of deep conversations about it, which led to the announcement we made late last fall of VMWare and AWS, which is going to allow customers who have been using the VMWare tools to manage their infrastructure for a long time to seamlessly be able to run those on top of AWS. And they get to do so as they move workloads back and forth and they evolve their hybrid implementation without having to buy any new hardware, which is a big deal for companies. Very few companies are looking to find ways to buy more hardware these days. And customers have been very excited about this prospect. We've announced that it's going to be ready in the middle of this year. You see companies like Amadeus and Merck and Western Digital and the state of Louisiana, a number of others, we've a very large, private beta and preview happening right now. And people are pretty excited about that prospect. So we will allow customers to run in the mode that they want to run, and I think you'll see a huge transition over the next five to 10 years. >> So in addition to hybrid, another question we get a lot from enterprises around the concept of lock-in and how they should think about their relationship with the vendor and how they should think about whether to spread the workloads across multiple infrastructure providers. How do you think about that? >> Well, it's a question we get a lot. And Oracle has sure made people care about that issue. You know, I think people are very sensitive about being locked in, given the experience that they've had over the last 10 to 15 years. And I think the reality is when you look at the cloud, it really is nothing like being locked into something like Oracle. The APIs look pretty similar between the various providers. We build an open standard, it's like Linux and MySQL and Postgres. All the migration tools that we build allow you to migrate in or out of AWS. It's up to customers based on how they want to run their workload. So it is much easier to move away from something like the cloud than it is from some of the old software services that has created some of this phobia. But I think when you look at most CIOs, enterprise CIOs particularly, as they think about moving to the cloud, many of them started off thinking that they, you know, very well might split their workloads across multiple cloud providers. And I think when push comes to shove, very few decide to do so. Most predominately pick an infrastructure provider to run their workloads. And the reason that they don't split it across, you know, pretty evenly across clouds is a few reasons. Number one, if you do so, you have to standardize in the lowest common denominator. And these platforms are in radically different stages at this point. And if you look at something like AWS, it has a lot more functionality than anybody else by a large margin. And we're also iterating more quickly than you'll find from the other providers. And most folks don't want to tie the hands of their developers behind their backs in the name of having the ability of splitting it across multiple clouds, cause they actually are, in most of their spaces, competitive, and they have a lot of ideas that they want to actually build and invent on behalf of their customers. So, you know, they don't want to actually limit their functionality. It turns out the second reason is that they don't want to force their development teams to have to learn multiple platforms. And most development teams, if any of you have managed multiple stacks across different technologies, and many of us have had that experience, it's a pain in the butt. And trying to make a shift from what you've been doing for the last 30 years on premises to the cloud is hard enough. But then forcing teams to have to get good at running across two or three platforms is something most teams don't relish, and it's wasteful of people's time, it's wasteful of natural resources. That's the second thing. And then the third reason is that you effectively diminish your buying power because all of these cloud providers have volume discounts, and then you're splitting what you buy across multiple providers, which gives you a lower amount you buy from everybody at a worse price. So when most CIOs and enterprises look at this carefully, they don't actually end up splitting it relatively evenly. They predominately pick a cloud provider. Some will just pick one. Others will pick one and then do a little bit with a second, just so they know they can run with a second provider, in case that relationship with the one they choose to predominately run with goes sideways in some fashion. But when you really look at it, CIOs are not making that decision to split it up relatively evenly because it makes their development teams much less capable and much less agile. >> Okay, let's shift gears a little bit, talk about a subject that's on the minds of not just enterprises but startups and government organizations and pretty much every organization we talk to. And that's AI and machine learning. Reinvent, we introduced our Amazon AI services and just this morning Werner announced the general availability of Amazon Lex. So where are we overall on machine learning? >> Well it's a hugely exciting opportunity for customers, and I think, we believe it's exciting for us as well. And it's still in the relatively early stages, if you look at how people are using it, but it's something that we passionately believe is going to make a huge difference in the world and a huge difference with customers, and that we're investing a pretty gigantic amount of resource and capability for our customers. And I think the way that we think about, at a high level, the machine learning and deep learning spaces are, you know, there's kind of three macro layers of the stack. I think at that bottom layer, it's generally for the expert machine learning practitioners, of which there are relatively few in the world. It's a scarce resource relative to what I think will be the case in five, 10 years from now. And these are folks who are comfortable working with deep learning engines, know how to build models, know how to tune those models, know how to do inference, know how to get that data from the models into production apps. And for that group of people, if you look at the vast majority of machine learning and deep learning that's being done in the cloud today, it's being done on top of AWS, are P2 instances, which are optimized for deep learning and our deep learning AMIs, that package, effectively the deep learning engines and libraries inside those AMIs. And you see companies like Netflix, Nvidia, and Pinterest and Stanford and a whole bunch of others that are doing significant amounts of machine learning on top of those optimized instances for machine learning and the deep learning AMIs. And I think that you can expect, over time, that we'll continue to build additional capabilities and tools for those expert practitioners. I think we will support and do support every single one of the deep learning engines on top of AWS, and we have a significant amount of those workloads with all those engines running on top of AWS today. We also are making, I would say, a disproportionate investment of our own resources and the MXNet community just because if you look at running deep learning models once you get beyond a few GPUs, it's pretty difficult to have those scale as you get into the hundreds of GPUs. And most of the deep learning engines don't scale very well horizontally. And so what we've found through a lot of extensive testing, cause remember, Amazon has thousands of deep learning experts inside the company that have built very sophisticated deep learning capabilities, like the ones you see in Alexa, we have found that MXNet scales the best and almost linearly, as we continue to add nodes, as we continue to horizontally scale. So we have a lot of investment at that bottom layer of the stack. Now, if you think about most companies with developers, it's still largely inaccessible to them to do the type of machine learning and deep learning that they'd really like to do. And that's because the tools, I think, are still too primitive. And there's a number of services out there, we built one ourselves in Amazon Machine Learning that we have a lot of customers use, and yet I would argue that all of those services, including our own, are still more difficult than they should be for everyday developers to be able to build machine learning and access machine learning and deep learning. And if you look at the history of what AWS has done, in every part of our business, and a lot of what's driven us, is trying to democratize technologies that were really only available and accessible before to a select, small number of companies. And so we're doing a lot of work at what I would call that middle layer of the stack to get rid of a lot of the muck associated with having to do, you know, building the models, tuning the models, doing the inference, figuring how to get the data into production apps, a lot of those capabilities at that middle layer that we think are really essential to allow deep learning and machine learning to reach its full potential. And then at the top layer of the stack, we think of those as solutions. And those are things like, pass me an image and I'll tell you what that image is, or show me this face, does it match faces in this group of faces, or pass me a string of text and I'll give you an mpg file, or give me some words and what your intent is and then I'll be able to return answers that allow people to build conversational apps like the Lex technology. And we have a whole bunch of other services coming in that area, atop of Lex and Polly and Recognition, and you can imagine some of those that we've had to use in Amazon over the years that we'll continue to make available for you, our customers. So very significant level of investment at all three layers of that stack. We think it's relatively early days in the space but have a lot of passion and excitement for that. >> Okay, now for ML and AI, we're seeing customers wanting to load in tons of data, both to train the models and to actually process data once they've built their models. And then outside of ML and AI, we're seeing just as much demand to move in data for analytics and traditional workloads. So as people are looking to move more and more data to the cloud, how are we thinking about making it easier to get data in? >> It's a great question. And I think it's actually an often overlooked question because a lot of what gets attention with customers is all the really interesting services that allow you to do everything from compute and storage and database and messaging and analytics and machine learning and AI. But at the end of the day, if you have a significant amount of data already somewhere else, you have to get it into the cloud to be able to take advantage of all these capabilities that you don't have on premises. And so we have spent a disproportionate amount of focus over the last few years trying to build capabilities for our customers to make this easier. And we have a set of capabilities that really is not close to matched anywhere else, in part because we have so many customers who are asking for help in this area that it's, you know, that's really what drives what we build. So of course, you could use the good old-fashioned wire to send data over the internet. Increasingly, we find customers that are trying to move large amounts of data into S3, is using our S3 transfer acceleration service, which basically uses our points of presence, or POPs, all over the world to expedite delivery into S3. You know, a few years ago, we were talking to a number of companies that were looking to make big shifts to the cloud, and they said, well, I need to move lots of data that just isn't viable for me to move it over the wire, given the connection we can assign to it. It's why we built Snowball. And so we launched Snowball a couple years ago, which is really, it's a 50 terabyte appliance that is encrypted, the data's encrypted three different ways, and you ingest the data from your data center into Snowball, it has a Kindle connected to it, it allows you to, you know, that makes sure that you send it to the right place, and you can also track the progress of your high-speed ingestion into our data centers. And when we first launched Snowball, we launched it at Reinvent a couple years ago, I could not believe that we were going to order as many Snowballs to start with as the team wanted to order. And in fact, I reproached the team and I said, this is way too much, why don't we first see if people actually use any of these Snowballs. And so the team thankfully didn't listen very carefully to that, and they really only pared back a little bit. And then it turned out that we, almost from the get-go, had ordered 10X too few. And so this has been something that people have used in a very broad, pervasive way all over the world. And last year, at the beginning of the year, as we were asking people what else they would like us to build in Snowball, customers told us a few things that were pretty interesting to us. First, one that wasn't that surprising was they said, well, it would be great if they were bigger, you know, if instead of 50 terabytes it was more data I could store on each device. Then they said, you know, one of the problems is when I load the data onto a Snowball and send it to you, I have to still keep my local copy on premises until it's ingested, cause I can't risk losing that data. So they said it would be great if you could find a way to provide clustering, so that I don't have to keep that copy on premises. That was pretty interesting. And then they said, you know, there's some of that data that I'd actually like to be loading synchronously to S3, and then, or some things back from S3 to that data that I may want to compare against. That was interesting, having that endpoint. And then they said, well, we'd really love it if there was some compute on those Snowballs so I can do analytics on some relatively short-term signals that I want to take action on right away. Those were really the pieces of feedback that informed Snowball Edge, which is the next version of Snowball that we launched, announced at Reinvent this past November. So it has, it's a hundred-terabyte appliance, still the same level of encryption, and it has clustering so that you don't have to keep that copy of the data local. It allows you to have an endpoint to S3 to synchronously load data back and forth, and then it has a compute inside of it. And so it allows customers to use these on premises. I'll give you a good example. GE is using these for their wind turbines. And they collect all kinds of data from those turbines, but there's certain short-term signals they want to do analytics on in as close to real time as they can, and take action on those. And so they use that compute to do the analytics and then when they fill up that Snowball Edge, they detach it and send it back to AWS to do broad-scale analytics in the cloud and then just start using an additional Snowball Edge to capture that short-term data and be able to do those analytics. So Snowball Edge is, you know, we just launched it a couple months ago, again, amazed at the type of response, how many customers are starting to deploy those all over the place. I think if you have exabytes of data that you need to move, it's not so easy. An exabyte of data, if you wanted to move from on premises to AWS, would require 10,000 Snowball Edges. Those customers don't want to really manage a fleet of 10,000 Snowball Edges if they don't have to. And so, we tried to figure out how to solve that problem, and it's why we launched Snowmobile back at Reinvent in November, which effectively, it's a hundred-petabyte container on a 45-foot trailer that we will take a truck and bring out to your facility. It comes with its own power and its own network fiber that we plug in to your data center. And if you want to move an exabyte of data over a 10 gigabit per second connection, it would take you 26 years. But using 10 Snowmobiles, it would take you six months. So really different level of scale. And you'd be surprised how many companies have exabytes of data at this point that they want to move to the cloud to get all those analytics and machine learning capabilities running on top of them. Then for streaming data, as we have more and more companies that are doing real-time analytics of streaming data, we have Kinesis, where we built something called the Kinesis Firehose that makes it really simple to stream all your real-time data. We have a storage gateway for companies that want to keep certain data hot, locally, and then asynchronously be loading the rest of their data to AWS to be able to use in different formats, should they need it as backup or should they choose to make a transition. So it's a very broad set of storage capabilities. And then of course, if you've moved a lot of data into the cloud or into anything, you realize that one of the hardest parts that people often leave to the end is ETL. And so we have announced an ETL service called Glue, which we announced at Reinvent, which is going to make it much easier to move your data, be able to find your data and map your data to different locations and do ETL, which of course is hugely important as you're moving large amounts. >> So we've talked a lot about moving things to the cloud, moving applications, moving data. But let's shift gears a little bit and talk about something not on the cloud, connected devices. >> Yeah. >> Where do they fit in and how do you think about edge? >> Well, you know, I've been working on AWS since the start of AWS, and we've been in the market for a little over 11 years at this point. And we have encountered, as I'm sure all of you have, many buzzwords. And of all the buzzwords that everybody has talked about, I think I can make a pretty strong argument that the one that has delivered fastest on its promise has been IOT and connected devices. Just amazing to me how much is happening at the edge today and how fast that's changing with device manufacturers. And I think that if you look out 10 years from now, when you talk about hybrid, I think most companies, majority on premise piece of hybrid will not be servers, it will be connected devices. There are going to be billions of devices all over the place, in your home, in your office, in factories, in oil fields, in agricultural fields, on ships, in cars, in planes, everywhere. You're going to have these assets that sit at the edge that companies are going to want to be able to collect data on, do analytics on, and then take action. And if you think about it, most of these devices, by their very nature, have relatively little CPU and have relatively little disk, which makes the cloud disproportionately important for them to supplement them. It's why you see most of the big, successful IOT applications today are using AWS to supplement them. Illumina has hooked up their genome sequencing to AWS to do analytics, or you can look at Major League Baseball Statcast is an IOT application built on top of AWS, or John Deer has over 200,000 telematically enabled tractors that are collecting real-time planting conditions and information that they're doing analytics on and sending it back to farmers so they can figure out where and how to optimally plant. Tata Motors manages their truck fleet this way. Phillips has their smart lighting project. I mean, there're innumerable amounts of these IOT applications built on top of AWS where the cloud is supplementing the device's capability. But when you think about these becoming more mission-critical applications for companies, there are going to be certain functions and certain conditions by which they're not going to want to connect back to the cloud. They're not going to want to take the time for that round trip. They're not going to have connectivity in some cases to be able to make a round trip to the cloud. And what they really want is customers really want the same capabilities they have on AWS, with AWS IOT, but on the devices themselves. And if you've ever tried to develop on these embedded devices, it's not for mere mortals. It's pretty delicate and it's pretty scary and there's a lot of archaic protocols associated with it, pretty tough to do it all and to do it without taking down your application. And so what we did was we built something called Greengrass, and we announced it at Reinvent. And Greengrass is really like a software module that you can effectively have inside your device. And it allows developers to write lambda functions, it's got lambda inside of it, and it allows customers to write lambda functions, some of which they want to run in the cloud, some of which they want to run on the device itself through Greengrass. So they have a common programming model to build those functions, to take the signals they see and take the actions they want to take against that, which is really going to help, I think, across all these IOT devices to be able to be much more flexible and allow the devices and the analytics and the actions you take to be much smarter, more intelligent. It's also why we built Snowball Edge. Snowball Edge, if you think about it, is really a purpose-built Greengrass device. We have Greengrass, it's inside of the Snowball Edge, and you know, the GE wind turbine example is a good example of that. And so it's to us, I think it's the future of what the on-premises piece of hybrid's going to be. I think there're going to be billions of devices all over the place and people are going to want to interact with them with a common programming model like they use in AWS and the cloud, and we're continuing to invest very significantly to make that easier and easier for companies. >> We've talked about several feature directions. We talked about AI, machine learning, the edge. What are some of the other areas of investment that this group should care about? >> Well there's a lot. (laughs) That's not a suit question, Ariel. But there's a lot. I think, I'll name a few. I think first of all, as I alluded to earlier, we are not close to being done expanding geographically. I think virtually every tier-one country will have an AWS region over time. I think many of the emerging countries will as well. I think the database space is an area that is radically changing. It's happening at a faster pace than I think people sometimes realize. And I think it's good news for all of you. I think the database space over the last few decades has been a lonely place for customers. I think that they have felt particularly locked into companies that are expensive and proprietary and have high degrees of lock-in and aren't so customer-friendly. And I think customers are sick of it. And we have a relational database service that we launched many years ago and has many flavors that you can run. You can run MySQL, you can run Postgres, you can run MariaDB, you can run SQLServer, you can run Oracle. And what a lot of our customers kept saying to us was, could you please figure out a way to have a database capability that has the performance characteristics of the commercial-grade databases but the customer-friendly and pricing model of the more open engines like the MySQL and Postgres and MariaDB. What you do on your own, we do a lot of it at Amazon, but it's hard, I mean, it takes a lot of work and a lot of tuning. And our customers really wanted us to solve that problem for them. And it's why we spent several years building Aurora, which is our own database engine that we built, but that's fully compatible with MySQL and with Postgres. It's at least as fault tolerant and durable and performant as the commercial-grade databases, but it's a tenth of the cost of those. And it's also nice because if it turns out that you use Aurora and you decide for whatever reason you don't want to use Aurora anymore, because it's fully compatible with MySQL and Postgres, you just dump it to the community versions of those, and off you are. So there's really hardly any transition there. So that is the fastest-growing service in the history of AWS. I'm amazed at how quickly it's grown. I think you may have heard earlier, we've had 23,000 database migrations just in the last year or so. There's a lot of pent-up demand to have database freedom. And we're here to help you have it. You know, I think on the analytic side, it's just never been easier and less expensive