(upbeat music) >> Hello and welcome today's session for the AWS Startup Showcase, the next big thing in AI, Security and Life Sciences featuring Coralogix for the AI track. I'm your host, John Furrier with theCUBE. We're here we're joined by Ariel Assaraf, CEO of Coralogix. Ariel, great to see you calling in from remotely, videoing in from Tel Aviv. Thanks for coming on theCUBE. >> Thank you very much, John. Great to be here. >> So you guys are features a hot next thing, start next big thing startup. And one of the things that you guys do we've been covering for many years is, you're into the log analytics, from a data perspective, you guys decouple the analytics from the storage. This is a unique thing. Tell us about it. What's the story? >> Yeah. So what we've seen in the market is that probably because of the great job that a lot of the earlier generation products have done, more and more companies see the value in log data, what used to be like a couple rows, that you add, whenever you have something very important to say, became a standard to document all communication between different components, infrastructure, network, monitoring, and the application layer, of course. And what happens is that data grows extremely fast, all data grows fast, but log data grows even faster. What we always say is that for sure data grows faster than revenue. So as fast as a company grows, its data is going to outpace that. And so we found ourselves thinking, how can we help companies be able to still get the full coverage they want without cherry picking data or deciding exactly what they want to monitor and what they're taking risk with. But still give them the real time analysis that they need to make sure that they get the full insight suite for the entire data, wherever it comes from. And that's why we decided to decouple the analytics layer from storage. So instead of ingesting the data, then indexing and storing it, and then analyzing the stored data, we analyze everything, and then we only store it matters. So we go from the insights backwards. That allowed us to reduce the amount of data, reduce the digital exhaust that it creates, and also provide better insights. So the idea is that as this world of data scales, the need for real time streaming analytics is going to increase. >> So what's interesting is we've seen this decoupling with storage and compute be a great success formula and cloud scale, for instance, that's a known best practice. You're taking a little bit different. I love how you're coming backwards from it, you're working backwards from the insights, almost doing some intelligence on the front end of the data, probably sees a lot of storage costs. But I want to get specifically back to this real time. How do you do that? And how did you come up with this? What's the vision? How did you guys come up with the idea? What was the magic light bulb that went off for Coralogix? >> Yes, the Coralogix story is very interesting. Actually, it was no light bulb, it was a road of pain for years and years, we started by just you know, doing the same, maybe faster, a couple more features. And it didn't work out too well. The first few years, the company were not very successful. And we've grown tremendously in the past three years, almost 100X, since we've launched this, and it came from a pain. So once we started scaling, we saw that the side effects of accessing the storage for analytics, the latency it creates, the the dependency on schema, the price that it poses on our customers became unbearable. And then we started thinking, so okay, how do we get the same level of insights, because there's this perception in the world of storage. And now it started to happen in analytics, also, that talks about tiers. So you want to get a great experience, you pay a lot, you want to get a less than great experience, you pay less, it's a lower tier. And we decided that we're looking for a way to give the same level of real time analytics and the same level of insights. Only without the issue of dependencies, decoupling all the storage schema issues and latency. And we built our real time pipeline, we call it Streama. Streama is a Coralogix real time analysis platform that analyzes everything in real time, also the stateful thing. So stateless analytics in real time is something that's been done in the past and it always worked well. The issue is, how do you give a stateful insight on data that you analyze in real time without storing and I'll explain how can you tell that a certain issue happened that did not happen in the past three months if you did not store the past three months? Or how can you tell that behavior is abnormal if you did not store what's normal, you did not store to state. So we created what we call the state store that holds the state of the system, the state of data, were a snapshot on that state for the entire history. And then instead of our state being the storage, so you know, you asked me, how is this compared to last week? Instead of me going to the storage and compare last week, I go to the state store, and you know, like a record bag, I just scroll fast, I find out one piece of state. And I say, okay, this is how it looked like last week, compared to this week, it changed in ABC. And once we started doing that we on boarded more and more services to that model. And our customers came in and say, hey, you're doing everything in real time. We don't need more than that. Yeah, like a very small portion of data, we actually need to store and frequently search, how about you guys fit into our use cases, and not just sell on quota? And we decided to basically allow our customers to choose what is the use case that they have, and route the data through different use cases. And then each log records, each log record stops at the relevant stops in our data pipeline based on the use case. So just like you wouldn't walk into the supermarket, you fill in a bag, you go out, they weigh it and they say, you know, it's two kilograms, you pay this amount, because different products have different costs and different meaning to you. That same way, exactly, We analyze the data in real time. So we know the importance of data, and we allow you to route it based on your use case and pay a different amount per use case. >> So this is really interesting. So essentially, you guys, essentially capture insights and store those, you call them states, and then not have to go through the data. So it's like you're eliminating the old problem of, you know, going back to the index and recovering the data to get the insights, did we have that? So anyway, it's a round trip query, if you will, you guys are start saving all that data mining cost and time. >> We call it node zero side effects, that round trip that you that you described is exactly it, no side effects to an analysis that is done in real time. I don't need to get the latency from the storage, a bit of latency from the database that holds the model, a bit of latency from the cache, everything stays in memory, everything stays in stream. >> And so basically, it's like the definition of insanity, doing the same thing over and over again and expecting a different result. Here, that's kind of what that is, the old model of insight is go query the database and get something back, you're actually doing the real time filtering on the front end, capturing the insights, if you will, storing those and replicating that as use case. Is that right? >> Exactly. But then, you know, there's still the issue of customer saying, yeah, but I need that data. Someday, I need to really frequently search, I don't know, you know, the unknown unknowns, or some of the day I need for compliance, and I need an immutable record that stays in my compliance bucket forever. So we allowed customers, we have this some that screen, we call the TCO optimizer, that allows them to define those use cases. And they can always access the data by creating their remote storage from Coralogix, or carrying the hot data that is stored with Coralogix. So it's all about use cases. And it's all about how you consume the data because it doesn't make sense for me to pay the same amount or give the same amount of attention to a record that is completely useless. It's just there for the record or for a compliance audit, that may or may not happen in the future. And, you know, do the same with the most critical exception in my application log that has immediate business impact. >> What's really good too, is you can actually set some policy up if you want a certain use cases, okay, store that data. So it's not to say you don't want to store it, but you might want to store it on certain use cases. So I can see that. So I got to ask the question. So how does this differ from the competition? How do you guys compete? Take us through a use case of a customer? How do you guys go to the customer and you just say, hey, we got so much scar tissue from this, we learned the hard way, take it from us? How does it go? Take us through an example. >> So an interesting example of actually a company that is not the your typical early adopter, let's call it this way. A very advanced in technology and smart company, but a huge one, one of the largest telecommunications company in India. And they were actually cherry picking about 100 gigs of data per day, and sending it to one of the legacy providers which has a great solution that does give value. But they weren't even thinking about sending their entire data set because of cost because of scale, because of, you know, just a clutter. Whenever you search, you have to sift through millions of records that many of them are not that important. And we help them actually ask analyze their data and work with them to understand these guys had over a terabyte of data that had incredible insights, it was like a goldmine of insights. But now you just needed to prioritize it by their use case, and they went from 100 gig with the other legacy solution to a terabyte, at almost the same cost, with more advanced insights within one week, which isn't in that scale of an organization is something that is is out of the ordinary, took them four months to implement the other product. But now, when you go from the insights backwards, you understand your data before you have to store it, you understand the data before you have to analyze it, or before you have to manually sift through it. So if you ask about the difference, it's all about the architecture. We analyze and only then index instead of indexing and then analyzing. It sounds simple. But of course, when you look at this stateful analytics, it's