to collect, store, analyze, and share data than it is today. Part of that has to do with the economics of the cloud. But a lot of it has to do with the really broad analytics capability that we provide you. And it's a much broader capability than you'll find elsewhere. And you know, you can manage Hadoop and Spark and Presto and Hive and Pig and Yarn on top of AWS, or we have a managed elastic search service, and you know, of course we have a very high scale, very high performing data warehouse in Redshift, that just got even more performant with Spectrum, which now can query across all of your S3 data, and of course you have Athena, where you can query S3 directly. We have a service that allows you to do real-time analytics of streaming data in Kinesis. We have a business intelligence service in QuickSight. We have a number of machine learning capabilities I talked about earlier. It's a very broad array. And what we find is that it's a new day in analytics for companies. A lot of the data that companies felt like they had to throw away before, either because it was too expensive to hold or they didn't really have the tools accessible to them to get the learning from that data, it's a totally different day today. And so we have a pretty big investment in that space, I mentioned Glue earlier to do ETL on all that data. We have a lot more coming in that space. I think compute, super interesting, you know, I think you will find, I think we will find that companies will use full instances for many, many years and we have, you know, more than double the number of instances than you'll find elsewhere in every imaginable shape and size. But I would also say that the trend we see is that more and more companies are using smaller units of compute, and it's why you see containers becoming so popular. We have a really big business in ECS. And we will continue to build out the capability there. We have companies really running virtually every type of container and orchestration and management service on top of AWS at this point. And then of course, a couple years ago, we pioneered the event-driven serverless capability in compute that we call Lambda, which I'm just again, blown away by how many customers are using that for everything, in every way. So I think the basic unit of compute is continuing to get smaller. I think that's really good for customers. I think the ability to be serverless is a very exciting proposition that we're continuing to to fulfill that vision that we laid out a couple years ago. And then, probably, the last thing I'd point out right now is, I think it's really interesting to see how the basic procurement of software is changing. In significant part driven by what we've been doing with our Marketplace. If you think about it, in the old world, if you were a company that was buying software, you'd have to go find bunch of the companies that you should consider, you'd have to have a lot of conversations, you'd have to talk to a lot of salespeople. Those companies, by the way, have to have a big sales team, an expensive marketing budget to go find those companies and then go sell those companies and then both companies engage in this long tap-dance around doing an agreement and the legal terms and the legal teams and it's just, the process is very arduous. Then after you buy it, you have to figure out how you're going to actually package it, how you're deploy to infrastructure and get it done, and it's just, I think in general, both consumers of software and sellers of software really don't like the process that's existed over the last few decades. And then you look at AWS Marketplace, and we have 35 hundred product listings in there from 12 hundred technology providers. If you look at the number of hours, that software that's been running EC2 just in the last month alone, it's several hundred million hours, EC2 hours, of that software being run on top of our Marketplace. And it's just completely changing how software is bought and procured. I think that if you talk to a lot of the big sellers of software, like Splunk or Trend Micro, there's a whole number of them, they'll tell you it totally changes their ability to be able to sell. You know, one of the things that really helped AWS in the early days and still continues to help us, is that we have a self-service model where we don't actually have to have a lot of people talk to every customer to get started. I think if you're a seller of software, that's very appealing, to allow people to find your software and be able to buy it. And if you're a consumer, to be able to buy it quickly, again, without the hassle of all those conversations and the overhead associated with that, very appealing. And I think it's why the marketplace has just exploded and taken off like it has. It's also really good, by the way, for systems integrators, who are often packaging things on top of that software to their clients. This makes it much easier to build kind of smaller catalogs of software products for their customers. I think when you layer on top of that the capabilities that we've announced to make it easier for SASS providers to meter and to do billing and to do identity is just, it's a very different world. And so I think that also is very exciting, both for companies and customers as well as software providers. >> We certainly touched on a lot here. And we have a lot going on, and you know, while we have customers asking us a lot about how they can use all these new services and new features, we also tend to get a lot of questions from customers on how we innovate so quickly, and they can think about applying some of those lessons learned to their own businesses. >> So you're asking how we're able to innovate quickly? >> Mmm hmm. >> I think there's a few things that have helped us, and it's different for every company. But some of these might be helpful. I'll point to a few. I think the first thing is, I think we disproportionately index on hiring builders. And we think of builders as people who are inventors, people who look at different customer experiences really critically, are honest about what's flawed about them, and then seek to reinvent them. And then people who understand that launch is the starting line and not the finish line. There's very little that any of us ever built that's a home run right out of the gate. And so most things that succeed take a lot of listening to customers and a lot of experimentation and a lot of iterating before you get to an equation that really works. So the first thing is who we hire. I think the second thing is how we organize. And we have, at Amazon, long tried to organize into as small and separable and autonomous teams as we can, that have all the resources in those teams to own their own destiny. And so for instance, the technologists and the product managers are part of the same team. And a lot of that is because we don't want the finger pointing that goes back and forth between the teams, and if they're on the same team, they focus all their energy on owning it together and understanding what customers need from them, spending a disproportionate amount of time with customers, and then they get to own their own roadmaps. One of the reasons we don't publish a 12 to 18 month roadmap is we want those teams to have the freedom, in talking to customers and listening to what you tell us matters, to re-prioritize if there are certain things that we assumed mattered more than it turns out it does. So, you know I think that the way that we organize is the second piece. I think a third piece is all of our teams get to use the same AWS building blocks that all of you get to use, which allow you to move much more quickly. And I think one of the least told stories about Amazon over the last five years, in part because people have gotten interested in AWS, is people have missed how fast our consumer business at Amazon has iterated. Look at the amount of invention in Amazon's consumer business. And they'll tell you that a big piece of that is their ability to use the AWS building blocks like they do. I think a fourth thing is many big companies, as they get larger, what starts to happen is what people call the institutional no, which is that leaders walk into meetings on new ideas looking to find ways to say no, and not because they're ill intended but just because they get more conservative or they have a lot on their plate or things are really managed very centrally, so it's hard to imagine adding more to what you're already doing. At Amazon, it's really the opposite, and in part because of the way we're organized in such a decoupled, decentralized fashion, and in part because it's just part of our DNA. When the leaders walk into a meeting, they are looking for ways to say yes. And we don't say yes to everything, we have a lot of proposals. But we say yes to a lot more than I think virtually any other company on the planet. And when we're having conversations with builders who are proposing new ideas, we're in a mode where we're trying to problem-solve with them to get to yes, which I think is really different. And then I think the last thing is that we have mechanisms inside the company that allow us to make fast decisions. And if you want a little bit more detail, you should read our founder and CEO Jeff Bezos's shareholder letter, which just was released. He talks about the fast decision-making that happens inside the company. It's really true. We make fast decisions and we're willing to fail. And you know, we sometimes talk about how we're working on several of our next biggest failures, and we hope that most of the things we're doing aren't going to fail, but we know, if you're going to push the envelope and if you're going to experiment at the rate that we're trying to experiment, to find more pillars that allow us to do more for customers and allow us to be more relevant, you are going to fail sometimes. And you have to accept that, and you have to have a way of evaluating people that recognizes the inputs, meaning the things that they actually delivered as opposed to the outputs, cause on new ventures, you don't know what the outputs are going to be, you don't know consumers or customers are going to respond to the new thing you're trying to build. So you have to be able to reward employees on the inputs, you have to have a way for them to continue to progress and grow in their career even if they work on something didn't work. And you have to have a way of thinking about, when things don't work, how do I take the technology that I built as part of that, that really actually does work, but I didn't get it right in the form factor, and use it for other things. And I think that when you think about a culture like Amazon, that disproportionately hires builders, organizes into these separable, autonomous teams, and allows them to use building blocks to move fast, and has a leadership team that's looking to say yes to ideas and is willing to fail, you end up finding not only do you do more inventing but you get the people at every level of the organization spending their free cycles thinking about new ideas because it actually pays to think of new ideas cause you get a shot to try it. And so that has really helped us and I think most of our customers who have made significant shifts to AWS and the cloud would argue that that's one of the big transformational things they've seen in their companies as well. >> Okay. I want to go a little bit deeper on the subject of culture. What are some of the things that are most unique about the AWS culture that companies should know about when they're looking to partner with us? >> Well, I think if you're making a decision on a predominant infrastructure provider, it's really important that you decide that the culture of the company you're going to partner with is a fit for yours. And you know, it's a super important decision that you don't want to have to redo multiple times cause it's wasted effort. And I think that, look, I've been at Amazon for almost 20 years at this point, so I have obviously drank the Kool Aid. But there are a few things that I think are truly unique about Amazon's culture. I'll talk about three of them. The first is I think that we are unusually customer-oriented. And I think a lot of companies talk about being customer-oriented, but few actually are. I think most of the big technology companies truthfully are competitor-focused. They kind of look at what competitors are doing and then they try to one-up one another. You have one or two of them that I would say are product-focused, where they say, hey, it's great, you Mr. and Mrs. Customer have ideas on a product, but leave that to the experts, and you know, you'll like the products we're going to build. And those strategies can be good ones and successful ones, they're just not ours. We are driven by what customers tell us matters to them. We don't build technology for technology's sake, we don't become, you know, smitten by any one technology. We're trying to solve real problems for our customers. 90% of what we build is driven by what you tell us matters. And the other 10% is listening to you, and even if you can't articulate exactly what you want, trying to read between the lines and invent on your behalf. So that's the first thing. Second thing is that we are pioneers. We really like to invent, as I was talking about earlier. And I think most big technology companies at this point have either lost their will or their DNA to invent. Most of them acquire it or fast follow. And again, that can be a successful strategy. It's just not ours. I think in this day and age, where we're going through as big a shift as we are in the cloud, which is the biggest technology shift in our lifetime, as dynamic as it is, being able to partner with a company that has the most functionality, it's iterating the fastest, has the most customers, has the largest ecosystem of partners, has SIs and ISPs, that has had a vision for how all these pieces fit together from the start, instead of trying to patch them together in a following act, you have a big advantage. I think that the third thing is that we're unusually long-term oriented. And I think that you won't ever see us show up at your door the last day of a quarter, the last day of a year, trying to harass you into doing some kind of deal with us, not to be heard from again for a couple years when we either audit you or try to re-up you for a deal. That's just not the way that we will ever operate. We are trying to build a business, a set of relationships, that will outlast all of us here. And I think something that always ties it together well is this trusted advisor capability that we have inside our support function, which is, you know, we look at dozens of programmatic ways that our customers are using the platform and reach out to you if you're doing something we think's suboptimal. And one of the things we do is if you're not fully utilizing resources, or hardly, or not using them at all, we'll reach out and say, hey, you should stop paying for this. And over the last couple of years, we've sent out a couple million of these notifications that have led to actual annualized savings for customers of 350 million dollars. So I ask you, how many of your technology partners reach out to you and say stop spending money with us? To the tune of 350 million dollars lost revenue per year. Not too many. And I think when we first started doing it, people though it was gimmicky, but if you understand what I just talked about with regard to our culture, it makes perfect sense. We don't want to make money from customers unless you're getting value. We want to reinvent an experience that we think has been broken for the prior few decades. And then we're trying to build a relationship with you that outlasts all of us, and we think the best way to do that is to provide value and do right by customers over a long period of time. >> Okay, keeping going on the culture subject, what about some of the quirky things about Amazon's culture that people might find interesting or useful? >> Well there are a lot of quirky parts to our culture. And I think any, you know lots of companies who have strong culture will argue they have quirky pieces but I think there's a few I might point to. You know, I think the first would be the first several years I was with the company, I guess the first six years or so I was at the company, like most companies, all the information that was presented was via PowerPoint. And we would find that it was a very inefficient way to consume information. You know, you were often shaded by the charisma of the presenter, sometimes you would overweight what the presenters said based on whether they were a good presenter. And vice versa. You would very rarely have a deep conversation, cause you have no room on PowerPoint slides to have any depth. You would interrupt the presenter constantly with questions that they hadn't really thought through cause they didn't think they were going to have to present that level of depth. You constantly have the, you know, you'd ask the question, oh, I'm going to get to that in five slides, you want to do that now or you want to do that in five slides, you know, it was just maddening. And we would often find that most of the meetings required multiple meetings. And so we made a decision as a company to effectively ban PowerPoints as a communication vehicle inside the company. Really the only time I do PowerPoints is at Reinvent. And maybe that shows. And what we found is that it's a much more substantive and effective and time-efficient way to have conversations because there is no way to fake depth in a six-page narrative. So what we went to from PowerPoint was six-page narrative. You can write, have as much as you want in the appendix, but you have to assume nobody will read the appendices. Everything you have to communicate has to be done in six pages. You can't fake depth in a six-page narrative. And so what we do is we all get to the room, we spend 20 minutes or so reading the document so it's fresh in everybody's head. And then where we start the conversation is a radically different spot than when you're hearing a presentation one kind of shallow slide at a time. We all start the conversation with a fair bit of depth on the topic, and we can really hone in on the three or four issues that typically matter in each of these conversations. So we get to the heart of the matter and we can have one meeting on the topic instead of three or four. So that has been really, I mean it's unusual and it takes some time getting used to but it is a much more effective way to pay attention to the detail and have a substantive conversation. You know, I think a second thing, if you look at our working backwards process, we don't write a lot of code for any of our services until we write and refine and decide we have crisp press release and frequently asked question, or FAQ, for that product. And in the press release, what we're trying to do is make sure that we're building a product that has benefits that will really matter. How many times have we all gotten to the end of products and by the time we get there, we kind of think about what we're launching and think, this is not that interesting. Like, people are not going to find this that compelling. And it's because you just haven't thought through and argued and debated and made sure that you drew the line in the right spot on a set of benefits that will really matter to customers. So that's why we use the press release. The FAQ is to really have the arguments up front about how you're building the product. So what technology are you using? What's the architecture? What's the customer experience? What's the UI look like? What's the pricing dimensions? Are you going to charge for it or not? All of those decisions, what are people going to be most excited about, what are people going to be most disappointed by. All those conversations, if you have them up front, even if it takes you a few times to go through it, you can just let the teams build, and you don't have to check in with them except on the dates. And so we find that if we take the time up front we not only get the products right more often but the teams also deliver much more quickly and with much less churn. And then the third thing I'd say that's kind of quirky is it is an unusually truth-seeking culture at Amazon. I think we have a leadership principle that we say have backbone, disagree, and commit. And what it means is that we really expect people to speak up if they believe that we're headed down a path that's wrong for customers, no matter who is advancing it, what level in the company, everybody is empowered and expected to speak up. And then once we have the debate, then we all have to pull the same way, even if it's a different way than you were advocating. And I think, you always hear the old adage of where, two people look at a ceiling and one person says it's 14 feet and the other person says, it's 10 feet, and they say, okay let's compromise, it's 12 feet. And of course, it's not 12 feet, there is an answer. And not all things that we all consider has that black and white answer, but most things have an answer that really is more right if you actually assess it and debate it. And so we have an environment that really empowers people to challenge one another and I think it's part of why we end up getting to better answers, cause we have that level of openness and rigor. >> Okay, well Andy, we have time for one more question. >> Okay. >> So other than some of the things you've talked about, like customer focus, innovation, and long-term orientation, what is the single most important lesson that you've learned that is really relevant to this audience and this time we're living in? >> There's a lot. But I'll pick one. I would say I'll tell a short story that I think captures it. In the early days at Amazon, our sole business was what we called an owned inventory retail business, which meant we bought the inventory from distributors or publishers or manufacturers, stored it in our own fulfillment centers and shipped it to customers. And around the year 1999 or 2000, this third party seller model started becoming very popular. You know, these were companies like Half.com and eBay and folks like that. And we had a really animated debate inside the company about whether we should allow third party sellers to sell on the Amazon site. And the concerns internally were, first of all, we just had this fundamental belief that other sellers weren't going to care as much about the customer experience as we did cause it was such a central part of everything we did DNA-wise. And then also we had this entire business and all this machinery that was built around owned inventory business, with all these relationships with publishers and distributors and manufacturers, who we didn't think would necessarily like third party sellers selling right alongside us having bought their products. And so we really debated this, and we ultimately decided that we were going to allow third party sellers to sell in our marketplace. And we made that decision in part because it was better for customers, it allowed them to have lower prices, so more price variety and better selection. But also in significant part because we realized you can't fight gravity. If something is going to happen, whether you want it to happen or not, it is going to happen. And you are much better off cannibalizing yourself or being ahead of whatever direction the world is headed than you are at howling at the wind or wishing it away or trying to put up blockers and find a way to delay moving to the model that is really most successful and has the most amount of benefits for the customers in question. And that turned out to be a really important lesson for Amazon as a company and for me, personally, as well. You know, in the early days of doing Marketplace, we had all kinds of folks, even after we made the decision, that despite the have backbone, disagree and commit weren't really sure that they believed that it was going to be a successful decision. And it took several months, but thankfully we really were vigilant about it, and today in roughly half of the units we sell in our retail business are third party seller units. Been really good for our customers. And really good for our business as well. And I think the same thing is really applicable to the space we're talking about today, to the cloud, as you think about this gigantic shift that's going on right now, moving to the cloud, which is, you know, I think in the early days of the cloud, the first, I'll call it six, seven, eight years, I think collectively we consumed so much energy with all these arguments about are people going to move to the cloud, what are they going to move to the cloud, will they move mission-critical applications to the cloud, will the enterprise adopt it, will public sector adopt it, what about private cloud, you know, we just consumed a huge amount of energy and it was, you can see both in the results in what's happening in businesses like ours, it was a form of fighting gravity. And today we don't really have if conversations anymore with our customers. They're all when and how and what order conversations. And I would say that this going to be a much better world for all of us, because we will be able to build in a much more cost effective fashion, we will be able to build much more quickly, we'll be able to take our scarce resource of engineers and not spend their resource on the undifferentiated heavy lifting of infrastructure and instead on what truly differentiates your business. And you'll have a global presence, so that you have lower latency and a better end user customer experience being deployed with your applications and infrastructure all over the world. And you'll be able to meet the data sovereignty requirements of various locales. So I think it's a great world that we're entering right now, I think we're at a time where there's a lot less confusion about where the world is headed, and I think it's an unprecedented opportunity for you to reinvent your businesses, reinvent your applications, and build capabilities for your customers and for your business that weren't easily possible before. And I hope you take advantage of it, and we'll be right here every step of the way to help you. Thank you very much. I appreciate it. (applause) >> Thank you, Andy. And thank you, everyone. I appreciate your time today. >> Thank you. (applause) (upbeat music)