a lot more, a lot more complex. >> Take me through your growth story, because first of all, I'll get back to the secret sauce in the same way. I want to get back to how you guys got here. (indistinct) you had this problem? You kind of broke through, you hit the magic formula, talking about the growth? Where's the growth coming from? And what's the real impact? What's the situation relative to the company's growth? >> Yeah, so we had a first rough three years that I kind of mentioned, and then I was not the CEO at the beginning, I'm one of the co founders. I'm more of the technical guy, was the product manager. And I became CEO after the company was kind of on the verge of closing at the end of 2017. And the CTO left the CEO left, the VP of R&D became the CTO, I became the CEO, we were five people with $200,000 in the bank that you know, you know that that's not a long runway. And we kind of changed attitudes. So we kind of, so we first we launched this product, and then we understood that we need to go bottoms up, you can go to enterprises and try to sell something that is out of the ordinary, or that changes how they're used to working or just, you know, sell something, (indistinct) five people will do under $1,000 in the bank. So we started going from bottoms up, and the earlier adopters. And it's still until today, you know, the the more advanced companies, the more advanced teams. This is our Gartner friend Coralogix, the preferred solution for Advanced, DevOps and Platform Teams. So they started adopting Coralogix, and then it grew to the larger organization, and they were actually pushing, there are champions within their organizations. And ever since. So until the beginning of 2018, we raised about $2 million and had sales or marginal. Today, we have over 1500, pink accounts, and we raised almost $100 million more. >> Wow, what a great pivot. That was great example of kind of getting the right wave here, cloud wave. You said in terms of customers, you had the DevOps kind of (indistinct) initially. And now you said expanded out to a lot more traditional enterprise, you can take me through the customer profile. >> Yeah, so I'd say it's still the core would be cloud native and (indistinct) companies. These are typical ones, we have very tight integration with AWS, all the services, all the integrations required, we know how to read and write back to the different services and analysis platforms in AWS. Also for Asia and GCP, but mostly AWS. And then we do have quite a few big enterprise accounts, actually, five of the largest 50 companies in the world use Coralogix today. And it grew from those DevOps and platform evangelists into the level of IT, execs and even (indistinct). So today, we have our security product that already sells to some of the biggest companies in the world, it's a different profile. And the idea for us is that, you know, once you solve that issue of too much data, too expensive, not proactive enough, too couple with the storage, you can actually expand that from observability logging metrics, now into tracing and then into security and maybe even to other fields, where the cost and the productivity are an issue for many companies. >> So let me ask you this question, then Ariel, if you don't mind. So if a customer has a need for Coralogix, is it because the data fall? Or they just got data kind of sprawled all over the place? Or is it that storage costs are going up on S3 or what's some of the signaling that you would see, that would be like, telling you, okay, okay, what's the opportunity to come in and either clean house or fix the mess or whatnot, Take us through what you see. What do you see is the trend? >> Yeah. So like the tip customer (indistinct) Coralogix will be someone using one of the legacy solution and growing very fast. That's the easiest way for us to know. >> What grows fast? The storage, the storage is growing fast? >> The company is growing fast. >> Okay. And you remember, the data grows faster than revenue. And we know that. So if I see a company that grew from, you know, 50 people to 500, in three years, specifically, if it's cloud native or internet company, I know that their data grew not 10X, but 100X. So I know that that company that might started with a legacy solution at like, you know, $1,000 a month, and they're happy with it. And you know, for $1,000 a month, if you don't have a lot of data, those legacy solutions, you know, they'll do the trick. But now I know that they're going to get asked to pay 50, 60, $70,000 a month. And this is exactly where we kick in. Because now, when it doesn't fit the economic model, when it doesn't fit the unit economics, and he started damaging the margins of those companies. Because remember, those internet and cloud companies, it's not costs are not the classic costs that you'll see in an enterprise, they're actually damaging your unit economics and the valuation of the business, the bigger deal. So now, when I see that type of organization, we come in and say, hey, better coverage, more advanced analytics, easier integration within your organization, we support all the common open source syntaxes, and dashboards, you can plug it into your entire environment, and the costs are going to be a quarter of whatever you're paying today. So once they see that they see, you know, the Dev friendliness of the product, the ease of scale, the stability of the product, it makes a lot more sense for them to engage in a PLC, because at the end of the day, if you don't prove value, you know, you can come with 90% discount, it doesn't do anything, not to prove the value to them. So it's a great door opener. But from then on, you know, it's a PLC like any other. >> Cloud is all about the PLC or pilot, as they say. So take me through the product, today, and what's next for the product, take us through the vision of the product and the product strategy. >> Yeah, so today, the product allows you to send any log data, metric data or security information, analyze it a million ways, we have one of the most extensive alerting mechanism to market, automatic anomaly detection, data flustering. And all the real law, you know, the real time pipeline, things that help companies make their data smarter, and more readable, parsing, enriching, getting external sources to enrich the data, and so on, so forth. Where we're stepping in now is actually to make the final step of decoupling the analytics from storage, what we call the datalist data platform in which no data will sit or reside within the Coralogix cloud, everything will be analyzed in real time, stored in a storage of choice of our customers, then we'll allow our customers to remotely query that incredible performance. So that'll bring our customers away, to have the first ever true SaaS experience for observability. Think about no quota plans, no retention, you send whatever you want, you pay only for what you send, you retain it, how long you want to retain it, and you get all the real time insights much, much faster than any other product that keeps it on a hot storage. So that'll be our next step to really make sure that, you know, we're kind of not reselling cloud storage, because a lot of the times when you are dependent on storage, and you know, we're a cloud company, like I mentioned, you got to keep your unit economics. So what do you do? You sell storage to the customer, you add your markup, and then you you charge for it. And this is exactly where we don't want to be. We want to sell the intelligence and the insights and the real time analysis that we know how to do and let the customers enjoy the, you know, the wealth of opportunities and choices their cloud providers offer for storage. >> That's great vision in a way, the hyper scalars early days showed that decoupling compute from storage, which I mentioned earlier, was a huge category creation. Here, you're doing it for data. We call hyper data scale, or like, maybe there's got to be a name for this. What do you see, about five years from now? Take us through the trajectory of the next five years, because certainly observability is not going away. I mean, it's data management, monitoring, real time, asynchronous, synchronous, linear, all the stuffs happening, what's the what's the five year vision? >> Now add security and observability, which is something we started preaching for, because no one can say I have observability to my environment when people you know, come in and out and steal data. That's no observability. But the thing is that because data grows exponentially, because it grows faster than revenue what we believe is that in five years, there's not going to be a choice, everyone are going to have to analyze the data in real time. Extract the insights and then decide whether to store it on a you know long term archive or not, or not store it at all. You still want to get the full coverage and insights. But you know, when you think about observability, unlike many other things, the more data you have many times, the less observability you get. So you think of log data unlike statistics, if my system was only in recording everything was only generating 10 records a day, I have full, incredible observability I know everything that I've done. what happens is that you pay more, you get less observability, and more uncertainty. So I think that you know, with time, we'll start seeing more and more real time streaming analytics, and a lot less storage based and index based solutions. >> You know, Ariel, I've always been saying to Dave Vellante on theCUBE, many times that there needs to be insights as to be the norm, not the exception, where, and then ultimately, it would be a database of insights. I mean, at the end of the day, the insights become more plentiful. You have the ability to actually store those insights, and refresh them and challenge them and model update them, verify them, either sunset them or add to them or you know, saying that's like, when you start getting more data into your organization, AI and machine learning prove that pattern recognition works. So why not grab those insights? >> And use them as your baseline to know what's important, and not have to start by putting everything in a bucket. >> So we're going to have new categories like insight, first, software (indistinct) >> Go from insights backwards, that'll be my tagline, if I have to, but I'm a terrible marketing (indistinct). >> Yeah, well, I mean, everyone's like cloud, first data, data is data driven, insight driven, what you're basically doing is you're moving into the world of insights driven analytics, really, as a way to kind of bring that forward. So congratulations. Great story. I love the pivot love how you guys entrepreneurially put it all together and had the problem your own problem and brought it out and to the to the rest of the world. And certainly DevOps in the cloud scale wave is just getting bigger and bigger and taking over the enterprise. So great stuff. Real quick while you're here. Give a quick plug for the company. What you guys are up to, stats, vitals, hiring, what's new, give the commercial. >> Yeah, so like mentioned over 1500 being customers growing incredibly in the past 24 months, hiring, almost doubling the company in the next few months. offices in Israel, East Center, West US, and UK and Mumbai. Looking for talented engineers to join the journey and build the next generation of data lists data platforms. >> Ariel Assaraf, CEO of Coralogix. Great to have you on theCUBE and thank you for participating in the AI track for our next big thing in the Startup Showcase. Thanks for coming on. >> Thank you very much John, really enjoyed it. >> Okay, I'm John Furrier with theCUBE. Thank you for watching the AWS Startup Showcase presented by theCUBE. (calm music)

(upbeat music) >> Well, hello everyone, John Walls here on theCUBE as we continue our CUBE conversations as part of the AWS Startup Showcase with Ariel Assaraf who is the CEO and co-founder of Coralogix based in Tel Aviv. And Ariel, thanks for joining us, especially under these trying circumstances I'm sure many people watching fully appreciate what's going on in Israel right now with the bombings that are happening on a perpetual basis. And I just hope you and family, friends and your coworkers are doing well and staying as safe as possible. >> Thank you very much, John. Yeah, this is a surreal period of time where we're in the office then occasionally going to the shelter for a couple minutes and then getting back to coding and planning. So yeah, thank you. >> Well, certainly take care and you're very much on our thoughts and in our hearts right now and we wish you all the well and safety. Let's talk about Coralogix though. This is obviously it's your baby and entering the wild world of data these days this exponential growth of data. You and I were talking about really the untapped potential of data a little bit early before the interview. So let's talk about maybe the genesis of Coralogix a little bit and why you came up with this concept and then the unique platform that you've now established to really help your clients make some sense of these vast reams of data that they have at their disposal. >> Yeah, I think that's a very interesting topic that a lot of companies are starting to now address each one with its own angle. We decided to go with the real-time streaming analytics approach. The problem starts with data growing exponentially like you mentioned, but it's not just growing exponentially it's growing faster than revenue. What happens is that, companies that are bound to the cost of data are getting to a point where their margins and our unit economics are being slaughtered by the amount of data that they need to analyze whether it's for BI or marketing, and certainly observability which is probably the largest data producer inside any organization. And what typically companies tend to do is to start cherry pick data. So they only collect relevant information or only collect areas or only collect specific servers or specific environments. And that causes that statistic you just mentioned from the MIT research, showing that 99.5 of the data remains untapped or unanalyzed. When we looked at it, we thought that, you want to monitor that data at a high level. You want to analyze it automatically or manually or visualize it with good performance. And so the approach that existed/exists in the market until today is to use storage tiers. But then you have to compromise the quality and the speed of analytics. And we chose instead of that, to unlike everyone that index and then analyze to ingest, analyze everything in real time, including the most stateful transformation and stateful analytics and only then store what matters that way giving broader coverage and allowing companies economically and also in terms of scale, to send everything get the full analytics layer that they need and basically improve both their businesses and their performance. >> Yeah, it sounds so sensible. It sounds so simple too. Right, we're just going to analyze data as it comes in real time, we'll make sense of it, we'll process that, we'll make it actionable and boom off we go. But obviously, as you know this is an extraordinarily complex series of operations occurring now especially in the microservices world, right? Because you have all these inputs and all these instances happening simultaneously in different environments. So untangle that for me a little bit in terms of microservices now, the complexity that that creates and your approach to that. >> Yeah. So two things that happen. One, there are more services in each company. Two, there are more versions uploaded to each service every day. So the world of CICB combined with the world of microservices creates a lot of uncertainty. On one hand that's great because you have less decoupling. You can go faster, you can be faster to market respond to the market faster. You can analyze data in specific units that allows you more flexibility and you can release a lot more. On the other hand, it gets much harder to triage, to figure out what specific microservice is causing a problem to monitor the communication between different microservices. And certainly to understand, what is the version that broke something? A lot of software problems come after upgrades or configuration changes. And these two factors together, they generate a lot of data that you need to start monitoring and analyze. Now, like you're saying, analyzing in real time that's been done in the past. That doesn't sound too complex but what happens is that the answer from real-time streaming was only applied to stateless things. Meaning, let me know when you see something. You see an event, send me an alert. You see a metric, send me an alert. What happens is that, it's still missing the longer term analytics. So it's some sort of an oxymoron to say, on one hand I'm doing real-time streaming on the other hand, I want to give you analytics that rely on a long-term state. Let me know if something happened more than it did last week. Let me know if something happened for the first time this month. Clustered a data based on a learning algorithm that learns the data continuously throughout the entire history of time. And this is Streama, the technology that we created that does the real-time streaming but also involves, components that store the state of the system at any given point in time. So while other solutions or other approaches use the storage as the state. So if I want to know what happened a week ago, I just go to the storage and see what's in there from last week. Now we hold the snapshot of state of everything relevant whether we automatically discovered or the customer defined it and make sure that our customers can go back in time and compare versions or compare matrix or compare graphs and see how specific versions affected specific microservices and how specific microservices affected their entire production systems. >> So where, or help me out here just in terms of cost efficiency, then now, if you kind of, you're not eliminating storage obviously, but you're kind of shifting responsibilities here or shifting process a little bit, right? And making it a little more accessible on a real-time basis. What kind of cost efficiencies do you get out of that, in terms of not having to go to storage for everything and dig everything out from a week or two weeks or a month ago? >> Yeah, that's a great question. So it affects multiple areas there. First of all, storage is one of the areas where you can least optimize because it is what it is besides compression that's been invented years ago and we're pretty much maxed out there. There's not a lot of waste to really save on storage. So what companies do they try to put it on lower tier storage, but then you lose performance. What we do is we bound ourselves only to CPU and CPU, when you do analytics, you can improve and optimize to the max and get to a point where you auto-scale analyze all data in real time, get better results and you can continuously improve your code and your microservices in a way that makes them more efficient. We're talking about roughly 70, 75% of savings when we compare that to the closest solution in our space. But it's more than that actually. We believe that at the end of the day the storage approach is not going to be a feasible because storage doesn't scale great, like any, you know, any CPU that you increase, you get better performance, you get faster performance, you get more power. But when you increase storage size when you store more data for a longer term you actually lose performance. It's actually slower, it's more cluttered. And so what happens is that companies that need long-term analytics one, they have to use the storage. They can do it in real time, but two, they also have to have that storage stored for a very long period of time. So it exponentially grows. And we believe that we'll come to an era because data grows exponentially and many of our users are engineers that understand exponential growth. It's going to get to a point where it's almost impossible to write all the data to the disk and then companies are going to need to compromise again. So we feel that the market is going to a place where you'd like to get the analytics taken out of the data and only relevant information for the analytics being stored because the matrix and the logs and the traces they are a means to an end. They're not the purpose for which we are actually generating and storing them. >> Right. And that's what your clients are all about too. Right? Get me the need, you know, get me the gold, the data, you know, that I actually need and help me separate the wheat from the chaff here. What about AWS? How did they come into play? Or what about your relationship with them and how has that developed and currently where does that sit? >> Yes. So we actually moved to AWS about two years ago and moved our entire production and built it on the AWS infrastructure. Our infrastructure is entirely on Kubernetes. We're using Terraform and we have our own CI/CD tool that we actually released as an open source. And we scaled on AWS massively and started seeing the opportunities with most of