>> Voiceover: Live from San Francisco, it's the CUBE covering AWS Summit 2017. Brought to you by Amazon Web Services. (electronic music) >> Welcome back to the CUBE. We have spent a great day in San Francisco at the AWS Summit. My co-host, George Gilbert, and I are very excited next to be talking to Eric Pan, the Senior Director of Alliances Marketing at Equinix. And Eric and I know each other when I worked at NetUP and you worked at VMware, so it's great to see you again. >> Back in the day. >> Back in the day. >> Eric: Yeah. It's great to be here, Lisa. >> It's great to have you on the CUBE. >> Eric: Thank you. >> So tell us about Equinix and what you're doing to help customers get to the cloud. >> Yes, love to. So Equinix was founded in 199-- ... 1998. We really have established what we call an interconnection data center platform. So Platform Equinix is a company that helps customers to interconnect with their trading partners, with networks, and customers. >> Excellent. And so one of the things that I actually just read yesterday, a press release, that Equinix just became part of the AWS partner network as an advanced technology partner. >> Eric: Right. >> Big news. >> Big news. So we've had a relationship with AWS for many years. We've established 14 points of presence around the world for what AWS calls their Direct Connect, which is, it's a great way for customers to be able to manage their hybrid clouds or mainline, if you will, directly into AWS, privately and bypassing the Internet entirely. So for us to be able to gain this certification, this badge if you will, it's a proud day at Equinix. >> Well, congratulations. Fantastic. I'm sure a lot of hard work has gone into that. >> Eric: Yes. >> So help us, talk though from a customer perspective, where they want to say, "I don't really want to apply any more of my real estate, and I, you know, I don't want to buy a lot more gear, but I have some stuff with legacy apps. And I'm actually starting to build out more in Amazon." What's that scenario? How do you help with that scenario? >> Right, so this is a very typical scenario we see every day with our customers. If I may just color this with what we call interconnection, Interconnection is, it is a set of ideas and concepts that we've established through many years of observing how our customers have worked with us and have built their infrastructure, both on-premises and into the cloud. So what you're referring to is really a hybrid cloud situation or scenario. And where a customer ideally says, "I would like to put the majority of my workloads and applications and maybe even data up in the cloud." But we know that's not practical. There's a lot of different reasons. Some of the reasons are data sovereignty or compliance or regulatory concerns. We see a lot of customers that have very specific hardware devices. For hardware maybe, certification or validation for certain things. So those sort of customers will come to Equinix. They'll place their own equipment within our data center. They'll manage that or they'll have a managed service provider come and help them with that. But they'll also be able to directly connect up into AWS. So that's one of the beauties of working with Equinix from our customers' perspective, is they get the best of both worlds. So they get to move their equipment out of their own data center, but they still have the look-and-feel or the management capability of on-premises. And then they also get to enjoy all the benefits of working in the cloud with AWS. >> So you've grown since early 2016, as we were chatting about before, Equinix has grown customer connections to AWS >> Eric: Yes, 250. >> 250% That's massive. >> Eric: Over 250%, yes. >> Over 250. Tell me just to get a little bit, kind of following on what you were just saying, what type of business would choose that route versus going, either keeping some on-prem then going right right to AWS or a cloud? Give us an understanding of really who this target market is. >> Sure, so really any and all enterprises would need to have this capability. The concept here with Direct Connect, it's really AWS' concept and where they say, "If you have certain applications that may be really heavy and are very compute-intensive or very data-intensive, you'll want to run those applications in AWS, and you want to make sure that you have good user experience around that." So Direct Connect privately connects from the end-user to AWS without zig-zagging through the Internet. You get predictability and performance. And what's really the most important thing is great user experience. >> And are you seeing the rise of enterprise as being more and more comfortable with migrating business-critical workloads? >> Oh absolutely yes. Yes, I went to Andy Jassy's fireside chat earlier today >> Lisa: Yeah, it was fantastic wasn't it? >> And he had a whole list of customers that are running business-critical applications. So we see a lot of customers that do that. And we also see, on the flip-side, a lot of customers, like what we were speaking about earlier in the hybrid cloud sense, that are running business-critical applications in AWS but they need to have their data local. So marked by regulatory or compliance issues in health care or in retail environments where PCI compliance demands that you have private data. And then in countries like, I'm just going to give you two examples, Canada and Germany, they have very stringent data sovereignty rules where you must have data in-country from operating on that data. So a lot of customers will use Direct Connect to connect up into AWS, but they'll also be able to maintain their data privacy if they need to. >> Just a drill down on that scenario, you know, there's debate as to, is there one cloud, one ring to rule them all? Or where is the sweet spots of different clouds? Would Equinix be for a customer who has a mission-critical application that's been running for years, that's got an Oracle database? They want to add some low-latency analytics, machine learning where they're scoring or predicting. So they want to put something close to where it's running. So they take the equipment from their data center, put it in Equinix, add around that application the low-latency stuff. >> Eric: Yes. >> And then maybe the digital experience part is in Amazon. >> Right. Yes. So we see many customers doing that very thing. And we also have a very close relationship with NetApp as a storage provider. And NetApp has an offering called NPS, or NetApp Private Storage. So symbiotically, we work together to provide what NetApp has as a ... Data Fabric, which they call. And in that scenario, the whole entire concept is based on running heavy applications or business applications in the cloud but having your data privately and distributed locally or close to where people live, work, and play. >> George: Okay. >> So one of the topics, actually, in, you mentioned attending Andy Jassy's fireside chat. I think we all did. It was fantastic. >> And one of the things that was really interesting was that he was talking about of all of the buzzwords, and as marketers, you know, we both know this, that IOT is the buzzword that he has seen really come to fruition. >> Eric: Come to life, right. >> The fastest. >> That was a fascinating part of his discussion. So we, Equinix, are at the center of, if you will, some of the things that are going on in the IOT world. So IOT, if you can imagine the Internet, a thing says that there's lots of different little devices or big devices like cars or huge devices like hydroelectric dams or jet engines. Those are all producing vast amounts of data that have to go somewhere. And the companies that, like Andy used GE for example in the wind turbines, the companies that need to look at that data, that are having to store that data or do something with it, they typically say, "Well, if we are based in one geographical city, and all this data is coming in from all over the world or all over some region, you need to have natural ingestion points for that data. So we, Equinix, are at the center of where data comes in. And then the next piece is, well, now that we have all this data or now that the organization has all this data in one place or maybe distributed in a few places, how do they then go operate on that? So the scenarios that we spoke about earlier, in where you have an application running up in AWS, to look at that data or, in some cases, there may be, like Andy talked about the Snowball and the Edge computing, Edge computing is something that Equinix very much puts forward as one of the concepts in our interconnection ideas. So that, it's kind of loud there. >> Sorry for the overhead announcement (laughs) >> So the idea around having all of these big data ingestion points, having Edge compute or cloud compute, Equinix becomes a really logical place for customers to be able to do all of that. And then, of course, there's all the data visualization. There's all the data analytics that have to occur with the data scientists. So maybe some of those analytics are running in AWS, but maybe some of the visualization pieces are running in other companies. I won't name the companies, but we all know who the data visualization companies are. >> Lisa: (laughs) >> So your points of presence are about 150 if ... >> Yes, we have 150 data centers in 40 of the biggest business-rich metros around the world. >> Now, do you see a need for a mini-data center or a point of presence that's more like when AOL had those dial-in >> Eric: (laughs) >> I mean, literally, it could've been one box that received phone calls and then ran them out over the network. And the reason I ask is when we have billions of devices, you might want points of presence in the thousands or hundreds of thousands even. >> Eric: Right. That is a very interesting question, and I kind of liken this to something that maybe is an easier idea to understand. A lot of us live in big cities. A lot of us work or ... A lot of us, yes, work at a big company. Some of us don't. A lot of us conduct our banking with big banks or small banks. So if you can imagine the world of maybe retail or banking where there's lots of little branch offices, those could be, we could think of those as maybe the mini-data center idea that you've brought up earlier. So in what Equinix calls interconnection, we have a concept that we call Edge Hub or Communications Hub, which is an idea in where we want to shorten the distance between where users live, work, and play and where the application is running. And so by doing that and simplifying the network topology, in the case that we're talking about, IOT, yes. You would definitely want to do that. So think of a branch office connecting up to a hub, if you will, a communications hub, as a natural ingestion point to bring in that data. >> So last question, Eric, as we wrap up here. We talked about the tremendous growth that Equinix has had just in the last not even 18 months alone and also the great news yesterday that you're very proud of and should be, as becoming an advanced technology partner of Amazon. So last word to you, what's next as an advanced technology partner of AWS? >> Wow. Well, if I can just maybe borrow some of Andy Jassy's words, we're not done here yet. There's no end in sight where Equinix goes. We continue to grow. We have over a third of the Fortune 500 customers that we've managed to attract and that are happy customers. We want to continue down that road and have 100% of the Fortune 500 customers. And we want to make all of our customers happy in working in this new era that we call cloud computing. >> Fantastic. Well, I think we can feel the momentum coming from you and very much Matt Schpive, the guys and the gals from AWS that were on stage today. So, Eric Pan, it's so great to see you after a few years of back in the day. >> Great to see you. Thanks for having me here. >> Absolutely, and for Eric Pan and my co-host George Gilbert, I'm Lisa Martin. You've been watching the CUBE live from the Amazon Web Services Summit in San Francisco. We will be right back. (futuristic electronic music)

(upbeat music) >> Announcer: Live from San Francisco, it's theCUBE! Covering AWS Summit 2017. Brought to you by Amazon Web Services. >> Welcome back to theCUBE. We thank you so much for hanging out with us today. We've had an amazing day, Jeff Frick, George Gilbert with me, Lisa Martin. I think guys, first impressions or overall impressions of the day, it started with Werner Vogels very energetic, very passionate keynote. It was almost what can't Amazon do. The amount of services that they're offering, the amount of customer logos validating, presumably, and substantiating all of these services. It was really quite eye opening. >> Yeah. >> I think for me. But also some of the use cases that they've shared were, those that were on main stage, those that were in breakout sessions or here with us, really shows that the culture that they're building, or have built over the last 11 years now at AWS, is really one of experimentation, failure is okay, let's keep moving. Speed, speed, speed and undulate. >> So many, so many great things. I just want to touch on some of the culture, pivot off the cuture. For instance, Andy Jassy and his keynote. And I think the culture is so, so important. But one of the things he talked about is they banned PowerPoint. He said because it wasn't interactive, wasted a lot of time, no one was prepared for a deep dive 'cause they just put the slides together. And they went to this thing he called the six page narrative, which I thought was pretty interesting. And everyone reads the narrative at the beginning of the meeting. So, you know, everyone's busy. >> Before the meeting. >> Yeah. >> Oh. >> But I think at the beginning of the meeting. So everyone's had a common point >> Right. >> 'Cause let's face it, everyone's busy, no one really preps as much as they should before the meeting. So now, they force it with a 20 minute read the narrative. And so everyone is at the same kind of depth of knowledge. I thought that was really powerful. And then, to write the press release and the FAQs- >> That was phenomenal! >> Before you write the line of codes. >> Yes! >> So what are the issues that people are going to raise and what's the really exciting value that you're delivering to the market that you defined in a press release. >> Yeah. >> Lisa: Yeah. >> You know, I think it's great stuff. >> That was as interesting, I thought, as any of the product releases. >> I agree, yeah. >> 'Cause it almost told us how they keep the wheel spinning so fast. >> Right >> Exactly! But that is really culturally different than I think a lot of the companies that we talked to who, when you get to a six dot one dot two press release. >> Yeah. Is it really that interesting? >> Right, right. >> So that was really, really revolutionary. And I think speaks to your point, you know, how have they been able to build this dominance this quickly and not let their competitors gain on what they project as a six or seven year advantage. >> Like he said though, 'cause they don't look at the competitors. They just keep movin', right. And they didn't have the kind of the legacy thing holding them back. You know, Clayton Christensen, innovator's dilemma. They just kept moving forward. But I thought the other really insightful thing that came out of his fireside chat was the conversation around third-party sales when they were still just Amazon. And do they let other people sell on their platform. And he said "You can't fight gravity". So, it goes back, it reminds me of like when Schwab went to $19 trades. Dave Pottruck tells the story of online trading. And they were giving up these expensive commissions but he basically said "If I don't kill my own business, somebody else is going to do it for me. So I better be the one that kills it and at least try to take advantage of that next wave". Really powerful concepts. >> But there's an analog to the "fulfilled by Amazon", which is where the third-parties went. Where they sort of, essentially, took the eBay model and said "We're going to essentially make our fulfillment platform, and commerce platform, stronger because we're going to take all of those other third-parties". And then what they did with Amazon, AWS, was take the whole commerce platform. >> Right. >> And open it up for other people 'cause that made it more powerful for them. And there's still more to come. What they really didn't talk about. They talked a lot about AI, and mostly at the framework and tools levels. And where framework levels would be for, you know, world-class scientists and the tools would be for data scientists. But when they talked about the image recognition, the voice recognition, and text-to-speech, things like that, they were saying then they're leveraging the Amazon data and training those models so that mere mortal developers can do that. What he didn't say, and when we had their product marketing guy here, what he didn't want to say was there's whole lot of other areas where Amazon the commerce company, the retail company, has data that no other cloud has that they can offer. Not to think about really the machine learning as tools again, but as semi-finished applications. >> Right. >> And I think that's going to be pretty profound differentiated versus other clouds. >> Right. And just the basic scale, right. The slide that Werner showed, not only with all the customers and partners of this, but just the breadth of services and the way they keep adding more based on whatever your special function is. I need High I/O, I need ML, I need really cheap cold storage, I need whatever. They can apply the scale to all those kind of sub-segments and offer a breadth at scale that, you know, pretty tough to compete against. >> Absolutely! And they continue to innovate. And Andy's fireside chat, he was really kind of talking about why and how they're able to do that. Being customer focused, not having to look at the competition, is a major advantage. And one of the themes I also heard and felt today was you think back 11 years ago to their genesis, they were very much focused on the start-up community, the developers, really won long ago the hearts and the minds of those developers. Because they were the ones that would try and innovate, and fail, and try again. >> Right. >> But as the code becomes, in I think Werner's words this morning, "the new normal", they've done a very good job of continuing to foster and enable developers within start-ups and those entrepreneurs who want to start SaaS companies. >> Right. >> All the way up to the enterprise. As we see the dynamic and buying software change dramatically, thinking about the Amazon marketplace as a great example, we are now seeing the C-suite being mandated sometimes by the board. You've got to move more applications into the cloud. Well how do I do that? >> Right. >> So it's developers, it's lines of business like the marketing folks, or the sales folks that Shadow IT say "We need to do this. You can help us move fast enough". All the way up to the C-suite and the board. And they've done a great job of expanding the conversation. >> Jeff: Right. >> Expanding the services to really target multiple audiences and meet a lot of pain points. >> You know, there was a press briefing, pre-brief for the announcement of the marketplace expansion yesterday. And what came out really interesting was, you know, when you go to the Amazon marketplace homepage and there's dozens of categories and about, I think it's 35 hundred actual products from third-parties and 12 hundred vendors. And, you know, you can't go to an enterprise, you can't go to JP Morgan and say "here, you know, go to town". But what IBM does with sort of their own rich library of stuff is they have their global business services and their industry solutions development groups. They take the piece parts and put solutions together for their customers. But what Amazon is now in a position to do is they have solution architects working either for them, who are billing out at maybe two- or three-hundred thousand a year, or who are working for VARS who turned into manage service providers who configure these solutions. And so, what looks like a self-service marketplace now can serve, you know, a bank with a hundred billion in assets or a trillion in assets because there's now the IBM equivalent of a system integrator who can put the pieces together. And who can run them for you if you don't want to. >> Right, and have the aggregated data of everybody else runnin' those services. So for best practices and stuff, you're leveraging the whole ecosystem, not a single instance at a single company. And that is so big! >> And that was actually, that was one of the themes of our last guest. From Datadog. Which is, they can watch so much of what's going on. Not just a customer's workload, but maybe they're not doing it now but they will be able to do it in the future, where they can look across workloads and identify best practices in configurations and things like that. >> Right. >> And then you send that back to the customer and they pay for that advice. >> Right. It's just interesting, you know, three years ago the conversation was all about security around public cloud and, you know, we're done with that conversation. Especially since most security breeches are people lose their laptops, right. It's an employee, or a disgruntled employee. But the thing that's interesting to me on this start-up and rent versus own is, again, the answer to every question in a Cube interview. Why do you want to do the undifferentiated heavy lifting of managing infrastructure? Those guys, ThinkLogic, still like 14 people and a couple of dozen developers that are attacking the IOT space. They would never even get an approved vendor status at somebody like Boeing or GE. They would never even get to the procurement issue. But now, as part of this marketplace, you know, they can come in either as a partner, part of a solution, an adjunct, part of an SI, or as a standalone app that you still buy through your approved vendor process with AWS. Why would you go anywhere else? >> Right. That was a great point that you brought up a number of times today. Showing, not only how Amazon is innovating internally and to enable the start-ups to the enterprises from a public cloud perspective, but they're also enabling businesses to be born that would never have gotten off the ground. >> Jeff: Right, right. And, to your point, it's very valid about even becoming an approved vendor for a company the size of ThinkLogics, they would never have been able to do that. So, it's really exciting I think overall, I think we'd all summarize the day as a very positive, very enlightening. I think, for me, I was really excited to hear what was going to be going on for IOT and Hybrid. Heard some interesting things there today, so I think that's just a dot dot dot to be continued. >> Yes. >> I think overall, really strong announcements from them. The passion was there. Culturally, I think they really reap what they sow and I think that was reflected in the conversations that we were able to have today. >> One thing I want to ask you about, George, you're a smart guy. Speed of light's too damn slow. >> So you think so. (laughter) >> The speed of light's too damn slow. >> Right. >> Jeff: Hear it over and over and over again. >> Yes! >> And still, cloud-based, soft underbelly of cloud, you got to be connected. Do you think that the speed of light issues with Edge and shifting resources, co-locating storage compute in the data. You see any really big hurdles that are just really scary? >> Like, following on the "dot dot dot", computing always follows a pendulum. Centralization, decentralization. No side ever goes away, it's just a change in emphasis. And we're going to see some analysis have to move to the edge because for the speed of light, you know, your smart car, you know, it doesn't have time to say "That looks like an old lady who's actually in the crosswalk, you know, I'm going to go back to the cloud and ask whether I should plow through her or, you know, the car next to me". You know, that needs a low-latency analytic. >> Jeff: Right, right. >> But at the same time, and one of our guests was talking about it, if you're looking at the pressure at valve at, you know, a thousand-mile pipeline, you probably don't need to react instantaneously. You send that back to the cloud and it'll look at it over, you know, a period of time and say "This one's looking like it's going to leak". >> Right. Anomalies. >> So, different scenarios. >> Okay. >> And, unfortunately, we are going to have to say "dot dot dot". We talked all day about this! Jeff Frig, thank you so much! George Gilbert, what a fantastic day we've had here at the AWS Summit in San Francisco. We thank you for joining! You can follow all of the replays here on siliconangle.tv. And Jeff, what do we got comin' up next week? We're at several events. NAB next week. >> NAB, Oracal, Modern Customer Experience, and you're doing a red carpet, I guess a green carpet award show. >> A green carpet award show at the Computer History Museum next week. So stay tuned, stick around on siliconangle.tv to find out all the things we're doing. It's going to be a exciting spring. Again, thanks for joining. See you next time. (light, upbeat music)

>> Voiceover: Live from San Francisco, it's The Cube. Covering AWS Summit 2017. Brought to you by Amazon Web Services. >> Hi, welcome back to The Cube. We are live in San Francisco at the AWS Summit. We've had a great day so far. I'm Lisa Martin here with my co-host George Gilbert. We are very excited to be joined by Datadog. K Young the Director of Strategic Alliances from Datadog, welcome to The Cube. >> Thank you, hi. Glad to be here. >> So, tell us, besides loving your shirt, as I've already told you, tell us and our viewers a little bit about who Datadog is and what do you do. >> Alright, so Datadog does infrastructure monitoring and application performance monitoring. So what that means is we're able to not only look at your hosts and the resources they have available to them, meaning CPU and memory and that sort of thing, but also all the software that's running on top of it. So, if it's off the shelf software, like a database, like Postgres, or maybe it's EngineX, we understand over 200 different off-the-shelf types of software, integrate with them directly so all you have to do is turn on those integrations, and we can tell you whether those pieces of software are performing at the rate that they ought to, with a sufficiently low number of errors. That's the infrastructure monitoring side of things. Then application performance monitoring, is where you can actually trace execution of requests, individual requests, across different services, or microservices, and tell where time is being spent and track metadata so that in a forensic case, you can go back and determine, oh this type of call is producing a lot of errors. Oh, and those errors are coming from here, and then, you know, maybe a lot of time is being spent here, and then because Datadog also does infrastructure monitoring, drill down into, okay well, what's happening under the hood? Maybe we're having problems because our infrastructure itself is misbehaving in some way. >> You have some pretty big customers: Salesforce, Airbnb, Samsung. I was just reading yesterday, an article that was published, that you've been, Datadog, in the top five businesses profiled by IDC as the multi-cloud management vendors to look out for. So, some pretty big accolades, some pretty big customers. How long have you been in business? >> K Young: Since 2010. >> Lisa: 2010. And tell us about what you're doing with Amazon. >> What we're doing with Amazon. So, let's see, where to begin. Amazon, a lot of people come to Datadog when they have complex systems to manage, meaning highly dynamic, or high scale, or they've adopted Docker, and their infrastructure is changing frequently. More frequently than infrastructure used to change ten years ago. Because Datadog makes it easy or ... Easy, possible even, to make sense of what's happening, even as your infrastructure changes on an hourly basis. So, a lot of customers come to us around the time they're interested in using dynamic infrastructure. Sometimes that's on Amazon, and sometimes that's when you're On-Prem but you're adopting Docker, for example, or microservices. We get a lot of business on Amazon. I think it's fair to say Amazon loves us, because it makes it so much easier to use their service and to adopt their service. And we're sort of the defacto infrastructure monitoring service for Amazon. >> So, you talking about containers, microservices, hyperscale. Is there a break with earlier monitoring and management software that didn't handle the ephemeral nature of applications and infrastructure? Is that the change? >> Yeah, that's basically it. Ten years ago, you as an assistant administrator or operations person, would have known the names of every one of your servers, and you kind of treat them affectionately. "Oh, you know, old Roger is misbehaving again, we got to give it a reboot." These days you don't know, in many cases, how many servers you have, much less what's running on them. So, it used to be that you could set up monitoring where you say, "Okay, I need to look at these things. They should be doing these set of tasks." And you set it up and basically forget it for six months or a year. Now, what's happening on any given machine or what's inside of a container, is churning very, very frequently. And so, to make sense of that, you have to use tags. So to tag all of your infrastructure with what it's doing, maybe what environment it is, like if it's staging or production, whether it's in AWS or On-Prem. Maybe it's a part of a build. And then you can look at your infrastructure and its performance through those lenses. You don't have to think in advance, "Oh, I'm going to want to know what's happening in US-East-1 in production with build number 1180." You can just do that on the fly with Datadog. And that's the sort of thing that we make possible. It's necessary for modern applications and modern services, that really wasn't possible before. >> So, it sounds like it's fairly straightforward at the infrastructure level to know what metrics and events you want to collect, in the sense that, you know, CPU utilization, memory utilization and, you know, maybe even a database number of connections and query time, but as you move up at the application level, the things that you want to ask could become very different between apps. >> K Young: Yeah. >> And then very different across Cloud or On-Prem. >> Yeah, that's right. So, there's sort of two classes of different things you could want to ask. Datadog accepts totally custom metric, so we know about, as I said, 200 different technologies, and we can collect everything automatically. But then, you're going to have your own application and you're going to want to send us things that are specific to your business. We take those just as well. So, for example, I think we have one customer who tracks when cash register drawers open or close. You know, that's not built in, but they can send those metrics to us. They get graphed the same way. We can set alerts on it the same way. We can use sophisticated machine learning to make projections about how we expect those patterns to be in the future, and if the cash registers don't open at the right rate, we can let somebody know that something has gone wrong. So, we can collect any kind of metrics. Then on top of that, we've got application performance monitoring. Right, so that's where you've written custom code, and Datadog, since it's already running on all of your servers, can track requests as it moves from service to service, or between microservices, and recompile that request into a visualization that will show you everything that happened, how long it took, and allows you to drill in and get metadata about each thing. So, you can actually reconstruct where time is going or whether there are problems. >> Why don't I ask you about some of the trends? As I mentioned a minute ago reading that article, or the mention of Datadog by IDC as one of the top five multi-cloud management vendors. What are some of the trends that you were seeing with respect to hypercloud, multi-cloud? You know, we've heard some conversation today from AWS, but I'd love to get your feedback, as the Director of Strategic Initiatives, what are you seeing? >> So, the trend that ... I'm going to answer this, but the trend that we were seeing a few years ago was more and more people were adopting Cloud, period. And that's continued and continued and continued. 18 months ago, if you went and talked to a large financial services organization and you told them, we do monitoring. Okay, they're interested. Well, we run only in the Cloud, so you actually have to send your data to the Cloud. They'd show you the door very politely. And now, they say, "Oh well, we're going to the cloud, now, too." It's a great place to be. Now, we're seeing organizations of all sizes, all types, are in the Cloud. So, the next leading trend is containerization and microservices. So, we actually published a Docker adoption report. We've done it three times now. We refreshed it yesterday. We do it about every six months, and we take a look at all of the usage that we can see. Because we have this somewhat unique vantage point of being able to see tens of thousands of customer's usage, real usage, of infrastructure, and look at, okay, which percent are using Docker? When they use it, do they dabble with it? Do they fully adopt it? Do they eventually abandon it? What are they running on it? So, we published a very long report. Anyone who's interested can actually Google "Docker adoption" and we'll be the top hit there. We've got eight different fact that talk about how quickly it's being adopted. Docker adoption is really quite remarkable. We're seeing a 40% growth in true adoption, not just dabbling, since last year. At the same time, we've seen a more than 100% increase, a more than doubling, of the companies that use Docker, that are using orchestrators, like Kubernetes, to manage even more sophisticated and rapidly changing fleets of machines. And that's really meaningful, because orchestration with containers really enables microservices, which enables Devox, which enables people to move quickly with very little friction and own specific parts of a stack. >> Does that mean that their On-Prem operations are beginning to look more and more in terms of processes like the Clouds? That it's not just a VM, but they're actually orchestrating things? >> Yes, it does. And people will run orchestration on top of the Cloud, or they'll run it On-Prem. But yeah, it's exactly the same. It's the same idea. If you're On-Prem you have a physical machine, you're running several containers in it, and they can just be very fluid and dynamic. >> And then how does machine learning ... How do you fit machine learning into the, whether it's at the infrastructure level or at the application performance management level, do you run it and get a baseline of what's normal? Or ... >> So there's some very deep math behind what we do, so we're able to project where metrics ought to be in the future. Across any number of different categories or tags that you give us, it's important that we do that very accurately 'cause we don't have false positives in our alerts, meaning we don't want to wake people up unnecessarily. We also don't want to have false negatives, meaning we don't want not alert when we should have. So there's a lot of math that goes into that and we can take care of very complex periodicity even while trends are happening within metrics, and doing that at scale, so it happens in real time is a challenge, but one that we're very proud of our solution. >> So you've been able to really derive some differentiation in the market. One of the things I was also reading was that a lot of the business, I mentioned some of those great brands, is in the U.S. and your CIO has been quite vocal about wanting to change that. What's happened in the last year, maybe with big rounds of Fund-Me raise, that's going to help you get more global as even Amazon was talking about expansion and geographies this morning? >> Well so it's even been a while since we've raised money, a year and a half now, I guess, but the company is doing so well. It's a great place to be. The company's doing so well that we're just able to expand our operations and look bigger and bigger. Our two founders are actually French, or they were born in France, at any rate. And so we have a Paris office and we're moving pretty aggressively into Europe now. >> Lisa: Fantastic. >> One question on, again, the hybrid-cloud migration. Whether it's On-Prem to, say, Azure, or On-Prem to Azure and Amazon, would the use of Datadog make it easier for the customer to, essentially, run the same workloads on either of the Clouds? >> Absolutely. So we see a lot of people coming to Datadog at the moment when they need to move from pure On-Prem to maybe hybrid or maybe fully into the Cloud. Because you can set up Datadog to look at both those environments and understand the performance characteristics and then move over bytes of into the Cloud and make sure that nothing's falling apart and that everything is behaving exactly as you expect. >> And then how about for those who say, "Well, we want to be committed to two Clouds, because we don't want to be beholden." >> K Young: Right. >> Do you help with that? >> Yeah, we don't help with literally, like, data movement, which is sometimes one of the challenges. >> But in managing, it's sort of pane of glass? >> Yes, exactly. It's all one pane of glass and you can take ... Once metrics are in Datadog, it doesn't really matter where they came from, you can overlay requests per second or latency and frame Google's Cloud right alongside latency that you're seeing in AWS on the same graph or next to each other, but you can set alerts if they deviate too much from each other. >> So it's kind of an abstraction layer or at least a commonality that customers would be able to have those applications and different clouds from different providers and be able to see the performance of the application and the infrastructure. And so one last question for you, as we're getting ready up to wrap here, you know there's a lot of debate about hybrid-cloud and there's reports that say in the next few years, companies will have to be multi-cloud, just look at the Snap and IPO filing from a couple months ago. Big announcement. Two billion dollars over five years with Google. And then, revise that S1 filing to announce a billion dollar deal with Amazon. >> K Young: Yeah. >> So I'm just curious. Are you seeing that maybe with the enterprises, like a Snap, more and more that, by default, whether it's for redundancy of infrastructure operations, is that a trend that you're also seeing? That you're quite well-positioned to be able to facilitate? >> Yeah, we're definitely seeing ... You know, it's clear that Amazon is in the commanding position, for sure, but we are definitely seeing more and more interest in actual action and other Clouds as well. >> Fantastic. Well, we thank you first of all for being on the program today. Great. Congratulations on the success that you've had with Amazon, with others, and with the market differentiation. Congrats on expanding globally as well, and we look forward to having you back on the program. >> Right. Well, thanks very much for having me. >> Excellent. So K Young, Director of Strategic Alliances from Datadog. On behalf of K, my co-host George Gilbert, I'm Lisa Martin. You're watching The Cube live from the AWS Summit in San Francisco, but stick around 'cause we're going to be right back. (techno music) (dramatic music)

(techno music) >> Announcer: Live, from San Francisco, it's theCUBE, covering AWS Summit 2017. Brought to you by Amazon Web Services. >> Hi, welcome back to theCUBE. We are live in San Francisco at the AWS Summit. We have a great day so far. I'm Lisa Martin, with Jeff Frick, and we're really excited to be joined next by ThingLogix, Carl Krupitzer from ThingLogix, welcome to theCUBE. >> Carl: Thank you. >> Tell us all about ThingLogix What do you guys do? And how do you work with AWS? >> Sure, so we're an IoT platform and solutions company. So we've actually helped customers design, develop, and deploy, and bring to market, IoT solutions and connected products. >> How long have you been, and tell us a little bit about your history. There's an Amazon tie in. >> Carl: There is. >> That kind of predates ThingLogix. >> Carl: Right. >> Give us a little bit of insight about that. >> So we were actually the services and solutions group with inside of a company called 2lemetry. And that was eventually purchased by Amazon and became the AWS IoT platform. So our DNA of our company goes back to the very beginnings of what is now the AWS IoT service. >> Excellent, and so you were founded in 2014? >> 2014, we spun out from 2lemetry. And we did so because we were working with a few big customers that really, we saw an opportunity to help companies really kind of figure out what to do with IoT and accelerate their adoption of IoT inside of the enterprise. >> So there's a consulting arm as well as a technology lead. >> Right, right. So we have our professional services, and our advisory services group that works with customers, really to get them through the idea phase, and then we offer a technology platform that is ThingLogix's foundry, that really is a platform that sits top of all the underlying AWS serverless compute resources. >> So IoT's a big space. GE's in it, everybody's in it. You're a little company. >> Carl: Yeah. >> So what's interesting is, both from an entrepreneurial point of view, as well as just, you know, punching above your weight, how does working kind of in the AWS eco system, both as for your own infrastructure, but also as for go to market and partnership, enable you guys to really do punch above your weight. >> You know, it's a big challenge when you start getting into a partner eco system, like AWS. The thing that sets us apart, really, is that we are very much a pure play, serverless, computing company. From the ground up, we built our own infrastructure that way, we built our own platform that way, and it allows us to be a lot more agile and creative with our customers. It allows us to move much faster and more cost effectively than a lot of other system integrators. >> Right, and you said before we turned on the cameras, that too, it also though, gives you these partnership opportunities with less pure plays. >> Carl: Correct. >> To insert you into potentially a bigger project for that piece that you guys can deliver better than anybody else. That's a pretty unique opportunity. >> Right, yeah. So us partnering with some of the bigger systems integrators is pretty standard practice for us because we can come in and we can work with the the business on really prototyping and innovating quickly. Get us, getting the rapid application development side of things done, and then transition that over to the more managed services oriented firms to take on board. >> Right. And can you imagine trying to do what you're doing without a big infrastructure provider, a big marketplace partner? >> No, it would be nearly impossible. Just to, IoT is fast-moving technology trend. It's been around for a while, in the M to M space. Typically, it's been controlled by the engineering side of house. What we're seeing now is that it's migrating more over to the product management and marketing folks. So they're expecting the same agility that you get with platforms like Salesforce, platforms like Workday. They want that same thing in their product development lifecycle. We've been able to help customers take projects from concept and prototype, through to actually in stores, in the market in about nine to nine weeks, nine to twelve weeks. >> Jeff: Wow. >> So I was just thinking, as you guys were chatting about what the consulting services are like. Give us an example of a typical customer, and you kind of just did, where they, are you talking to retailers that have IoT products to sell, you mentioned, kind of more of a bind center, maybe within products and marketing. So I was just wondering kind of, what is that typical customer like, and what sort of questions have they come to you with? Is it more of an idea that we need to get to market, or is it more of a, we have all of these devices at the edge that we sort of need to-- >> It's a combination, right? We deal a lot with consumer product companies that are trying to enable or connect an existing product or an existing line of products. And they're doing so, not for the engineering purposes, but more to get a better customer experience, and a more timely customer experience, right? Being able to connect with their customers in new and different ways. We're also seeing quite a bit of migration from legacy systems like Exeda or In-House Solutions to the AWS cloud. Really this idea of cloud first architecture, has taken root in the enterprise. And it's been happening over the last 10 years, and I think it's really starting to pay off because companies are looking for a reason not to go to the cloud, versus a reason to go to the cloud. And IoT with the AWS platform and serverless compute resources, really, it takes away all those reasons why you wouldn't. >> [Convention Intercom] Ladies and Gentlemen, don't forget to stop-- >> Lisa: Oops, we'll pause for a-- >> The big voice from above, right. >> Pause for an announcement. >> [Convention Intercom] Get a t-shirt. >> Get a t-shirt. >> Oh, a t-shirt. >> Get a t-shirt. >> I don't want to miss out on that. So just wanted to ask you, give us some ideas of how customers are using the services. I was looking at your webpage, I'll open it back up, and as a pool owner, I though, oh, pool energy. I think I need that. Give us an idea of a company like that. Was this an idea that has really been enabled by what you provide? >> Sure, we've seen companies really try to evolve some of the products, some of their commodity products into more of a smart service, right? When AWS IoT launched, we led with a company called Sealed Air. And they were actually investigating, they make commodity chemicals and cleaning equipment, and things like that. And they were looking for new and different ways to really add value to their products. So we came out, helped them prototype and come out with a connected hand soap dispenser, which seemed kind of silly at the time, but when you start looking at the secondary uses of the data, it allowed them to really start to hone in on hand sanitation compliance, and really kind of start to