our customers being on AWS. So we partnered with AWS partnerships teams. We went through the competencies the well-architected, the accelerate program. And now the relationship is at a level where our sales teams are working closely together with the AWS account managers to spot opportunities where AWS customers need an additional layer of analytics or better cloud security or cost reduction. And we're working together to find them that solution. Now to make it easier and more seamless for AWS customers to use us, we are onboarded to the AWS marketplace. So we're under the unified agreement of AWS and we can be paid through the AWS bill. So now Coralogix can be seen as an AWS service that you're using you don't have to use another vendor and you can get additional insights and lowered costs and 24/7 support that we provide. So that's how we partner with AWS. And of course, a lot of joint marketing and content activity. So we're running a webinar together with AWS teams at a general, not about us. In general, how can we give back to the community? How do you scale? For instance, we ran a webinar on how do you scale Kafka? Which is certainly not our domain, but definitely an issue that we had to handle and had to scale and it's a pain point for many AWS customers. So we're trying to give back, we're a lot from AWS and we are partnering with them to solve problems together. >> So what's it done for you then at Coralogix? So you said it's been a two year relationship so it's matured obviously and you've worked out something very nice. You're leveraging each other's strengths, you know, in a very smart and tactical way. So what does it mean to you though Coralogix and ultimately, what do you think it means to your end user, your client base when you bring the kind of this combined power into their needs? >> Yeah. So for us working together with AWS means that they help us where a startup is lacking the most strength. So startups, they can be extremely fast they can develop cutting edge technologies they can bring new approaches and products to the market. But when you start working with the larger organizations the most hardest part of a POC because the engineering teams see the value immediately is the procurement is the legal parts is getting there opening the door and showing them the value proposition that you have and working together with AWS allows us to first of all, meet these customers, understand their needs and then being able to route through the AWS marketplace. And of course, to make it easier for them. We created like 20 different plugins to all AWS services so they can seamlessly connect all their data. Cause you remember one of the things that we wanted to get to is people not having to cherry pick logs. We're not having to cherry pick matrix. So now they can connect their entire environment and get full cloud observability and security within minutes and do it in an economic way. >> Wait, you're talking about all these capabilities and providing the client base and obviously this is a field that we're talking about data and what you're doing with it that's growing so rapidly. What does it mean to you like inside your office there in terms of, do you have enough space for people? I assume your growth trajectory is pretty impressive right now. >> Yes. This is, it's something that we are trying to learn now. This is a third office in three years and we're now outgrowing this one and going to the next one. So we grew from about 10 people, two years ago when we moved to AWS to over a hundred people now and continuing to hire in East, center, West US and in Israel and in London and in India. And the company is going to double itself within the next few months. So it's definitely, you know, a challenge now with COVID era also, but thank God, you know here in Israel, we're kind of past that. And it seems like the US is going to be past that in the next few months. So we're going to get back and start hiring and growing the teams. >> Well, it sounds impressive. And congratulations on that particular aspect of your business. I know it's always fun to bring on new people. It's all a very positive sign. So congratulations on that front. Thank you for the time today. And most importantly, again, we do wish you a great health and wellness and safety given that all that's going on right now and our hopes and prayers are that it ends as quickly as possible and you can return back to business as usual there. >> Thank you very much, John. I appreciate your time. >> Thank you sir. >> You bet. My pleasure. Once again, we're talking about Coralogix here, on theCUBE Conversation as part of the AWS Startup Showcase with Ariel Assaraf, who is the CEO and co-founder. I'm John Walls. Thanks for joining us here on theCUBE. (upbeat music)

>> Live from Las Vegas, it's theCUBE Covering Informatica World 2019 Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019 here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Ariel Kelman. He is the VP, Worldwide Marketing at AWS. Thank you so much for coming on theCUBE. >> Thanks so much for having me on today. >> So let's start out just at ten thousand feet and talk a little bit about what you're seeing as the major cloud and AI trends and what your customers are telling you. >> Yeah, so I mean, clearly, machine learning and AI is really the forefront of a lot of discussions in enterprise IT and there's massive interest but it's still really early. And one of the things that we're seeing companies really focused on now is just getting all their data ready to do the machine learning training. And as opposed to also, in addition I mean, training up all their people to be able to use these new skills. But we're seeing tons of interest, it's still very early, but you know one of the reasons here at Informatica World is that getting all the data imported and ready is, you know, it's almost doubled or tripled in importance as it was when people were just trying to do analytics. Now they're doing machine learning as well. You know, we're seeing huge interest in that. >> I want to get into some of the cloud trends with your business, but first, what's the relationship with Informatica, and you know we see them certainly at re:Invent. Why are you here? Was there an announcement? What's the big story? >> I mean, we've been working together for a long time and it's very complementary products and number varies. I think the relationship really started deepening when we released Redshift in 2013, and having so many customers that wanted to get data into the cloud to do data we're housing, we're already using Informatica in, to help get the data loaded and cleansed and so really they're one of the great partners that's fueling moving data into the cloud and helping our customers be more successful with Redshift. >> Yeah, one of the things I really admire about you guys is that you're very customer centric. We've been following Amazon as you know since their, actually second reinvent, Cube's been there every time, and just watching the growth, you know, Cloud certainly has been a power source for innovation, SAS companies that are born in the cloud have exponentially scaled faster than most enterprises because they use data. And so data's been a heart of all the successful SAS businesses, that's why start ups gravitated to the Cloud right away. But now that you guys got enterprise adoption, you guys have been customer centric and as you listen to customers, what are you guys hearing from that? Because the data on premises, you've got more compliance, you've got more regulation, you've got-- news today-- more privacy and now you've got regions, countries with different laws. So the complexity around even just regulatory, nevermind tech complexity, how are you guys helping customers when they say, you know what, I want to get to the cloud, love Amazon, love the cloud, but I've got my, I've got to clean up my on param house. >> Yeah, I would say like a lot, if you look at a lot of the professional services work that we do, a lot of it is around getting the company prepared and organized with all their data before they move to the cloud: segmenting it, understanding the different security regulatory requirements, coming up with a plan of what they need, what data they're going to maybe abstract up, before they load it, and there's a lot of work there. And, you know, we've been focused on trying to help customers.. >> And is there a part in you're helping migrate to the cloud, is that.. >> Yeah, there's technology pieces, companies like Informatica helping to extract and transform and load the data and on data governance policies. But then also, for a lot of our systems integrator partners, Cognizant, Accenture, Deloitte-- they're very involved in these projects. There's a lot of work that goes on; a lot of people don't talk about just before you can even start doing the machine learning, and a lot of that's getting your data ready. >> So how, what are some of the best practices that have emerged in working with companies that, as you said, there's a lot of pre-work that needs to be done and they need to be very thoughtful about about sort of getting their data sorted. >> Well I think the number one thing that I see and I recommend is to actually first take a step back from the data and to focus on what are the business requirements of, what questions are you trying to answer, let's say with machine learning, or with data science advanced analytics, and then back out the data from that. What we see a lot of, you know companies sometimes will have it be a data science driven project. Okay, here's all the data that we have, let's put it in one place, when you may not be spending time proportionate to the value of the data. And so that's one of the key things that we see, and to come up-- just come up with a strong plan around what answers you're, what business questions you're trying to answer. >> On the growth of Amazon, you guys certainly have had great record numbers, growth, even in the double digit kind of growth you're seeing on top of your baseline has been phenomenal. Clearly number one on the cloud. Enterprise has been a big focus. I noticed that on the NHL, your logo's on the ice during the playoffs; you've got the Statcast. You guys are creating a lot of aware-- I see a lot of billboards everywhere, a lot of TV ads. Is that part of the strategy is to get you guys more brand awareness? What's the.. >> We're trying, you know, it's part of our overall brand awareness strategy. What we're trying to do is to help, we're trying to communicate to the world how our customers are being successful using our technology, specifically machine learning and AI. It's one of these things where so many companies want to do it but they say, well, what am I supposed to use it for? And so, you know, one of, if you dumb down what marketing is at AWS, it's inspiring people about what they can run in the cloud with AWS, what use cases they should consider us for, and then we spend a lot of energy giving them the technical education and enablement so they can be successful using our products. At the end of the day, we make money when our customers are successful using our products. >> One of the hot products was SageMaker, we see in that group, AI's gone mainstream. That's a great tail wind for you guys because it kind of encapsulates or kind of doesn't have to get all nerdy about cloud, you know, infrastructure and SAS. AI kind of speaks to many people. It's one of the hottest curriculums and topics in the world. >> Yeah, and with SageMaker, we're trying to address a problem that we see in most of our customers where the everyday developer is not, does not have expertise in machine learning. They want to learn it, so we think that anything we can do to make it easier for every developer to ramp up on machine learning the better. So that's why we came up with SageMaker as a platform to really make all three stages of machine learning easier: getting your data prepared for training, training in optimized models, and then running inference to make the predictions and incorporate that into people's applications. >> One of the themes that's really emerging in this conversation is the need to make sure developers are ready and that your people are skilled up and know what they need to know. How are, how is AWS thinking about the skills gap, and what are you doing to remedy it? >> Yeah, a couple things. I mean, we're really, like a lot of things we do, we'll say what are all the ways we can attack the problem and let's try and help. So, we have free training that we've been creating online. We've been partnering with large online training firms like Udacity and Coursera. We have an ML solutions lab that help companies prototype, we have a pretty significant professional services team, and then we're working with all of out systems integrators partners to build up their machine learning practices. It's a new area for a lot of them and we've been pushing them to add more people so they can help their customers. >> Talk about the conferences, you have re:Invent, the CORE conference, we've been theCUBE there. We've just also covered London, Amazon's Web Services summit, and 22,000 registered, 14,000 showed up. Got huge global reach now. How do you keep up with this? I mean it's a... >> Well we're trying to help our customers keep up with all the technology. I mean, really, we have about, maybe 25 or so of these summits around the world-- usually around two days, several thousand people, free conferences. And what we're trying to do is >> They're free? >> The summits are free and it's like, we introduce so much new technology, new services, deeper functionality within our exiting services, and our customers are very hungry to learn the latest best practices and how they can use these, and so we're trying to be in all the major areas to come in and provide deep educational content to help our customers be more successful. >> And re:Invent's coming around the corner. Any themes there early on, numbers wise? Last year you had, again, record numbers. I mean at some point, is Vegas too small >> Yeah, we had over 50,000 people. We're going to have even more, and we've been expanding to more and more locations around Las Vegas and you know we're going to keep growing. There's a lot of demand. I mean, we want to be able to provide the re:Invent experience for as many people as want to attend. >> What's the biggest skill set, you know the folks graduating this month, my daughter's graduating from Cal Berkeley, and a lot of others are graduating >> Congratulations >> high school. Everyone wants to either jump into some sort of data related field, doesn't have to be computer science, those numbers are up. What's your view of skill sets that are needed right now that weren't in curriculum, or what pieces of curriculum should people be learning to be successful if machine learning continues to grow from helping videos surface to collecting customer data. Machine learning's going to be feeding the AI applications and SAS businesses. >> Yeah, I mean look, you just forget about machine learning, you go to a higher level. There's not enough good developers. I mean, we're in a world now where any enterprise that is going to be successful is going to have their own software developers. They're going to be writing their own software. That's not how the world was 15 years ago. But if you're a large corporation and you're outsourcing your technology, you're going to get disrupted by someone else who does believe in custom software and developers. So the demand for really good software engineers, I mean we deal with all the time, we're hiring. It is always going to outstrip supply. And so, for young people, I would encourage them to start coding and to not be over reliant on the university curriculums, which don't always keep pace with, you know, with the latest trends. >> And you guys got a ton of material online too, you can always go to your site. Okay, on the next question around, as someone figures out, okay, enterprise versus pure SAS, you guys have proven with the Cloud that start ups can grow very fast and then the list goes on: AirBnB, Pinterest, Zoom Communications, disrupting existing big, mature markets by having access to the data. So how do you talk about customers when you say, hey, you know, I want to be like a SAS company, like a consumer company, leverage data, but I've got a lot of stuff on premise. So how do I not make that data constrained? How do you guys feel about that conversation because that seems to be the top conversation here, is you know, it's not to say be consumer, it's consumer-like. Leveraging data, cause if data's not into AI, there's no, AI doesn't work, right? So >> Right >> It can't be constrained by anything. >> Well, you know, you talk to all these companies and at first they don't even know what they don't know in terms of what is that data? And where is it? And what are the pieces that are important? And so, you know, we encourage people to do a good amount of strategy work before they even start to move bits up to the cloud. And of course, then we have a lot of ways we can help them, from our Snowball machines that they can plug in, all the way to our Snowmobile, which is the semi truck that you can drive up to your data center and offload very large amounts of data and drive it over to our data centers. >> One of the things that is trending-- we had Ali from Data Bricks talk about, he absolutely believes a lot of the same philosophies you guys do-- data in the cloud. And one of his arguments was is that there's a lot of data sets in these marketplaces now where you can really leverage other people's data, and we see that on cybersecurity where people are starting to share data, and Cloud is a better model for that than trying to ship drives around, and there's a time for Snowball, I get that, and Snowmobile, the big trucks for large ingestion into the cloud, but the enterprise, this is a new phenomenon. No one really shared a lot in the old days. This is a new dynamic. Talk about that, is it-- >> I mean, sharing, selling, monetizing data. If there's something that is important, there will be a market for it. And I think we're seeing that just the hunger, everything from enterprises to startups, that want more data, whether it's for machine learning to train their models, or it's just to run analytics and compare against their data sets. So I think the commercial opportunity is pretty large. >> I think you're right on that. I think that's a great insight. I mean, no one ever thought about data as a service from our data set standpoint, 'cause data sets feed machine learning. All right, so let's do, give the plug on what's going on with AWS. What's new, what's on your plate, what's notable. I mean I love the NHL, I couldn't resist that plug for you being a hockey fan. But what's new in your world? >> Um, you know, we're, we're in early planning stages on our re:Invent conference, our engineers are hard at work on a lot of new technology that we're going to have ready between now and our re:Invent show. You know, also we're, my team's been doing a lot of work with the sports organizations. We've had some interesting machine learning work with major league baseball. They rolled out this year a new machine learning model to do stolen base predictions. So, you can see on some of the broadcasts, as a runner goes past first base, we'll have a ticker that will show what the probability is that they'll be successful stealing second base if they choose to run. Trying to make a little more entertaining all those scenes we've seen in the past of the pitcher throwing the ball back to first, trying to use AI machine leaning to give a little bit more insight into what's going on. >> And that's the Statcast. Part of that's the Statcast >> That's Statcast, yeah >> And you got anything new coming around that besides that new.. >> Yeah, I think that yeah, major league baseball is hard at work on some new models that I think will be announced fairly soon. >> All right, to wrap up Informatica real quick, an announcement here, news coming I hear. How are you guys working with Informatica in the field? Is there any, can you share more about relationship >> Yeah I mean I think we're going to have an announcement a little bit later today, I mean it's around the subject we've been talking about: making it easier for customers to, you know, be successful moving their data to the Cloud so that they can start to benefit from the agility, the speed and the cost savings of data analytics and machine learning in the Cloud. >> And so when you're working with customers, I mean, because this is the thing about Amazon. It is a famously innovative, cutting edge company, and when you talk about the hunger that you describe, that these customers, isn't it just that they want to be around Amazon and kind of rub shoulders with this really creative, thinking four steps ahead kind of company. I mean how do you let your innovation rub off on these customers? >> I mean there's a couple ways We do, one of the things we've done recently is these innovation workshops. We have this thing we talk about a lot this working backwards process