wrap a reduction of foodborne illness around this one connected device. And as we started to extend that, we started to get into auto-replenishment, we started to get into consumption billing, so they can actually, companies can now take a piece of equipment, put it out to a customer with less capital investment, and charge per hour of use, or per thing that happens on that machine, right? So we're seeing a lot of evolution of business models. People trying to do different things. And it comes down usually to make money or save money, right? >> Jeff: Right, right. >> Companies that want to make money are going down a path of really that enhanced customer experience, companies that want to save money are really looking for efficiencies in field service and warranty claims, and in waste reduction. >> Right. I'm curious though, on kind of the secondary value of the data. >> Carl: Right. >> Was that something they kind of thought about ahead of time, that maybe we'll be able to get? >> Carl: No, no. >> Or was it something that kind of came along. Because clearly, auto-replenishment, right, that's a easy, and billing by consumption, that's not brain surgery. But it's the secondary stuff that really becomes the essence of digitizing your business. >> Carl: Right. >> And I think the hand sanitizer's a really interesting example, because who would ever think there's a digital play beyond the obvious in hand sanitizer. >> Right, right. And what it allows them to do is focus in on behaviors of people that you could never measure otherwise. It would be very difficult to sit in the deli all day long and watch whether or not every employee washes their hands a correct amount of time, but we can really easily take a look across an entire supermarket chain and pick out who the outliers are, and then focus the efforts on training those individuals, and really enhancing the compliance of that. >> So does it pick up their ID tag when they're in proximity to the hand sanitizer? >> Carl: Well, see there are a lot of privacy concerns. >> Right, right. >> The use would be more, take a look at the aggregate of the data and just say, "That one is completely out of norm from the others. >> Jeff: Right, right. That's great, though. >> That's amazing, you again, wouldn't really think of that, but to your point, that does really kind of underscore just one of the important elements that businesses need to consider when digitizing. It's new business opportunities, new revenue streams, cost optimization, and that is a really, kind of a, I don't know, maybe it's not a unique example, the hand sanitizer example, of the other elements in which that business was able to get into by having this secondary look, or maybe a completely different look at the data. >> Yeah, and it's, as IoT really starts to serve those other masters besides the engineering and R and D folks, the marketing people are asking completely different questions than the technology people have been asking, which is why we're being pressured to move so quickly, beause as the creativity starts to enter in to this technology trend, they're expecting results immediately versus having to wait nine months, and spend millions of dollars-- >> It was interesting, in Andy's fireside chat, Buzzword Bingo, he said the buzzword that's delivering on its promise the fastest, in his opinion, was IoT. I was totally caught by surprise. Of all the different things, I would never have guessed that he would pick IoT, but you're right at the leading edge of this stuff, and it's moving faster than probably people probably give it credit for. >> The tough part about IoT is it's so huge, right? >> Jeff: Right. >> There's so many different flavors of it. GE has the industrial IoT that they're chasing after, the consumer products tends to be, right now, it's a trend. They connect everything from toothbrushes to whatever. But the idea being that having this connected product, can either enable new customer experiences, drive new business models, or help drive efficiencies in an organization, is really the fulfillment of that promise. >> Jeff: All right. >> From the culture perspect, I'm just curious, you're small right now, one of the things, too, that Andy talked about that I thought was interesting, was he was starting to talk about the culture of AWS. One of the things that they've been very vocal about is, they're very customer centric. They rarely talk about competition. How is that being a partner and being in the marketplace, with one of the announcements today, that's making it even simpler. Do you feel that, as a partner with them, that being in this marketplace, does their culture kind of permeate through that and help you open doors, like we talked about a minute ago, with other partners? >> Oh, they're fantastic. It's a great partner program just because they're super collaborative with even small partners like us. We had, maybe a little bit different experience coming into Amazon, because we ame with a little bit of knowledge of what they were already dealing with, but they've been really responsive and helpful, and it's, being in the marketplace is going to change the game for us because it offloads a lot of the things that we don't want to do, as we make the move more toward providing a platform as a service. They will take over the billing, and the distribution, and the management of, and customer, more so, than a small company like us would be able to do. So I think it enables a small company to get a greater reach than it would for normal, normally distributed. >> Excellent. Well, Carl, thank you so much for joining us-- >> Carl: Thank you. On theCUBE today, and sharing with our audience, a little bit about ThingLogix. We wish you continued success. >> Thank you. >> In connecting more and more devices globally. >> Carl: Thank you. >> For my co-host, Jeff Frick, I'm Lisa Martin. You've been watching us live on theCUBE, at AWS Summit, San Francisco. Stick around, we'll be right back. (techno music)

(techno music) >> Narrator: Live from San Francisco, it's theCUBE covering AWS Summit 2017. Brought to you by Amazon Web Services. >> Hi, welcome back to theCUBE. We are live in San Francisco at Moscone Center at the AWS Summit. And I'm Lisa Martin, joined with my co-host Jeff Frick. We're very excited to have the chief architect and co-founder of Nextdoor, Prakash Janakiraman, on the program, welcome. >> Thank you for having me. >> For those of you who missed the keynote this morning, Prakash, you did a really fantastic keynote in the session that Werner did, and I loved how you positioned what Nextdoor is. If there's anyone out there that actually doesn't know, give us for those who didn't see the keynote just how you positioned it and talk to us about what you guys are doing, how you've achieved 70% of US neighborhoods covered, over 135,000 neighborhoods in the US. You're expanding globally. Give us in a few minutes this back story on Nextdoor. >> Yeah, so we think of Nextdoor as sitting kind of alongside Facebook, Twitter and LinkedIn. People are using Facebook to communicate with their friends and family all over the world, and Twitter to connect with people with whom they share common interests. And despite having hundreds of Facebook friends and thousands of Twitter followers, in my own neighborhood I only knew a couple of my neighbors, and it's sort of unfortunate. Your neighbors are a great resource that you should know, right, in a local context. So we feel like Nextdoor is bringing back a sense of community to the neighborhood by connecting people in the medium that we use today, which is this virtual medium, online social networking. So we kind of sit alongside of those. And as you think about the difference between what Facebook has been able to do, or an Instagram or a SnapChat, they really leverage existing connections, and when most people don't know their neighbors we have our work cut out for us, because we need to go build those connections. And once those connections are built, they're really, really valuable, right? All of a sudden you didn't know how to find the local electrician or a handyman, or how to reunite with a lost pet, or what happened with this rash of break ins in the neighborhood. Or who do you go to if there's a natural disaster that's happening in your community? How do you band together? How do you even communicate with other people? And so we think we're bringing that basic connectivity to these neighborhoods. >> What was the secret sauce? Because obviously you can't just authorize integration to my contacts, because by definition they're not in my contacts. So how did you kind of crack that code? >> There were a couple of things. So number one was we made a very deliberate decision, we'll get to why this was important, to verify each of our members as being actual residents of a neighborhood community. In our parlance, a neighborhood is really a geographically bounded region. Every single neighborhood in the Nextdoor (mumbles) is started by one member. The member draws the neighborhood boundaries, and then subsequent members-- >> Jeff: They draw the boundaries. >> They do, they do. >> They define it. >> That's right. And so every member that joins has to actually prove that they're residents of the community, and that raises trust. And all these neighborhoods have grown because of word of mouth. People go out, they tell their neighbors, "Hey, you should come on to Nextdoor. "It's a great place for us to connect. "I just heard something interesting "that happened on Nextdoor." But then we also use a variety of different mechanisms to invite people that are a little unusual for internet companies. For example, we allow our members to send out postcard invitations, because again, they don't have phone numbers, they don't have emails or electronic correspondence. So we've had to resort to some non-traditional types of marketing to get the word out about Nextdoor. >> Don't share any secrets, but what percentage of new members come through a postcard sent by a neighbor? >> Well, not isolating postcards, the vast majority of our members are coming in because of a referral from another member, of which postcards are one of those mechanisms. >> Are one piece. >> Yeah. >> With all apps and stuff there's kind of the moment, right, the CNN moment, whatever, where things kind of catch. Is there a particular type of activity in the neighborhood that is the one that kind of catalyzes people where you get that little spike? >> Well, it depends. In a lot of different cases, I remember an example from really the early days of the company, a community in Woodside, California, which is just right down the road from here, a small child in that neighborhood had developed meningitis, and meningitis if you don't know is hugely contagious. It's really bad news. And so lots and lots of members got the word out about this, and that brought new members in because they were saying, "Well, wait a minute, what do I do about meningitis? "Do we need to get immunizations? "Do we need to do something else? "Do we need to quarantine our kids?" So sometimes it's bad news like that. In other cases it's good news, it's about neighborhood events, it's about bringing people together. One of our very first neighborhoods organized a Halloween party, a Halloween parade in their neighborhood, and they used Nextdoor to organize the entire thing, and they had like 50% participation in the neighborhood. It was a small neighborhood of 90 households, but to see all the kids, and them taking pictures and publishing them on Nextdoor, it created this connectivity that just didn't exist before, so. >> And that's such great point, because we are so used to be connecting with everything, and we think we're so connected, we all have over 500 LinkedIn connections, and Twitter followers and Facebook friends, but it's very abstracted. It actually can be very isolating. So it seems like it probably was like a no brainer from an investment perspective to go, this is the, as you said in your keynote, paraphrase, this is the original social network. So talk to us about how you built that on AWS, how you've leveraged the power of AWS, the technologies, to gain what you have done so far. >> It's interesting, because prior to starting this company I was at Google, and we always felt like one of our technology advantages at Google was that we had near infinite computing resources, and that the computing resources were abstracted away from the developers so that they could just go in and build applications and not need to worry about what actually powered those applications under the hood. You said, "I needed some RAM. "I needed some disk. "I need some CPU," and boom, you're off to the races. So coming out of Google, and this is a long time ago now in 2008, seeing that AWS was just getting started with EC2 and S3, and that the playing field was leveled for developers in such a way that you no longer had to rack and stack servers, you didn't need to have a physical data center, you didn't have to have people managing that data center, and that you could use hardware as software really, you could interface with it as software, was hugely powerful. So we started out on EC2 and S3, and over time, and as we fast forward to today, we're using almost 30 different AWS services, including DynamoDB, ECS, Redshift, Kinesis, and so all of these components fit together in running our business. >> Right. Early lines, right, for AWS. It's a data center as an API is one of my favorites, just those little lines, but one of the, so security, all that stuff is kind of done talking about security for a publisher in the cloud, but one other thing that still is out there is at some point, you know, you rent, rent, rent, and at some point you pass a milestone where it's more economical to buy. Clearly you guys didn't buy. Netflix hasn't bought. There's plenty of cases, but from your point of view as a founder and a businessman, and also an operator, when you looked at that, has that ever come up in your discussion as your Amazon bills get bigger and bigger and bigger, and if so, kind of what's the internal discussion and why are you where you are today? >> So we, like a lot of startups, we really value speed and we want to focus on the things that we're really good at, and the things that we're good at are building great products. So we are so advantaged by the fact that we do not have to manage our own infrastructure, that we can use these component parts from which to build our applications, and nowhere is this more apparent than in prototyping new features, right? When you don't necessarily know if something is going to work, you don't want to go make a big investment in procuring resources for that. You want to just be able to spin something up on demand and try it out, and if it doesn't work you shut it back down. And so for us it's a TCO, total cost of ownership, type of question. And along with having on premises data centers and infrastructure comes overhead for maintenance and having teams that do that, and right now, for all of these neighborhoods that we support, 135,000 neighborhoods in the US, right, we only have two people that manage our underlying infrastructure, two dev-ops folks. >> And how many people do you have in the whole company? >> The entire company is about 150 people, and we have 60 of them in engineering, so we're a really, really, really lean team, and we're supporting massive scale with that small team because of the help of something like AWS. And this would've never been possible 10 years ago, so. >> And you'd rather spend that next marginal dollar on another developer, or marketing, or sales, or community, or something, than a piece of metal and a rack. >> We want to put that money towards making a better user experience for our users, and whether that means going into new communities, places where we aren't already, or improving engagement in communities where we already have the product, in either case that's a better dollar spent for us. >> Jeff: Right, right. >> So along those lines, how do you do that? How are you taking user feedback and determining kind of what the next steps are even within the US, but also is there, as Andy Jessie actually talked about in his fireside chat, the GOs that they're expending, they're in 16 now, is there maybe a model to kind of follow along where AWS has a footprint? I'd love to know what that customer engagement is to help really refine that user experience. >> Yeah, there's a few different ways that we can get customer feedback. One is sort of implicit. So we have lots of data that we collect and look at and trying to understand like what features are really popular, what features are taking off, what features aren't really as popular as others, and we do a lot of A/B and split testing to understand how these features perform. But the other way is just good old fashioned qualitative feedback from our users. So we have a couple of different ways that we do that. One, we have a neighborhood operations team that's always in contact with our communities. We have leads in our communities that serve as sort of moderators and the folks that manage the community's activity, and so we lean on them heavily for feedback. And then we have a national leads forum where they all come together and they exchange their own experiences building their community. And from that, it's a treasure trove of information where we communicate with them, we participate in those forums, and then we build the features that we think that they want. >> It's funny, this theme of competing for speed, you're competing against time has come up a number of times today, in Werner's keynote, you mentioned it, it came up again in Andy's fireside chat. I thought it was interesting in your keynote piece how the measure of speed and deployment has so radically changed. It used to be one push a day was crazy. And you talked about a concept of democratization of deployment, which I thought was interesting. Because in the big data world we're always talking about democratizing big data, get it out of the hands of data scientists, let everybody make better decisions based on data, but you talked about democratization of deployments which I've never heard before. Wondering if you could share that story, because again, one a day, that's no longer the measure of success. >> No. So the way that we think about this and the reason that I call it democratization of deployment is as you invest more in automation and you turn down the kind of batching of releases together, the batches of commits together, you can now isolate their impact, and you can move faster by doing more frequent, smaller releases. And part of that is each developer is responsible for their own code going out to production. So what we've invested in is a lot of automation that makes it possible for any one of our developers to push a button, a metaphorical button, and actually push code out to the site, observe how that code performs, and then we can move onto the next one. So in the old world people would often hire a release engineer, and the release engineer's job was really to coordinate all of the activities of the developers, build packages, get them staged and ready for QA, and the overhead on that just does not scale well as a company gets bigger and bigger. And so what we've said is, "Listen developers, "you guys are responsible. "You guys know how your code works. "Let's make it possible for you "to minimize the amount of time "between you committing a piece of code "and that code being live to our users." And so that's what I kind of was talking about in terms of democratization of the release process, making sure that everyone can do it. >> What I thought just was fascinating is on one end everybody is talking about smaller, marginal units of compute and store, etc., right, and yet on the other hand you were basically doing, basically duplicating your entire production environment in kind of this red black strategy, build it up, make that one go, crash the other one. Build up a fresh one, make that one go, crash the old one, and leveraging what are basically infinite resources behind the AWS screen in a really innovative way that you could never do that with your own hardware. >> Well, a lot of credit goes to Netflix. We saw a presentation that they did a few years ago at Reinvent that inspired us to build our own kind of version of the red black deployment system. But again, because these resources are ephemeral, you can spin them up and shut them down, that's what makes any of that possible. So it's really, really awesome for us, because we're moving fast. In fact, I don't even know how many deploys we've done today, but every time a deploy is done we get a message in a Slack channel that says, "Hey, somebody pushed." And it says who the conductor of the train that actually pushed out the commit is, and so you have full accountability in this like living log of what's going on in your production environment, and anyone can do it. Even our interns are allowed to do it, and so it's really, really empowering for our developers to be able to do that. >> I was going to say, that was the word that I heard when you were talking earlier. It's really this empowerment, which is huge for productivity. And speaking of productivity, you guys have really achieved some pretty significant technology achievements that are presumably in this feedback circle going back and making the experience for your mother in law, your mom, probably my mom, even better. What are some of those technology achievements? You mentioned a few of them on the keynote. I'd love for our guests to hear some of those big things that you've achieved. >> Yeah, I think when we think about the technology stack there are two things that we're trying to do. Number one is build for our developers so that they can push features out faster, but then the second and the most important is that we improve the user experience for our users. And most recently, we've done a few different things. Number one is we've introduced this concept around personalization, and it's very lightweight at this point where we can try and understand, what is your experience? If you're not a parent, do you really care about play dates and all of the information about kids? If you don't have a pet, do you care about what's going on with pets? Do you not like the classified ads? And so we're starting to look at data, this is where the big data stack becomes very interesting, and say, "Okay, let's see if we can "match the content in your neighborhood "to the things that you're interested in," and sort of making a lot of investments in that area. And then the second is we're making a lot of investments in search, because we think a lot of people will come to Nextdoor with an intent in mind. They'll say, "Hey, you know what? "I need to find a handyman or a plumber." And what's the natural way to do that is to go into a search box and say, "Handyman," right, and look for handymen in your local community. And so we're making a lot of investments on both fronts that will make it easier for our users to get connected to the information that's most important to them. >> That personalization is so key because we sort of, as consumers and as people who expect things immediately, we want what we want, we want what's relevant. And we know a lot of companies, whether they're banks or whatnot, retailers, spend a tremendous amount of time and dollars investing to know how do I make this experience unique to Jeff, or unique to Prakash, or unique to Lisa. And so I think that there's so much capabilities. I have to ask you though, you must have the most connected neighborhood. You must have the best block parties. (laughing) >> Me personally? >> Yeah. >> You know, it's interesting. The product takes on the identity of each of our communities. And so in an urban community like in San Francisco I'll say that I still don't know all of my neighbors, but it's really, really comforting to know that I know by name and by face who they are even if I haven't had in person interactions. But one of the things that we always talk about, which is pretty cool, and it's happened to me in my own community, is we talk about this concept of bits that move atoms, and using our platform, which is really exchanging information electronically, to facilitate in person interactions, and those are the atoms. And it's definitely happened to me where I've seen someone on the street and I just say, "Hello," right, because I'm like, "Oh yeah, I saw your post. "Did you get your dog back? "Were you able to sell that television?" And so it's really interesting that this is actually happening, and ultimately that's our goal is to bring back that sense of community to neighborhoods and to make neighborhoods stronger and safer, and the way you do that is by connecting people in the community. >> Right, right. >> Absolutely. >> It's a neighborhood watch on steroids kind of, right? >> Totally, yeah. >> Yeah. >> It's fantastic. And I'm sure they would be blown away if they knew they were talking to the guy behind Nextdoor. >> Prakash: One of them. >> One of them. >> One of them. >> Well Prakash, thank you so much for sharing your story with us on theCUBE. >> Thank you very much for having me. >> It's so exciting to get back to the original social network and really kind of, I love that. I think people might even be learning how to write again rather than type. >> My handwriting is terrible. >> We can only hope. >> Reaching, you're reaching there. >> Well Prakash, thank you so much. Continued success with Nextdoor. >> Thank you very much. >> We so much appreciate your time here. >> For my co-host Jeff Frick, I'm Lisa Martin. You've been watching theCUBE. Don't go anywhere. We'll be right back. (techno music)

>> Narrator: Live from San Francisco, it's The Cube! Covering AWS Summit 2017, brought to you by Amazon Web Services. (upbeat music) >> Hi, welcome back to The Cube. We are live in San Francisco at the AWS Summit at Moscone Center. Really excited to be here. A tremendous amount of buzz going on. I'm Lisa Martin with my cohost George Gilbert and we're very excited to have Lowell Anderson, product marketing guru at AWS. Welcome back, Cube alumni! >> Lowell: It's great to be here, Lisa, thank you. >> Great to have you here as well. The keynote this morning was so energetic with Werner and Nextdoor is going to be on the program in a little bit. Over a thousand product launches last year. Not only are there superpowers now that AWS, I like that. You don't have a T-shirt, but maybe next time. But I think the word that I heard most today so far is customer. And I think that it's such a, and as AWS really talks about, it's a really differentiated way of thinking, of doing business. I'd love to understand what the products that were announced today. Walk us through some of the key highlights there. Customer logos were everywhere. So talk to us about how customers are influencing the development of the new services and products coming from AWS. >> Yeah, well, you know, for us, customers are always core to what drives our innovation. It's how we start, we start with what our customers want, and we work backwards from that to try to deliver a lot of the new features and services that we talked about today. And Werner covered a huge breadth of things, but they really fall into maybe four or five categories. He started talking about, directly for developers, talking about what we're doing with a product called CodeStar, which is designed to really help developers build and deploy software applications in the Cloud. He also then went and talked about our new marketplace, SaaS Contracts' capability, which makes it super easy for customers to sign up and purchase SaaS applications using multi-year contracts on AWS, but it also makes it easier for ISVs to make their offerings available for our customers. So again, really trying to make that easy for customers. We talked a lot about what we're doing in artificial intelligence, with the general availability of Amazon Lex today, and then a really entertaining video with Polly, where we saw that avatar speaking and the new whispering capability, so adding a lot more value to our suite of artificial intelligence services. Some exciting stuff in analytics, where we talked about Redshift Spectrum, which is the new search capability on Amazon Redshift that allows customers to search not just the data in their Redshift database, but also search all the unstructured data they have in S3. And then some really exciting announcements here on the database space with DynamoDB DAX, which is an accelerator for DynamoDB. And we also talked about the availability of a new version of Aurora for Postgres. So a lot of new capabilities, both in databases, big data, analytics, machine learning and artificial intelligence, and our offerings for SaaS Contracts as well. >> And that was all before lunch. (laughs) >> Lowell: Yeah, a lot of stuff. >> Lowell, following up on, in order of, let's say the comments on AI and the announcements made there. Microsoft, Google, Amazon all have gone beyond frameworks and tools to fully trained services that a normal developer can get their hands around. But in the areas of conversational user interface, natural language understanding, image recognition. Why do you think that those three vendors, the three vendors have been able to make such progress in those areas, to make that capability accessible, and there's so many other areas where we're still down in the weeds? >> I think there's, we sort of see it in, sort of focusing in maybe three different areas that are really targeted at what our customers are asking for. We have some very sophisticated customers who really want to build their own deep learning and machine learning applications, and they want services like MXNet, which is a highly scalable deep learning framework, that they can do and build these deep learning models. So there's a very sophisticated, targeted customer who wants that. But we also have customers that want to build and train and create prediction algorithms, and they use Amazon Machine Learning, which is a managed service which allows them to look at their past transactional data and build prediction models from it. And then the third piece is kind of what you mentioned, which is services that are really designed for the average developer, so they can really easily add capabilities like chatbots and speech and visual recognition to their applications with a simple API interface. And I think what you touched on is, why did we focus here, Well I think, as Andy also talked about today, that it's really early days in this space. And we're going to see a really, really strong amount of innovation here. And Amazon, which has been doing this for many, many years, and thousands of developers focused on this in our retail side, we're really working hard to bring that technology out, so that our customers can use it. And Lex, which is based on Alexa, which we're all familiar with from using the Echo. Bringing that out and making that type of capability available for average developers to use is a piece of that. So I think you're just going to continue to see that and over the course of the next year you're going to see continued new services coming from us on machine learning and artificial intelligence, across all those three spectrums. >> So let me jump to another subject which is really a hot button for our customers, both on the vendor side and the enterprise side, which is the hybrid cloud, I don't know whether we should call it migration or journey or endpoint. But let's take a couple scenarios. Let's say you're a Hadoop customer, and you've got Cloudera on-prem, you're a big bank, you've put an instance of it on Amazon and on Azure so that you can move your data around and you're relatively free. >> Lowell: Sure. >> Now the big use case has been data warehouse offload. So all of a sudden you have two really great data warehouses that are best in class on Amazon. With Redshift, with now the significant expansion of it, and Snowflake, and then you have Teradata, which now can take their on-prem capabilities and put them on the Cloud. How does the customer weigh the cost/benefit of lowest common denominator versus-- >> Yeah, yeah, sure. I think for us and for our customers it's not a one-size-fits-all. Every customer approaches this differently, and so what our focus has been on is to give them the range of choice. So if you want to use Cloudera, you can deploy it on EC2 and you can manage that yourself, and that's going to work great for you. But if you want a more managed service where maybe you don't want to have to manage the scalability of that Cloudera deployment, maybe you want to use EMR and deploy your Hadoop applications on EMR which manages that scalability for you. And so you make those tradeoffs and each of our customers makes those tradeoffs for different reasons and in different ways and at different times. And so our focus has always been to really try to give them that flexibility, to give them services where they can make the choice themselves about which direction they want to go for their individual applications, and maybe mix it up and try different ways of running these types of applications. And so we have a full range of those types of, from the ability to deploy those directly onto EC2 and manage it themselves, all the way to fully managed services that we maintain all the scalability and management and monitoring ourselves. >> One of the interesting things that Andy Jassy said in his fireside chat just in the last hour or so about HyperCloud was that most enterprises are going to operate in HyperCloud for the next several years, and there are those customers that are going to have to, or want to have their own data centers for whatever type of application. But something also that he brought up in that context, and I know you know a lot about this, George, is VMware. So when I was looking at the announcement that was made in the last six months or so about VMware, vSphere-based cloud services, VMware has just sold off their vCloud Air, kind of competing product, wondering with the VMware Cloud on Amazon, how does that... what are really the primary drivers there? Is that sort of a way to take those VMware customers eventually towards hybrid cloud, or is that an opportunity to maybe compete with some of the other guys who might have more traction in the legacy application migration space? >> I think for us, it's again, it comes back to our customers saying, some of our workloads that maybe for a long period of time have been deployed on VMware and we've been using VMware ESX for many, many years on-premise, and we have these applications that have been deployed for many years there, and they're highly integrated, they use specific features of VMware, and maybe we also like using VMware's management tools, we like using vCloud to manage all of these different instances of our VMware virtualization platform, but we just want to run it in the Cloud, because we want that scalability. When you deploy that stuff on-premise, you're still kind of locked in. Every time you want to expand, you've got to go out and you've got to buy more hardware. You really don't have the agility to expand that business, both as it grows, or as it declines. So you're paying for that hardware to power it and run it no matter what. And so they're telling us we'd like to get some of this up into the Cloud, but we don't want necessarily to have to, we've built these apps, we're comfortable with how they're running them, but we want to run them up in the Cloud and we want to do it with low risk. And that's what this VMware relationship is about, is letting those enterprises that have spent years building and maintaining and using VMware and their various management tools, to do that up in the Cloud. That's really what it's about. >> So let's switch gears to another topic that Andy talked about, since all his topics were topical. Edge computing and IIoT. That's another big shift that's coming along and changing the architecture so we have more computing at the edge again, and huge amounts of data. Obviously there's many scenarios, but how do you think customers will basically think through this, or how should they think through how much analytics and capability is at the edge, that issue of should it look like what is in the Cloud? Or should it be really tight and light and embedded? >> I think we're seeing just an increasing range. And also a really interesting mix, where you have some very intelligent devices, your laptop and so on, that is connected to the Cloud and it has a pretty significant amount of processing power, and so there can be applications that run on that machine that are very sophisticated. But if we're going to start to expand that universe of edge devices out to simple sensors for pipelines, and simple ways to monitor the thermostat in your home, and simple ways to measure and monitor and track all sorts of, you know, automobiles and so on, that there's going to be a range of different on-premise or edge types of compute, that we need to support in the Cloud. And so I think what Andy's saying is that we want to build the Cloud to be the system that can act as the, has the analytics power to ingest data from these maybe tens of millions of different devices, which will have a range of different compute power, and support those applications on a case by case basis. >> We've got to wrap things up here, and I know this conversation could continue for many hours. I think what we've heard here today is a tremendous amount of innovation, and I made the joke, all announced before lunch, but really it was. We're seeing the flexibility, we're seeing the customers really drive the innovation. Also the fact that AWS starting in the startup space with the developers, that's still a very key target market for you, even as things go up to the enterprise. So continued best luck with everything going forward. We're excited to be at re:Invent in just, what, five or six months from now, and with many, many more thousands of people and hearing the great things that continue to come from the leader in public cloud. >> Lowell: All right. Thank you, Lisa. >> Thanks for joining us, Lowell, we appreciate it. Next time I want the superpower T-shirt. (laughs) >> (laughs) Okay, I'll take you up on that. >> All right. I'm Lisa Martin for my cohost George Gilbert. Thanks so much for watching, stick around. We are live at the AWS Summit in San Francisco, and we will be right back. (upbeat music)

>> Announcer: Live, from San Francisco, it's theCUBE, covering AWS Summit 2017, brought to you by Amazon Web Services. (bouncy techno music) >> Hi, welcome back to theCUBE, live in San Francisco at the AWS Summit here. I'm Lisa Martin, joined by my co-host Jeff Frick. Our next guest is from Sumo Logic. We have the VP of Product Marketing, Kalyan Ramanathan. Welcome to theCUBE! >> Thank you very much. Very excited to be here. >> Very excited to have you here. So, tell us a little bit about what Sumo Logic is doing with AWS and machine data. What services are you delivering, who's your target audience, all that good stuff. >> Yeah, absolutely. We are a cloud native, i.e., SaaS-based, machine data analytics platform, and what we do is to help our customers manage the operations and security of their machine-critical applications. Right, so we are an entirely AWS-based customer, we've been using AWS since our inception. What we do is to provide machine data and machine learning so that our customers can manage the performance of their applications, right. So, what is machine data, you might ask. So machine data typically includes logs, metrics, events, anything that your application is generating when it is running, when it is serving the enterprise's customers. And what Sumo Logic excels at is to ingest this data. We collect and ingest this data, and then we apply a lot of analytics on that data. We have some patented machine learning technologies that helps us correlate this data, get insights from this data, and then using this data, our customers manage the applications that they are providing to their end customers. >> And it's not just their applications that are co-located at AWS with your application, it's beyond that, I assume. >> Absolutely, I mean, we have customers from, you know, very different walks of life, we have customers who are on-prem, customers who are down the hybrid path and moving to AWS, and customers who are on an AWS. You know, I can rattle off a queue of great names, Pinterest, Twitter, Airbnb, are examples of customers who are born in the cloud. They run on AWS from the very get-go. And they use us today to manage the security and performance of their applications. We have other customers who have migrated to AWS, Scripps Network, the guys behind HGTV, it's a great example of a customer who was running applications in their on-prem data center, and then one day decided that they are a content company, and they don't want to be running their own data center. >> Right. >> And so they wanted to move their applications to the cloud, and they used Sumo Logic to help migrate their applications to AWS. >> What are some of the barriers that you help customers overcome when it comes to maybe that daunting task of migrating services? >> Yeah, that's a great question. You know, the first thing that someone has to do before they start to migrate their applications to the cloud is to understand what is it that they have within the data centers, right. If I don't know what I have, how do I even migrate that to the cloud? The first task is obviously provide visibility into what is within their data center. And that's where Sumo Logic comes in, right. If you deploy Sumo Logic, and if you implement Sumo Logic as a SaaS service, the first thing that we do is to provide you complete visibility into your applications. All the application components, the infrastructures that the application is deployed on, the services that the application may be using. The next thing that you want to do is start to migrate your workload to the cloud. But you want to do this in a very thoughtful way, and what that means is that you start to move your applications and your infrastructure to AWS, but then you do this cut of work to AWS, only when you are convinced about the performance as well as the security of that application in this new environment. So the ability to baseline what you have in your current environment, and then compare it to what it might look like in this new environment within AWS is extremely critical, and what Sumo Logic helped Scripps Network do is to essentially compare and contrast how they are performing in this new environment. And when they were extremely comfortable that their security and their performance was no less in this new environment compared to what they were doing in the data center, they were able to flip that switch and complete the move over to AWS. >> You guys are in an interesting position, because you were born in AWS, essentially, cloud-native, and you have a lot of customers that are running in AWS. And so you guys did a survey, a report, really kind of taking a look at what's actually happening with cloud-native companies running their apps in AWS. I wonder if you can kind of give-- What did you guys find in this thing? >> Yeah, absolutely Jeff. And this is, the report that we put out towards the end of last year, I think is one of the first start leadership reports that gives, you know, people in AWS, a birds-eye view into how are their peers, you know, deploying, architecting, and managing their applications within the AWS environment. So, how did we put this report together? Sumo Logic has over 1200 customers under management today and more than 80% of our customers are, you know, using AWS today. They are implementing their applications within AWS. So what we did was to anonymously mine data from our customers, and publish a report that provides the set of best practices, and the commonly-used techniques and architectures that, you know, the leaders are doing and implementing today as they move to AWS. Now there were some great learnings that we found out as we put this report together, alright. First and foremost, we discovered that the stack, that a customer typically deploys in AWS, is very unlike the stack that they deploy within their on-premise data center. So, how does that work out? I mean, so, many of the AWS customers that we mined here, happen to use Docker extensively within their AWS environment. In fact, 18% of our customers, this was last year, already are using Docker, you know, for the production application. Which is pretty amazing, given that Docker is just, you know, two or three years-- >> Well hopefully Solomon and Ben are watching, we actually have another crew with Docker-- >> Absolutely. >> Right now. >> We'll have to report that back. >> You know, Docker is all the rage, no doubt about, and we are seeing, you know, increased adoption of Docker across the board, among all of, for AWS customer. The other thing that we found very interesting was that the applications that you may typically expect to succeed in your data center, are not quite doing that well in the AWS world. I'll give you a good example, in the database world, you would expect to see Oracle and SQL Server, you know, ruling the root within a typical data center today. You go on AWS, that's not the case at all. The NoSQL databases, right, are the leading vendors of databases within the AWS world. MongoDB, Redis, you know, are well ahead of Oracle and SQL Server when it comes to AWS. When it comes to web server technologies. You know, Nginx and Apache, you know, are well ahead of IAS, which happens to be the web server of choice within the data center world. Now we've also seen, you know, pretty amazing adoption of Lambda Technologies within AWS. I mean, that's to be expected, a certain bit, because I know AWS is definitely pushing it, but again, 12% use it within a production environment. You know, one year into Lambda, GA in some sense, is pretty astonishing numbers, so-- >> What was your takeaway? Was it because of the applications that are deployed, is it because, kind of, historical legacy of what Amazon offered, kind of for an on-prem versus an on-prem, you know, those early business decisions, not so much today, but, you know, years ago, when there was the security and public cloud, you know, it was a very different conversation three years ago. What were some of your takeaways as to the why? >> The takeaways that I think, there's a meta takeaway here, and let me start with that. The meta takeaway is that as people are building applications in AWS, native AWS applications, or as they are migrating their applications from an on-prem data center to, let's say, AWS, this is giving IT architects the opportunity rethink how their applications are constructed. You know, they are no longer bound by the old shackles of, if I have to use a database, it's Oracle or SQL Server. If I have to use a IIS web server, it's IIS or some other option. >> Right. >> So, once you are unchained from these shackles, you have the ability now to rethink and re-architect your application from scratch to target and to focus on this amazing new world that the cloud, you know, offers. So that's the, that's a big meta takeaway for us, and, what we have learned is that once you are unbound, you can come up with new technologies and new ways of doing things that are adopted and better suitable for this new space. That's one. The second thing that we do see, obviously, is that the vendors of yesterday are not yet focused on the cloud technologies. It may be heresy to say this, but, you know, Oracle has not found a cult religion until very recently. And that's why you see Oracle as not doing a lot, or not making a dent in, you know, in cloud places or in cloud technologies like AWS. >> Right, right, it's just interesting, that procurement angle, because, as anyone who's ever been at a relatively small company, trying to sell into a big company, one of the biggest hurdles is actually just getting on the procurement list, becoming an approved vendor. So, it's interesting to think about that from the other side as a consumer. That if now you are unshackled from the approved vendor list, and you, because if now the only approved vendor is Amazon, and now you have this whole breadth of things to choose from within that ecosystem, that, how that could really impact your behavior and what you actually buy, build, and deliver. >> Yeah, I mean, I think that's a great point too. I mean, there are economics involved here, there is the friction of adopting certain technologies to AWS, which also makes it a little harder to adopt some of the more traditional software applications in the AWS world. Now that's why AWS obviously has come up with the notion of a marketplace, and Sumo Logic, you know, we face the same challenges when we are signing up customers, right. We have some big-name customers who, you know, if we have to sell into those customers, you know, we have to get into their procurement list, we have to, you know, go through a few rigamaroles-- >> Jeff: Right, Right >> To even get into that list. That's where, you know, getting into the AWS marketplace has really helped us a lot. Now you have one vendor, you have one relationship, you have one payment terms, and that vendor is already on your approved list. And so, hey, Sumo Logic comes along with the rights. >> So, definitely a simplification there, which was one of the themes in the keynote this morning, as well as this unshackling. What are your objectives for the report, are you going to be either going back to some of your existing customers or to new customers to show them all of these best practices that you've developed? >> Yeah, I mean, I think our goal of this report, obviously, first thing from us is to make this an annual report, we plan to do this every year, write it on reinvent. And what we want to do is to provide our community, who are mostly AWS shops today. We do have a few Microsoft Azure customers, and we are starting to see some Google Cloud platform customers too. But what we want to do is become the hot leader, who not only serves his customers, but also provides them a road map, in terms of, you know, how should they be adopting these cloud technologies. >> Jeff: Right. >> What are their leading-edge peers like the Twitters and the Airbnbs and the Pinterests of the world starting to do. Obviously, in a anonymized way, we don't want to be calling out any of our customers by name, but here is how you need to think about architecting your applications in the cloud. There is an opportunity, as we said, to, you know, break open from the chains of the past, redo this. We want to help our customer redo this well. >> I'd love to get your perspective, what are the, you know, and I think we're past the security and some of those kind of historic impediments, to you will, to public cloud adoption, but one of the ones that still comes up all the time is the rent versus buy, and you know I think it goes back to the tested roots of, yes, it's great to rent for awhile, but at some point in time, when you hit some scale-- >> Kalyan: Right. >> The business model flips and now it's more economical to buy and operate your own. But what we see in the slide that Werner showed today, there's plenty of customers, Netflix, of course always being the flagship, that are giant, and must have a giant AWS bill every month, who have chosen to still leverage them as their IT platform, and not flip the switch to a purchase. So you know, kind of either from the survey or anecdotally with your own customers, and you as a company, you know, what impacts that decision and do you have, like this review every couple of years, when those CFOs go, "Ah, we're paying these guys a lot of money," should we build our own stuff, but clearly you haven't gone that route. >> I mean, there are definitely enterprises who are still on-prem today, I think the last stat that I heard from Gartner is that 20% of enterprises have flipped over to public cloud infrastructure. 