where we force the engineers to write a press release before we'll green light the product because we feel like if you can't clearly articulate the customer benefit, then we probably shouldn't start investing, right? And so we, that's one of the processes that we use to help us innovate better, more effectively and so we've been walk-- we walk customers through this. We have them come, you know there's an international company that I was, part of one of the efforts we did in Palo Alto last year where we had a bunch of their leadership team out for two days of workshops where we worked a bunch of ideas through, through our process. And so we do some of that but the other area is we try and capture area where we think that we've innovated in some interesting way into a service that then customers can use. Like Amazon Connect I think is a good example of it. This is our contact center call routing technology and you know, one of the things Amazon's consumer business is known for is having great customer support, customer service, and they spent a lot of time and energy making sure that calls get routed intelligently to the right people, that you don't sit on hold forever, and so we figure we're probably not the only company that could benefit from that. Kind of like with AWS, when we figure out how to run infrastructure securely and high performance and availability, and so we turn that into a service and it's become a very successful service for us. A lot of companies have similar contact center problems. >> As a customer, I can attest to being on hold a lot. Ariel, thank you so much for coming on theCUBE. It's been great talking to you. >> I appreciate it. Thank you. >> Thanks for coming out, appreciate it. >> I'm Rebecca Knight, for John Furrier. You are watching theCUBE. Stay tuned. (upbeat music)

>> Live from Orlando, Florida, it's theCUBE, covering .conf18. Brought to you by Splunk. >> Welcome back to Splunk .conf18, #splunkconf18. You're here watching theCUBE, the leader in live-tech coverage. My name is Dave Vellante, and I'm with my cohost, Stu Miniman, and we're going to take a cruise with the data. Curt Persaud is here. He's the director of IT for Guest Technology at Carnival Cruise Lines. So, he's the ship. And Ariel Molina is here. He's the Senior Director of web development and enterprise architecture at Carnival Cruise Line. He's the shore. Gents, welcome to theCUBE. Good to see you. >> Happy to be here. Very, very. >> Thanks for having us guys. >> Dave, I sea what you did there. (laughs) >> Yeah, Stu, it's pretty good, huh. Well, this is kind of, you know, Splunk is known for a little tongue in cheek. >> Alright, let's keep this interview on course. >> (laughs) Alright, you got it. So Arnold Donald, your CEO, was on stage today with Doug Merritt, a very inspirational individual. You guys have an amazing company. You see those ads and just go "wow." Just makes you want to go. But Ariel, let's start with you, your role, what you guys are doing here. Just kick it off for us. >> So, no, it's fantastic, great to be here. Great energy in the conference today. The keynote was fantastic. It was great to see our CEO up there and really represent our company, really talk about, sort of, where we're heading and how Splunk helps us along that journey when it comes to data. Things are changing, they're moving faster every day, right? We're pressured into delivering more value, delivering innovation at a faster pace, and Splunk is a key enabler of that, for us. >> And Curt, at any one point in time, you guys said you have like 250,000 guests on the seas around the world. Wow! And everybody wants to be connected these days. So that's kind of your purview, right? >> Yeah, absolutely. Five, 10 years ago, what sold cruises was the ability to be disconnected. Right now, people want to be connected more than ever. So what we try to do, beyond just the connectivity, and giving them better bandwidth, and stuff like that, was to try to develop products onboard that helps them be connected, be social, but not miss out on the product that we're actually selling, which is the ship, the people, the crew, and the actual entertainment and the staff onboard. So we're trying to make people social, but not anti-social with some of the technologies that we're bringing onboard, as well. >> Doug Merritt said today, "we're all data emitters." And I think the number was you guys will service 13 million guests in any given year? So a huge, huge number of data emitters. And of course, Ariel, you obviously are analyzing a lot of data, as well. So, how has the use of data changed over the years at Carnival? Maybe you could kind of take us through that. >> Well, ultimately I think it's about personalizing the experience. So, how do we use the data to better understand what folks are looking for in that guest journey? We call the guest journey everything from planning a voyage, purchasing a voyage, purchasing all the auxiliary items that are up for sale, and then ultimately making it into the ship. So, what we're doing these days, is looking at mining this data, and looking for opportunities. On the dot-com side of things, obviously it's about resiliency and personalization. How do we deliver innovation through multiple releases, and then do so in a resilient way? And a lot of those innovations, typically, are around personalization. And we see that move the needle. We're incentivized to have more folks book online. That's ultimately good for the bottom line. So, data's a big part of that. Personalization, resiliency. >> Yeah, it's one of those interesting things we look at. Most people probably think of cruise ships as you're vacation or transportation, everything like that. You're a technology company now. You're tied in, you've got multiple mobile apps, before and during. Maybe bring us a little bit inside what that's like. >> Over the past three years, we've seen a great transformation in terms of the technologies that we're bringing on board. You name it, whether it's very high end tools, like Splunk and other APM tools that we use, to cutting-edge technology like AI, chatbots, facial recognition. We're using the full breadth of all these innovations, in terms of technology, to try to enhance guest experience. And to Ariel's point, the focus is really on trying to be very personal, trying to personalize this information, trying to personalize the guest experience, and using all those data points that we're capturing to really target what a custom experience looks for you. It's really interesting, because one of the things that we try to do in that personalization is try to manage those micro-moments. We're trying to get you what you want, we're trying to get you the feedback that you need in that micro-moment, so that you can do your transaction and move on to enjoying your cruise. >> There's something that you mentioned. You want a balance. You want people to take advantage of what's there. You used to think of a vacation like this, you'd disconnect yourself. Help understand that balance. >> You'd be surprised. We were just recently on a cruise, my family and I, and we don't cruise as often as you would imagine. >> Because you work for the company. >> Even though, when you do, it feels good to be a customer, right? There's so much activity going on on a ship on a given day. It's very hard to understand where to be at a certain point in time, and some people find that overwhelming. What things like the app does is really allow you to curate your day. To say hey, you like music? Let's focus on events that are music-oriented and that's going to be in Location XYZ on the ship. And they're going to be sequenced. So, that's personalizing the experience. But it's also ensuring that folks are really taking advantage of the full product. >> From our perspective, the technology should be in the background. It's more complementary. The real product is really the ship, the crew members, the activities, the entertainment on board. That's the product we really want people to really connect to. The stuff that we do is auxiliary in terms of, let me help you maximize those experiences on board. And that's what we're really trying to do. If we can get that done and accomplished, than we have done our jobs. >> So the app is the digital conduit to the physical experience >> Exactly. >> If you have a good app, it makes all the difference in the world. If you're at Disney, and you're trying to figure out what's next, what do the lines look like? You get a lot of people on a ship, and you want to prioritize. You all call that curating your experience. It's all about the app, as they say. What's the state of the app? The 1.0 probably needed a little work. Where are you know in the evolution? >> We're in a 2.0 release version of it. The original version, we started with what we called the meat and potatoes. The very basic stuff, that hey, where can I get food? What is the entertainment lineup for the day? We started off with some innovation in terms of being able to generate, we did a chat, kind of like, communication, so people could chat with their families onboard without having to purchase a plan or have any bandwidth needs. And then, as we evolved that, then we started to go into things that are more transactional. So, you're able to purchase your photos digitally through the app. We leveraged facial recognition software, so that if a photographer on a ship takes a picture of you, it recognizes that as you and puts your photo in your photo stream and your photo album. So, very, very convenient. We do things like sell shore excursions in terms of transactional stuff. You can sit at the pool and say "oh, tomorrow's a port day, "I'm going to be in the Bahamas. "Let me see what shore excursion I want to do. And you can do it directly from the app without even moving. So now, as we evolve that now, as Ariel said, now we're trying to leverage all that data now, to go beyond the transactions, and make things even more personalized. So, I know that you favor the casino, maybe you're a spa person, you want a facial. We'll target you and say hey, on your previous cruise you did this. Let's target you because we might have something special waiting for you onboard. >> And then carry that across the journey, right. So now they leave our ships. And how do we get them to come back to our ships? How do you create that conversation that's ongoing, notifications about what's going on on our ships. People follow their favorite cruise director. People follow a lot of the unique experiences there. How do you bring that to the online, to the dot-com experience? So