80% is still running things in the cloud, you know, within the data center, maybe a private cloud or maybe in the traditional old ways of running applications. But that tide is definitely turning. And what we see from many of our customers is that there are many reasons for customers or enterprises to now start adopting public cloud. Economics is obviously one, I mean, there is a big advantage of going from Capex to Opex, it obviously makes a lot of sense to do that. The second thing is that what we see is that it's not just about moving the application to the cloud, it's also having the right tooling around the application that can now allow you to manage that application, manage the performance of that application, the security of that application, the deployment of that application in the public cloud environment. And that has taken a while to mature, and I think we are already there, I mean, in an event like this, you can see so many companies come up with new, innovative ways of managing applications within the public cloud environment. And I think we are there now, I mean, the pendulum has swung, and we have enough technologies now to do this with a very high level of confidence. The third thing I would say, and you know, we keep hearing this from our customers again and again, and you know, I brought up Scripps as a great example, you know, we just did a public webinar with a company called Hootsuite, and, you know, they are a social media management platform company, and one of the comments from the Hootsuite VP of Operations was very telling, he said, "Look, I can do this, I can run my own stuff, but do I really want to do it, right? I am a social media company, I want to provide the best application to my customers. I'm not in the business of running a management technology, you know, on-prem or even, for that matter, you know, within the four walls of the company itself. What I want to do is focus on where I can deliver the best value to my customer, and that is by delivering a great social media application." >> Lisa: Exactly. >> "And I want to let the infrastructure game, the management game to the experts," right. >> Focusing on their core competencies to really drive more business. >> I mean I think we are definitely starting to see that, there are certain verticals that have adopted this, you know, wholeheartedly, retail is a good one, media is a good one, there are also cost pressures in those verticals that are forcing them to adopt this at a much faster pace. Financial is kicking and screaming, but they are also getting on board. >> But definitely from a thematic perspective, you talk about maturation, maturation of the services, maturation of the technologies, and maturation of the user. So we want to thank you so much for stopping by theCUBE, great to have you here. >> Thank you very much, I mean, it's been a great conversation with you guys, and it's a great event. >> Excellent, well for my co-host Jeff Frick, I am Lisa Martin, you're watching this on theCUBE live in San Francisco as the AWS Summit. Stick around, we'll be right back. (bouncy techno music)

>> Voiceover: Live from San Francisco it's theCUBE. Covering AWS Summit 2017. Brought to you by Amazon Web Services. (upbeat music) >> Hey welcome back to theCUBE live at the Moscone Center at the Amazon Web Services Summit San Francisco. Very excited to be here, my co-host Jeff Rick. We're now talking to the Chief Scientist and professor at University of San Francisco, Greg Benson of SnapLogic. Greg, welcome to theCUBE, this is your first time here we're excited to have you. >> Thanks for having me. >> Lisa: So talk to us about what SnapLogic is, what do you do, and what did announce recently, today, with Amazon Web Services? >> Greg: Sure, so SnapLogic is a data integration company. We deliver a cloud-native product that allows companies to easily connect their different data sources and cloud applications to enrich their business processes and really make some of their business processes a lot easier. We have a very easy-to-use what we call self-service interface. So previously a lot of the things that people would have to do is hire programmers and do lots of manual programming to achieve some of the same things that they can do with our product. And we have a nice drag-and-drop. We call it digital programming interface to achieve this. And along those lines, I've been working for the last two years on ways to make that experience even easier than it already is. And because we're Cloud-based, because we have access to all of the types of problems that our customers run into, and the solutions that they solve with our product, we can now leverage that, and use it to harness machine-learning. We call this technology Iris, is what we're calling it. And so we've built out this entire meta-data framework that allows us to do data science on all of our meta-data in a very iterative and rapid fashion. And then we look for patterns, we look for historical data that we can learn from. And then what we do is we use that to train machinery and algorithms, in order to improve the customer experience in some way. When they're trying to achieve a task, specifically the first product feature that is based on the Iris technology is called the Integration Assistant. And the Integration Assistant is a very practical tool that is involved in the process of actually building out these pipelines. We call, when you build a pipeline it consists of these things called snaps, right? Snaps encapsulate functionality and then you can connect these snaps together. Now, it's often challenging when you have a problem to figure, OK, it's like a puzzle what snaps do I put together, and when do I put them together? Well, now that we've been doing this for a little while and we have quite a few customers with quite a few pipelines, we have a lot of knowledge about how people have solved those puzzles in the past. So, what we've done with Iris, is we've learned from all of those past solutions and now we give you automatic suggestions on where you might want to head next. And, we're getting pretty good accuracy for what we're predicting. So, we're basically, and this integration system is, a recommendation engine for connecting snaps into your pipelines as they're developing. So it's a real-time assistant. >> Jeff: So if I'm getting this right, it's really the intelligence of the crowd and the fact that you have so many customers that are executing many of the similar, same processes that you use as the basis to start to build the machine-learning to learn the best practices to make suggestions as people are going through this on their own. >> Greg: That's absolutely right. And furthermore, not only can we generalize from all of our customers to help new customers take advantage of this past knowledge, but what we can also do is tailor the suggestions for specific companies. So as you, as a company, as you start to build out more solutions that are specific to your problems, your different integration problems... >> Jeff: Right. >> The algorithms can now be, can learn from those specific things. So we both generalize and then we also make the work that you're doing easier within your company. >> And what's the specific impact? Are there any samples, stories you can share of what is the result of this type of activity? >> Greg: We're just, we're releasing it in May. >> Jeff: Oh OK. >> So it's going to be generally available to customers. >> Couple weeks still. >> Greg: Yeah. So... So... And... So... So we've done internal tests, so we've dove both through sort of the data science, so the experimentation to see, to feed it and get the feedback around how accurately it works. But we've also done user studies and what the user studies, not only did the science show but the user studies show that it can improve the time to completion of these pipelines, as you're building them. >> Lisa: So talk to us a little bit about who your target audience is. We're AWS, as we said. They really started 10 years ago in the start of space and have grown tremendous at getting to enterprise. Who is the target audience for SnapLogic that you're going after to help them really significantly improve their infrastructure get to the cloud, and beyond? >> Greg: So, so, so basically, we work with, largely with IT organizations within enterprises, who are, you know, larger companies are tasked with having sort of a common fabric for connecting, you know, which in an organization is lots of different databases for different purposes, ERP systems, you know, now, increasingly, lots of cloud applications and that's where part of our target is, we work with a lot of companies that still have policies where of course their data must be behind their firewall and maybe even on their premise, so our technology, while we're... we're hosted and run in the cloud, and we get the advantage of the SAS, a SAS platform, we also have the ability to run behind a firewall, and execute these data pipelines in the security domains of the customers themselves. So, they get the advantage of SAS, they get the advantage of things like Iris, and the Integration Assistant, right, because we can leverage all of the knowledge, but they get to adhere to any, you know, any regulatory or security policies that they have. And we don't have to see their data or touch their data. >> Lisa: So helping a customer that was, you know, using a service-oriented architecture or an ETL, modernize their infrastructure? >> Greg: Oh it's completely about modernization. Yeah, I mean, we, you know, our CEO, Gaurav Dhillon has been in the space for a while. He was formerly the CEO of Informatica. And so he has a lot of experience. And when he set out to start SnapLogic he wanted to look, you know, embrace the technologies of the time, right? So we're web-focused, right? We're HTTP and REST and JSON data. And we've centered the core technologies around these modern principles. So that makes us work very well with all the modern applications that you see today. >> Jeff: Look Greg, I want to shift gears a little bit. >> Greg: Yeah. >> You're also a professor. >> Greg: Correct. >> At University of San Francisco and UC Davis. I'd just love to get your perspective from the academic side of the house on what's happening at schools, around this new opportunity with big data, machine-learning, and AI and how that world is kind of changing? And then you are sitting in this great position where you kind of cross-over both... How does that really benefit, you know, to have some of that fresh, young blood, and learning, and then really take that back over, back into the other side of the house? >> Greg: Yeah, so a couple of things. Yeah, professor at University of San Francisco for 19 years. I did my PhD at UC Davis in computer science. And... My background is research in operating systems, parallel and distributed computing, in recent years, big data frameworks, big data processing. And University of San Francisco, itself, we have a, what we call the Senior and Masters Project Programs. Where, we've been doing this for, ever since I've been at USF, where what we do is we partner groups of students with outside sponsors, who are looking for opportunities to explore a research area. Maybe one that they can't allocate, you know, they can't justify allocating funds for, because it's a little bit outside of the main product, right? And so... It's a great win, 'cause our students get experience with a San Francisco, Silicon Valley company, right? So it helps their resume. It enhances their university experience, right? And because, you know, a lot of research happens in academia and computer science but a lot of research is also happening in industry, which is a really fascinating thing, if you look at what has come out of some of the bigger companies around here. And we feel like we're doing the same thing at SnapLogic and at the University of San Francisco. So just to kind of close that loop, students are great because they're not constrained by, maybe, some of us who have been in the industry for a while, about maybe what is possible and what's no so possible. And it's great to have somebody come and look at a problem and say, "You know, I think we could approach this differently." And, in fact, really, the impetus for the Integration Assistant came out of one of these projects where I pitched to our students, and I said "OK, we're going to explore SnapLogic meta-data and we're going to look at ways we can leverage machine-learning in the product on this data." But I left it kind of vague, kind of open. This fantastic student of mine from Thailand, his name is Jump, he kind of, he spent some time looking at the data and he actually said, "You know I'm seeing some patterns here. I'm seeing that, you know, we've got this great repository of these," like I described, "of these solved puzzles. And I think we could use that to train some algorithms." And so we spent, in the project phase, as part of his coursework, he worked on this technology. Then we demoed it at the company. The company said, "Wow, this is great technology. Let's put this into production." And then, there was kind of this transition from sort of this more academic, sort of experimental project into, going with engineers and making it a real feature. >> Lisa: What a great opportunity though, not just for the student to get more real-world applicability, like you're saying, taking it from that very experimental, investigational, academic approach and seeing all of the components within a business, that student probably gets so much more out of just an experiment. But your other point is very valid of having that younger talent that maybe doesn't have a lot of the biases and the pre-conceived notions that those of us that have been in the industry for a while. That's a great pipeline, no pun intended... >> Greg: Sure. >> For SnapLogic, is that something that you helped bring into the company by nature of being a professor? Just sort of a nice by-product? >> Well, so a couple of things there. One is that, like I said, University of San Francisco we were running this project class for a while, and... I got involved, you know, I had been at USF for a long time before I got involved with SnapLogic. I was introduced to Gaurav and there was this opportunity. And initially, right, initially, I was looking to apply some of my research to the technology, their product and their technology. But then it became clear that hey, you know we have this infrastructure in place at the university, they go through the academic training, our students are, it's a very rigorous program, back to your point about what they are exposed to, we have, you know, we're very modern, around big data, machine-learning, and then all of the core computer science that you would expect from a program. And so, yeah, it's been... It's been a great mutually beneficial relationship with SnapLogic and the students. But many other companies also come and pitch projects and those students also do similar types of projects at other companies. I would like to say that I started it at USF but I didn't. It was in existence. But I helped carry it forward. >> Jeff: That's great. >> Lisa: That is fantastic. >> And even before we got started, I mean you said your kind of attitude was to be the iPhone in this space. >> Greg: Of integration, yeah. >> Jeff: So again, taking a very different approach a really modern approach, to the expected behavior of things is very different. And you know, the consumerization of IT in terms of the expected behavior of how we interact with stuff has been such a powerful driver in the development of all these different applications. It's pretty amazing. >> Greg: And I think, you know, just like maybe, now you couldn't imagine most sort-of consumer-facing products not having a mobile application of some sort, increasingly what you're seeing is applications will require machine-learning, right, will require some amount of augmented intelligence. And I would go as far to say that the technology that we're doing at SnapLogic with self-service integration is also going to be a requirement. That, you just can't think of self-service integration without having it powered by a machine-learning framework helping you, right? It almost, like, in a few years we won't imagine it any other way. >> Lisa: And I like the analogy that Jeff, you just brought up, Greg, the being the iPhone of data integration. The simplicity message, something that was very prevalent today at the keynote, about making things simpler, faster, enabling more. And it sounds like that's what you're leveraging computer science to do. So, Greg Benson, Chief Scientist at SnapLogic. Thank you so much for being on theCUBE, you're now CUBE alumni, so that's fantastic. >> Alright. >> Lisa: We appreciate you being here and we appreciate you watching. For my co-host Jeff Rick, I'm Lisa Martin, again we are live from the AWS Summit in San Francisco. Stick around, we'll be right back. (upbeat music)

>> Narrator: Live from San Francisco, it's the Cube, covering AWS Summit 2017. Brought to you by Amazon Web Services. (upbeat music) >> Hi, welcome back to the Cube, live from the AWS Summit San Francisco. Jeff Frick, and I are here with the CMO of Splunk, Brian Goldfarb. Hey Brian, welcome to the Cube. >> Thanks, thanks for having us, we're really glad to be here. >> You've been the CMO at the Cube, the Cube, congratulations! >> Brian: Promotion, this is amazing. (laughs) >> You've been promoted. Let me start again, you've been the CMO with Splunk, am I red yet, for about six months. Talk to us about the new role that you have there, what do you, what's exciting, what's happening? >> Yeah, it has been almost six months now. It's been an amazing experience. Splunk was super attractive to me as I was looking at opportunities, because it has both an amazing product and customers who love it. And that combination, particularly in technology, is that rare first place. That's a marketer's dream. You're not creating champions, you're not convincing anyone that it's great. And so I've been coming in focusing on how do take that incredible asset, our community, and our users and really expand it. And that's been a big focus for me over the last five months. It's an amazing company. I'm very honored and lucky to be working with such a great place. And in fact, we won, "Best Places to Work." >> Lisa: Congratulations. >> For the tenth year in a row. >> The Santana row office are pretty nice. I was lucky enough to go down there and check those out when you opened them. >> Oh yeah, that's awesome. Our headquarters is in San Francisco, but as you think about the expansion of the area having facilities down in San Jose is super great for as we grow our company. >> So I guess, it's a match made in heaven, but the word on the street is you're a data guy. You want data to support everything. Data driven solutions. Data backed decision making. What a perfect fit because the essence of Splunk is basically sitting on that machine data that's flowing through the system. >> That's right. You think about where our roots are, is really how do take big data and make it useful for people. Like machine data is often forgotten. All the information flowing from sensors and hardware and servers. And as we sit here, at the Amazon Web Services show in San Francisco, all of that infrastructure is core to creating machine data. And we want to make it accessible and usable for everyone to get insights. And what we see is that manifest itself in a lot of interesting ways. I'll give you an example, Yelp. Think about food, think about reviews, but they're using Splunk for a couple things. One, make sure that their core infrastructure is up and running, obviously important. Because we need that restaurant review, you need it now. That's a very San Francisco thing. But more importantly as they've rolled out their new food delivery capabilities, all of the business analytics required to make sure that operations business runs tip top is critical. So they're using Splunk for all those pieces. >> So I wonder if you can speak a little bit about the relationship with AWS? I know you're relatively new, but Doug Merritt is relatively new. And of all of the logos that Verner went which were numerous and hard to see, (Brian laughs) he picked Doug to come up and really help out with the keynote. Obviously, Cloud, big deal, AWS, big deal. What is the relationship, how has it evolved over time, and how is this cloud-enabled delivery impacting the way Splunk does business? >> Yeah, we're very fortunate to have a wonderful partnership with Amazon Web Services. We've been a strategic partner of them for almost five years. And we made a big bet of our business on using their product to deliver our product in the cloud. Our business started 14 years ago with Splunk Enterprise, an on-premises based software solutions that's been adopted by over 13,000 customers around the globe. And we heard time and time again, as the cloud became more important in the decisions people were making, how do we get the visibility that we need both across our on-premises assets and our cloud assets? And so the relationship with Amazon has been predicated on how do we deliver Splunk in the cloud and more importantly, how do we give everyone who's now adopting Amazon at this amazing clip the visibility into all the components that they're using, so they can maintain their solutions, they can make sure things are running, they can optimize their span, et cetera. >> And it's even a building partner, right? So it's an infrastructure partner, it's a delivery slash sales channel partner, and there you can even build directly through Amazon, if I heard right today. >> That's right. So we're both a customer and a partner is one way to think about it. And today in the keynote, we announced with their new AWS marketplace, SaaS Contracts API release, that we're one of their first partners delivering our product through that new delivery model. And what's really interesting about it is today enterprises are trying to innovate faster. They get stuck sometimes through things that shouldn't matter. Procurement, legal, how do you actually get the assets that you need in order to do the things they need to do? Speed is such an important part of being successful. And now that we can deliver Splunk through the AWS marketplace, customers can easily find it. They can now easily buy it using their existing building relationship with Amazon. They can use friendly terms that are defined there. And they can buy on one year, two year or three year contracts with the appropriate term-based discount. So the longer you buy, the cheaper it is. So, procurement's happy, legal's happy, the technical user's happy 'cause they can move faster than they ever have before. >> One of things that we're hearing in a lot of enterprises is that directives coming down from the board to the CIO. You've got to move more legacy applications to the hub, but you've also got to try to find more value from digital assets. With that respect, what are some of the core functions that Splunk Enterprise on AWS is delivering to customers from a value out of our assets perspective? >> There's assets across so many different categories, so we look at, what are we doing across the infrastructure side of the business? What are we doing across the security side of the business and now this emerging category of IOT, how do we get all of the assets working together? And one of the things that we think about a lot with our customers is we have all this data. How do you apply different lenses so that different people can ask different questions of this same data and get the key insights back. So if I'm a security investigatory trying to prevent fraud, that's something that we can do, but that's also helping the people in IT maintain systems faster and it's also doing business, process management, working with supply chain and we see that happening everywhere. We were talking just before we started about this mental model that enterprises have where they're stuck in this reactive place. Something breaks, then you fix it. Or a customer complains and you deal with it and everyone's on this journey to being more proactive. How do I get notified that something broke so that I can fix it, or better yet, predictive? So we're taking machine learning and artificial intelligence concepts, baking them in directly into the Splunk platform and using that to help people go from that reactive state that they're in to this forward state of predictive intelligence and being able to fix things before they even become a problem. >> I would love to dig in a little bit deeper on IOT, 'cause you guys are into IOT when it was called machines. Machines are just a subset of the things and now, the IOT thing is really taking off. Obviously, we to the GE shows and also people are things, too, which sometimes gets forgotten in the conversations, and we all throw off a ton of digitals off, so you guys are pretty well positioned to apply your technology techniques, processes now to a whole giant new set of data flows coming off all these things. >> You put the words in my mouth. People forget about people being things. We talk about machine data, the word machine can mean anything, really. It's how do you take all of this data, correlate it together in interesting ways, then do something with it. Thing about the retail use case. Customers now have an expectation of the experience that they're going to have, higher than ever before. You just expect more, you know they have the information, so you want it. You think about beacons and knowing your preferences, so retailers need to take advantage of that and they can use technology like Splunk to really get there. Another example around customer expectation, think about travel. We all travel here, you guys probably flew in or drove in, and we have mediocre experiences at the airport in particular. We have a customer Gatwick Airports in the UK who's completely Splunked everything they're doing at the airport with a goal of reducing the amount of time that it takes to go from the front desk to your gate to less than five minutes. So on a dashboard, they can see wait times at any particular security terminal, they can redeploy assets, they get alerts, and they can monitor all the different data streams, whether it's weather data, air traffic control data, airline data, sensors from all the different parts of the airport, and pull all that together into a people-based experience to drive up that engagement. >> Gatwick, great example, and your CEO was also talking about Coca Cola on stage, for example. You've got over 13,000 customers, so as we look at where we are today with cloud users maturing, cloud providers maturing, looking at what Amazon has to date, over 90 services. As customers look at getting more legacy applications out of operations, how is Splunk helping customers on this journey to hybrid, or is hybrid a destination? What's the conversation there like with the senior leaders that you talk to down to the IT folks? >> In my job, I get the luxury of talking to hundreds of CIOs and I'll tell you, all of them see hybrid as the destination. Most of the enterprises that exist in the world have investments in things from mainframes to existing infrastructure and data centers and even as they consolidate more and more into the cloud, we're going to be in a world where people have assets in many different places. What we've seen with Amazon and why I think our partnership has been so successful is we're helping a lot of these enterprises justify and control how they're able to get to the cloud faster. We talked about innovation and speed. Being able to adopt services in the cloud in addition to what we're doing on premise is critical. And with Splunk, they get insight across all their different components. They feel that they can manage the security across both on premises and the cloud and they get the peace of mind that they have that operational visibility because they're going to be hybrid, they're going to be running in the cloud, they're always going to have their existing investments. That's kind of the state of the world for the foreseeable future. >> So, looking forward, you've been in the job about six months or so, what are your priorities for the next six months? Doug says, "alright, warm up time's over, "get to work, Brian." >> He said that on the third day. >> (laughs) On the third day. So what are some of your priorities? >> As a business, we have a collection of priorities. One is the cloud, full stop. We know that the journey to the cloud is coming full speed and what we can do around Splunk cloud and being able to fulfill and delivers services for our customers there is absolutely critical and continuing to grow that capability. And second for us is customer success. How we get people beyond single use case to multi use case. Using it in IT, how to take advantage of it in security. How do you take advantage of it in supply chain? Because that magic moment that customers have is really when they have the same data in and they get value across their entire business. For me, as the CMO, my priority is piggy back on that. First and foremost is digital. It's kind of trite, everyone's talking about it but I came from Google and sales force. I'm a performance guy and so I'm looking at how we can reconstitute the entire buyer journey from the moment someone says, "I'm interested "in a topic that's relevant to our product" to "I transact online" and that's a big initiative for what we're doing across web and sales team to work through all those pieces, and then second, I now am the chief t-shirt officer. >> Jeff: That's not an easy job. >> It's the hardest job I've ever had, 'cause I'm not in my strength and always innovating on what's next. I hear I was trending on Twitter, Doug's t-shirt versus Werner's t-shirt today. >> Jeff: That's right. >> I think we were winning. >> And you guys have the biggest t-shirt booth installation, device at trade shows than anyone rather than just giving away, in the back, the entire booth is basically built around the t-shirts. >> Oh, and we're Splunking everything, too. >> Impressive. >> And we saw a spike in traffic, too. Our store this morning after we went on stage. >> I put the picture up, so I sent the link, hopefully it will get me some Amazon affiliate money back. I don't know. >> The t-shirts match the buyer's journey. >> Of course. >> Of course, as a marketer, of course. >> Stop chasing your tail dash f. You got to connect to your logs and always keep watching. >> Before we let you go, let's get a plug in for splunk.conf. The Cube has been going, I think this will be our fifth or sixth year, I can't count that high, I'm out of fingers and toes. >> Eighth. >> Your eighth, our sixth there, I think. >> There you go, you're a regular. >> So where is it, what's the highlights this year? It's always a great event. >> Much like AWS, we're doing events all across the world all the time. We have a series called Splunk live, we just did one in San Francisco last week which are super great ways to come and learn about the product and get hands-on keyboard to improve your skills, but it all culminates in .conf which is our leading event in the category. It's going to be in D.C. this year, September 25th to 28th and that's the best place to come, learn about Splunk, get hands-on with the product, meet the product team, learn from your peers, which to me, is the thing that matters the most. To see all the innovative ideas that everyone is doing, 'cause one of the great things about Splunk is the use cases for the product are basically infinite, and so you hear more and more stories, whether it's the city of San Francisco or shazaam or Yelp or Gatwick or thousands of others and .conf is the place, so you guys are going to be there, I'm going to be there, which is the reason everyone should come, obviously. >> Exactly, t-shirts for all. >> Brian: T-shirts for everybody. >> Well, Brian Goldfarb, CMO of Splunk, I got that right this time, thank you so much. >> Brian: And the Cube. >> And the Cube, apparently. (laughs) >> Jeff: Watch out, John, we've got a new CMO. >> Lisa: Thank you so much for joining us. Great, your passion is evident, we wish you the best of luck and continued success in your role. For my co-host Jeff Frick, I'm Lisa Martin. We are live at the AWS Summit San Francisco. Stick around, we'll be right back. (upbeat music) Is changing and this entire process, you started to mention a little bit, how is-- (upbeat music)

>> Announcer: Live from San Francisco, it's the Cube, covering AWS Summit 2017. Brought to you by Amazon Web Services. >> Hi, welcome to the Cube. We are live in San Francisco, at the Amazon Web Services Summit, 2017 AWS Summit. I'm Lisa Martin, with my co-host Jeff Frick. We've got George Gilbert here, as well. Packed house here. We all just came from the keynote, where there were some fantastic announcements. Lot of passion, Dr. Werner Vogels, the CTO and VP at AWS did a fantastic keynote, and some of the themes that I heard, guys, were really customers, customers, customers. We know how obsessed AWS is with customers. A lot of great announcements, all really substantiated by phenomenal customers from enterprise, startups, public sector. We've obviously seen how quickly they've been innovating. They've done a fantastic job turning this first mover status into sustained market leadership. What are some of the things, Jeff, that really kind of caught your eye in the CTO's keynote this morning? >> Lisa, the thing that I was actually taken back to Tuesday night with James Hamilton at AWS re:Invent, which if you are not going to re:Invent, you should register just for that. And really, the idea is that scale just trumps everything. And because Amazon has so many customers in so many areas, they can apply such scale to all their infrastructure, across such a broad array of services. I mean, all the slides that Werner kept popping up had so many little squares, 'cause they have so many services, so if you need fast I/O, you need fast compute, you want facial recognition, you want machine learning, they have a set of services for you. So a lot of people talk about the application-centric view of the world, but Amazon is actually delivering that to people, and they had Nextdoor app as kind of their showcase customer where they focus on the application, because Amazon does the rest, and now I thought it was interesting now they're moving into the development sphere. So now you can do your native development in AWS. Again, use that set of services that most apply to the applications you're building, and focus on your application and your customer. I mean, how do you compete with the scale? And who wants to compete in infrastructure scale if you're a company that's building a web-centric or native application? The other thought I think was interesting, at the beginning, he had his NASCAR slides, his logo slides, went through the startups, great, went through the enterprises, great, went through public sector, great, went through ISVs, great, went through system integrators, great. I mean, the ecosystem is phenomenal. So again, James Hamilton, I just love his talks, but the amount of resources he can apply to his business problems, compared to any individual company, it's just, you can't even compare. What'd you think, George? >> I look at the capabilities, the top three vendors are providing. You know, Amazon, Azure, and Google. And they each bring some different strengths to bear. Google is still building out for commercial access to services that they built internally for their own use. So you have what's a spectacular relational database that's globally distributed, called Spanner, but it's not actually something that commercial customers are used to. That's was built really for Google internal gurus. Now, it's in many ways better than anything that commercial developers have access to, but it's a bit of a migration hurdle in terms of learning. So, now, Amazon took, they took their internal infrastructure, but they built it so much differently. It wasn't meant to sort of stretch the capabilities of their internal developers and external developers. But they've been getting richer over time. Let's just use an example of a product that got significantly enhanced today. Redshift, which with, they called it Spectrum. Redshift used to be a traditional MPP data warehouse, and its data was tied into the same servers, or nodes, as the compute analytic functions. And so it was not that elastic, it was almost like a on PRIM product ported to the cloud, but they've been improving it, and today, there was a huge step forward where they put the storage on S3, which is completely separate from the sequel, Compute. And so now they go from what was essentially data warehouse that can max out at two petabytes to something that can go to the exabyte range. And because the data's on a cheap S3 storage, you can spend the compute down, and then you're just essentially paying for archive. So that's something that now looks more like Snowflake which was the best in class cloud data warehouse up until this point. Now there, I'm sure, are many other differences. But Amazon has that iteration to taking more and more advantage of taking what were conventional products and turning them into, you know, cloud ready services. >> You mention the re:Invent show last November. 32,000 attendees, sold out. >> Right, right. >> Lisa: And then 50,000 watching the livestream of the keynote. Andy Jassy was on the Cube talking with John and one of the things that I found interesting about that and also, some recent press that Andy has done is talking about how, which they're normally very customer focused, and the theme today was customer obsession, which I think we saw with all those logos up there. But they talk about, they don't really talk about competition. What, one of the things that I found interesting was that Andy has talked recently about them being six to seven years ahead of their competition. We see them continue to innovate. Add capabilities, add technology integrations. Jeff, you mentioned the ecosystem partners growing. We've had a number of them on the show today. They're so far ahead of competitors. And kind of going off what you said about Google, George. Amazon is now starting to productize some of the technologies like Amazon Connect that was announced last month, a virtual call center, that they use in-house, which is something we hadn't seen from a Google yet. >> That's a great point. And that was actually one of the differences, that I didn't get to sort of talking database. But both companies or all, Amazon, Azure, Google, IBM, all have really advanced machine learning, essentially engines and algorithms. But what makes machine learning really useful as the data is when you combine those with the data that trains those algorithms. And that's what makes essentially application ready services. Otherwise it's just tooling. And Google can leverage its data for, from search, from voice search, from video and image recognition with YouTube. So it has a bunch of machine learning services that are good for a conversational user interface and a visual user interface, but what Amazon is going... Amazon is leveraging the Alexa and Echo product to get, to train their natural language understanding and speech to text, text to speech. So that was added to today. But the thing that they're doing that's really interesting that Google and Microsoft can't yet is they're taking the machine learning capabilities that they use for fashion merchandising, price optimization, fulfillment, and they're going to be taking those and putting them out on AWS for developers to use just the way they their compute and basic software middleware and put them out for other companies to use. So in other words, they're going to take some of their core, most core mission critical, machine learning capabilities and open them up for others. But the key thing is they're trained on Amazon data so that they're immediately useful by corporate developers, not data scientists. And that's something in those areas where Amazon's unique. Every cloud vendor will have their, you know, areas of data where they can make it accessible to corporate developers. But Amazon has a unique set. >> And the other thing we talk often about, founder-led companies. And I think the culture thing, it just can't be overstated. Recently, Jeff Bezos says day one, you know, kind of internal memo is making the rounds again on social media. So I took a minute to reread it and you know, we talk often on the key of are we in the first inning, are we in the second inning or the third inning of whatever trend we happen to be covering, and I think his attitude that it's always day one is pretty significant. And you can't bet against the guy. That's why I love to say never bet against Bezos, 'cause he's got a vision, he's got to execute it. And the team that he's put in place with Andy, you know, it's just a quiet execution. Like you said, they don't really look at the competition. That's not who they're competing against. Werner said it today. They're competing against time. And their customers are competing against time. And I thought the examples again, from the keynote of next door about the time compression for all the various processes in their company were giant, which allow again, better application development. It allows their customers to better serve their customers. And I don't think that can be really overstated. And you don't get that as much in Google, where you know, Google Cloud is a different thing and they brought in new leadership. Obviously Satya has done a hell of a job turning Microsoft around from what it was before. But you know, you just see this quiet, confident execution within AWS team that I think is pretty special. >> There's one thing... Oh, sorry Lisa, let me just add on that execution point and the lead that they have over the competition. Internally, Andy Jassy tells his team there's no compression algorithm for a lead time of six years. It's not like just because Azure got started a little bit later, and they know what things are going to look like sooner because they can see the future before Amazon had to wait ten years to get there. That, you still have to go through that learning curve. And in other words what he's trying to say is their lead is, it's not, they can maintain their lead just by continuing to execute that flywheel affect that Jeff was talking about. >> Right and they continue to innovate. One of the things that I know that Jeff, you and John, George, have been following AWS since symphysis ten years ago. And they continue to innovate, they continue to add integrations. One thing that I was particularly interested in and just doing some prep for today's event is what they announced with VMware a few months ago. VMware vSphere base cloud services. Is that a... Couple things. Is that a foray to be able to bring VMware legacy customer applications into the cloud? Is that maybe a step towards saying hey, we're ready to start taking our customers to hybrid cloud? I'm curious to hear from some of our guests today what they think the next steps are. It wasn't talked about in the keynote but if you talk about competition, or rather growth, one of the areas that they've really excelled in obviously with the developer community and the start-ups, where they started is in greenfield, right. They have a great rich set of application developmentals, ideal for cloud development, ideal for greenfield. If you look at the legacy application space, you might think Microsoft, IBM, do they have an advantage there. But now what Amazon's doing in hopefully later this year with VMware is that, a bat signal. That hey, we're ready to take these customers and their legacy applications into the cloud as a competitive signal or really as a signal to hey, customers, we're ready to take you to the hybrid cloud. What are your thoughts on that? >> I guess that they started with start-ups. They were the ones who were the most demanding on the infrastructure because they were greenfield apps. And so there was, you know, they needed to go beyond the constraints of legacy systems. And in fact, Satya Nadella said of Azure, we need our Netflix, which was, you know the lighthouse customer for Amazon that was always pushing the envelope of what was possible. What's happening now though is that there was this journey that I just want to touch on that there was a pre-brief yesterday about the sort of the typical customer journey where they start with dev test workloads, then they go to new greenfield apps, then digital experience and user experience, then analytics and mobile. And what's now happening is that we're getting to the mission critical systems. And that's why like we heard a lot on database issues. 'Cause that's where, application databases are the foundation of mission critical apps. >> Speaking of that, I think, well we're really excited. We have a great guest line up today. We've got Splunk's CMO on the show. We've got a number of ecosystem partners. Datadog as well, so guys I think it's going to be a fantastic day. A lot to talk about. Really excited to hear about a lot of the innovation, the evolution that's going on and where these partners are going to be able to take their customers next. So, you're watching the Cube. Again, we're live at the Amazon Web Services Summit in San Francisco for George Gilbert and Jeff Frick, I'm Lisa Martin. Stick around, we'll be right back. (techno music)