that when they're thinking about that next cruise, they can remember what that last cruise was about, and they can know what's happening on each one of our ships in real-time. It's a journey. And technology definitely is a huge enabler for us and the experience. >> So what's the data architecture look like on there? We always talk on theCUBE about the innovation sandwich of the future. It used to be Moore's Law, doubling every two years. Okay, great. Now, it's data, plus machine intelligence, and you scale with the cloud. What's your data architecture look like? >> Well, I think it's early days. I think it's, I mean, they're all over the place, right? I think there's silos within the enterprise that are really maximizing data. I think that that trend continues to happen. But I think there's got to be, and the enterprise architecture world is sort of about wrangling that, and figuring out how data from different dispersed touch points affect that. So, it's early days. I do think that you're starting to see that machine learning algorithms do play a part. I'm seeing it personally, more in the operations side of the world. So all these systems, at the end of the day, they need to be resilient and they need to have high service levels. So, what I'm seeing now is tools, and at Splunk, you saw that today, being able to be really predictive about where the anomalies are. Traditionally, you were having to log errors and then interpret errors, and then that would be the way you action some of these things. The predictive nature of some of these tools are such that you're being proactive. So when you talk about data there's so many different places you can go. If you think about our technology stack, and that guest experience point of view, it's all about really maintaining that SLA's, resolving issues as quickly as possible. And there's a ton of data in that space, right? I mean, it's everywhere, there's a ton of signals. >> Well you guys know, we tend not to throw stuff away in technology. You sort of have to figure out how to integrate. >> A signal via the customer is probably one of those, as well. So at the end of the day, what more information are we collecting about our guest to ultimately personalize that experience? It's centered around that. >> And that's challenging, I mean, look at the airlines. And your app, which you love the airline apps. I mean, you're not, like, tethered to them. But the phone experience, and even the laptop experience, are a little bit different. Because of the data, it's very, very challenging. Have you figured that out? Or are you sort of figuring that out? >> That's API's, right? It's that experienced API layer. Being able to activate that data which is sitting in distinct silos and then do so across those experience apps, the experience channels, which is dot-com, the app, the chatbot, there's so many interfaces out there. But, yeah, it's a solid, mature API strategy that's going to get us there. >> And I think one of the things that our challenge is, as technology partners, is the ability to build those platforms so that the next wave of conversions, as you mentioned, there's some disjointed experience across the desktop view versus the mobile view, is to try to bring those conversions together. And in order to that, like Ariel said, maybe making some API extraction layers figuring out how to mine the data better, figuring out how to leverage insights from different tools or machines and sensors, we have a ton of sensors on these ships as well. And bringing all those things together to be able to put us in a position that when we do finally get a seamless conversion, we're ready for it from a technology and a platform perspective. >> It's obvious why data is important for your business. You actually did a press release with Splunk. Maybe explain a little about how Splunk Cloud fits into this discussion that we've been having? >> Well, Cloud really removes the barriers of experimentation. How do you right-size a problem you don't understand very well? I think Cloud really helps with that. We're looking forward to being able to be flexible. Flexibility in architecture, flexibility in infrastructure. So that's absolutely the use-case I think security's got a number of use-cases. You see it every day in the news. So yeah, more opportunities, I would say, it scales that flexibility that's taken us the cloud route. >> When you think Splunk, you think security. You got guys in the Knock. That's not where you guys are. You're kind of closer to the business. And so you're seeing Splunk, as I said before, permeate into other parts of the organization. You kind of expected somebody else to do that. I don't know, the Hadoop guys. And it's interesting, Splunk never used to talk about big data. Now that the big data era is, sort of, behind us, Splunk talks a lot about big data. It's kind of an interesting flip. >> I would say it's democratizing the data. That's the stuff I liked, that I heard today. How do you get these tools away from the IT operators that are writing these complex queries to get insights? And how do you elevate that up to the analysts, and the product managers? And how do they get access to those interfaces? You know, drag-and-drop, whatever you want to call it. But I think that where I see this happening more so than, machine learning, that's great and predictive. But just empowering others to really leverage that data. I would say Splunk is leading there and it's good to see some of that stuff today. >> Absolutely. It's putting the power where it really needs to be, where it's the end users, the guys making decisions, it's the product owners, the product managers, that are making those slight tweaks to that interface, or to that design, or to that experience, that makes a difference. And that's what we're trying to do, and leverage with tools like Splunk, as well. >> Even the simple visualization, right, the stuff that's out of the box is really important for the business user, right? >> The out of the box part's another thing that I saw today, which is more, sort of, curating for particular use-case, and saying hey, we're going to build that end-to-end and really turn it on and activate it a little sooner. So that infrastructure product we saw today, I think that's a big step forward. Where you're a platform, but at some point you're going to have to start being a little more vertical in the way that you bring to market, the way that they did with security. >> And Doug talked about, you know, Doug Merritt, that is, talked about data is messy, and the messiest landscape is the data. And then he talked about being able to organize that data in the moment. So, I think about, okay, just put it in the, we like to call data ocean, right, and just capture it. But then having the tools to be able to actually look at it in whatever schema you want, when you want it, is a challenge that people have. My question is, did he describe it accurately? I think yes. But then, can you actually do that with this messy data? >> I think it's a great concept. I'm interested to see how that plays out going forward. But I think in our world, we have several use-cases where that makes sense. We have a very captive audience for seven to 10 days. So we really have a very limited amount of time to make a really good impression. So, it's not only about attracting first-time cruisers; it's trying to get a repeat cruiser. So that limited time frame that we have to leave a really lasting impression is very limited. So things like recovery, in terms of getting metrics or data real-time, and being able to act on it immediately. Say you had a bad experience at the sushi bar. If we're able to grab that information, whatever data points that allow us to understand what happened, and then do a quick recovery, we may have a guest for a repeat cruise. Those are the things that we're trying to do. And, if what Doug is saying is something that they've kind of solved, or are able to try to solve in a good way, that is very powerful for us as well, and we definitely see leverage in that. >> Last question, Ariel, you're saying off-camera it's kind of early days. What's the future hold? I mean, that's going to blow our minds. Blow our minds! >> Oh, it's the predictive thing, right? It's bringing you your favorite drink before you're ready to have it, or something. I don't know. The cruise line business, the travel and hospitality space is a very fun space to work in. We get to really see our guests enjoy the product. And us, as technologists, we get to see how technology moves the needle. Continued innovation, right? If you're in the development side of the world, challenging yourself to deploy more often, to deliver more value more often. And if you're on the data side, how to get aggregated, compile all this this data, for ultimately what we're looking for, which is to enhance the guest experience. >> I mean, that real-time notion that you were talking about Curt, you can see that coming together and completely transforming the guest experience. So guys, thanks so much for coming on theCUBE. It was great to have you. Congratulations on all your success and good luck. Alright keep it right there everybody, we'll be back at Splunk .conf18. You're watching theCUBE. Dave Vellante with Stu Miniman. we'll be right back! (upbeat music)

>>Hey, welcome back and roll here. Live in San Francisco for Amazon web services summit. that's the hashtag. Go on Twitter. Go to crowd chat.net/aws summit. Rolling a special crowd chat document in the conversation. According every tweet in that room, join that community. I'm John furry, the founder's Silicon angle. This is the cube, our flagship program. We go out to the events and extract the signal from the noise. I'm joined my cohost, Jeff Frick filling in for Dave Volante. Uh, Jeff, always hard to fill up with Dave a lot day. Um, who was on those per, it doesn't look good on you. I know you're from California and Ariel Ariel Kelman worldwide marketing lead head of worldwide. Margaret Amazon. Welcome back to the cube. Thanks for having me. You were here last less than that. Reinvent, um, kinda markets itself, the company. I mean, you just tried to features out there on stage and keep on pushing new. What we try and do is lean back and just like the customers' testimonials. Let me come on. >>Yeah, I mean we try and just focusing on educating our customers. About what our services are doing, how customers are using them, which is something they ask for a lot. And then, you know, go pretty light on the marketing. Most technical people don't like to be marketed to and they find our approach quite refreshing. >>And when you're in the lead, you don't need to really worry about too much layer. You've got some meat on the bone, you've got great use cases, you've got great technology and a market leader in cloud and you're forging it a new territory. So there is a new element in the enterprise now coming in where you guys are being attacked. Certainly in the market. Google had some moves this week, as you can see, know IBM is doing HP, Oracle, the list goes on and on. So okay, those guys are kind of putting up the seawall for the big innovation way that you guys have built. The question is will it last? And so there is people really moving quickly to Amazon. The customer uptake is pretty comprehensive. So I'd say it's mainstream. So now as you go to the enterprise, you've got to do some messaging, right? You gotta you gotta have the innovation message. So what is the core opportunity for you there? >>There's a couple of things in the enterprise I think, you know, first of all we're helping people save money. We have organizations like Dow Jones that predict they're going to save over a hundred million dollars essentially by shutting down data centers and moving more of their infrastructure to the cloud. But I think the real interesting part is how we make these companies more innovative that if we can lower the cost of using technology to roll out their new projects, then essentially we take the cost of experimentation and have it almost approach zero. So then now if you want to try something new, the costs of failure aren't so high that they prevent people from sticking their neck out of the line and trying new things. And so we see a lot of these companies are adopting us more heavily. Their culture is changing their employees or are excited about trying things because when they try something out, the cost of failure is a fraction of what it was before because they don't have to buy servers. >>Delta buys all this equipment, get data center space, they can try something quickly. If it works, great, they expand. If not, they don't have to live with all this expense that they tried it out. So it's increasing the pace of innovation and also allowing more people in the company to be able to try new things, involve technology because we're eliminating these gatekeepers where before if you get a project required a lot of money, a lot of infrastructure, think about the committees you have to go, all the justifications. But if anyone could go spin up these resources with self-service, totally changed the dynamics of who can innovate. >>Yeah. I mean the whole try before you buy the puppy dog close as they used to say in the sales tactics is, let me try it before you buy it. Yeah. Shadow it as the, legitimize the fact that for very little cost and collateral damage, as Andy talks about, you can get something up and running pretty quickly. So the old I, that'll never work. Comment. That's a killer phrase of innovation gets eliminated because, no, no, no, I already tried it. Here's the numbers. Is that, is that a big part of it too? >>I'm a little bit, I mean it's almost like we need a new term there. There's, you know, people talk about shadow it and what we typically see is that once you give the CIO the keys to the cloud infrastructure and you set up a governance approach where you can decide what people can do, how much money they can spend, what things they can try. Um, then you get the best of both worlds. You still have a vetted platform from a security perspective. You have governance controls and sure people doing the right thing, but then it doesn't have to say, no, sorry, you've got to wait in line. You got to wait till next year. Um, so that is the new model that we're seeing where you're seeing developers distributed across the organization and smaller official it departments, but more people doing it stuff in the company because everyone can have access to infrastructure when they go big on cloud, especially with AWS. >>And are they getting it? Are the corporate it guys getting it that this is a good thing for them and they can leverage this to actually add more value in the company and enable more at the end of the day. More ideas. Yeah, absolutely. The companies that we talked to, look, they've got a lot of questions. If you're a big organization, you want to know if we can meet your security requirements, your compliance requirements. Can you run a sends Alaska? Well look, we want to do two things. We want to run the software the last 20 years in the cloud. Can you help us with that? And then we want to build these new cloud native applications so we can be as agile and efficient as some of these new internet startups that now we're competing with. And so we spent a lot of time with them to talk through what it should do first, how I should think about it, what apps make sense to run on us and, and you know, more importantly with the sequences, which lady first us should ask us. Like we want to go, we've, we've played around, we've tested, we've had lots of developers using this for years, but now we want to go big. I having a material percentage of our infrastructure in the cloud so we can fundamentally change how it adds value to the business. And like those are the conversations we love having the customers. >>I want to ask you about just to show by, just to get, check this out. Check the box on the interview here because I want to make sure people can understand Amazon. Reinvent your mega show. That's your global conference. And why don't you explain, explain, reinvent versus the, >>sure. So the AWS summits, um, it's our three one day event, uh, that we do maybe like 14, 15 around the world. It's two purposes. One for people that are new to AWS, they can come in one day, get an overview of what it's about, how to use it and get inspired on what they can do with it. And then for our existing customers who are having users, they get an update on what's new, which may sound kind of tactical, but we released them, you gotta do stuff right. And so that's of my biggest challenge is how do we make sure that people know what all the new stuff is. They come here for one day, go to our keynote, go to a bunch of breakout sessions, do some training, and they get ramped up on everything we've done in the past year. Speaking of it, so we had you on last year and we were here. >>So what's been the big change from 2013 to 2014? I mean, we've had a lot of new services that we've released. We're going to new areas and think about Amazon workspaces. It's more of an it business application, right? Um, what you saw our demo today wasn't people coding. It was someone actually as an end user using, um, a virtual desktop on their iPad, on their computer. And so different types of applications, but we're, we're still going after that same goal, which is to allow these enterprise it organizations to take advantage of the cloud with more workloads. Essentially the larger percentage of their projects that they're doing that we can help them with, the happier they are with the relationship and the test, the test dev conversation seems to have simmer down quite a bit where it seemed like last year that was, and that was everybody's kind of testing waters. >>That's where you had initial traction, the initial shadowing it and that, that seems to really have dying down. And I mean, I think it's kind of gone mainstream or whatever is past mainstream where, you know, if you're a big SAP shop and your developers don't have their own SAP development environments, you're kinda, you're behind the curve. Same for Oracle, for SharePoint, if that's the new standard. Um, and so people don't talk about as much because they're already doing it. Right. It's, it's a, you know, the idea of well, you know, what are the big bets, um, you know, what should we use it for next? Should I do big data analytics using, um, like our Redshift product or should they build new high-scale web applications? Should this be my mobile infrastructure? That's where more of the conversation is coming on. Now >>Eric, I want to ask you about marketing and kind of one-on-one, you know, take me through the business school level marketing relative to your vision of Amazon and how the company's operating. I see Andy sets the tone up on stage, very customer centric. We hear all the people on Amazon talk about, Hey, we listened to the customer. They said they're tight on the messaging, they're really tight on the messaging. But you know, you starting to see, you know, tweets on the wild emerge. Like the new strategy for Amazon is price reduction as a service. And you know, it's like, so you seeing these messages come out. So is that, is that your plan to message just the price reduction to show the continuous improvement in terms of cost reductions and improvements in innovation and capability and just kind of be humble. >>So what, what our market organization is trying to do is to educate our customers in the easiest, most scalable way about what our services do, what are the best practices, how could they can use them and how they can save money near site. Andy talked about it a little bit earlier. We want our customers to feel like they're spending the least amount of money they need with us cause we want a longterm relationship and a price reductions. I mean it's probably one of the top three or four most boring parts of marketing AWS because every service team is trying to relentlessly take costs out of uh, their services. And when they get to a certain point, we pass those cost savings along to customers. It's kind of like clockwork two of them. Is that an internal metric for you guys? You guys all under pressure or mandated? >>That's just the DNA of the company. Let's get the cost out. Let's strap, distract away, cost and complexity. There's some bragging rights, little competition between the teams. How many price reductions have you done? I mean, it's a sign that they're being efficient and that they're making customers happy. It's a great metric. Price reduction and also feature increase. So again, now with flash, you start to see some new stuff hit the table. Yeah, that's part of the plan, right? Price reductions and more functioning. I mean the most, one of the most important parts of our overall strategy is to constantly innovate both on building new services, let people run more things in the cloud, but then also adding new functionality based on feedback we get from our customers. We'd like to release services relatively early versus sitting in an ivory tower trying to figure out what the perfect feature set is. >>We'll get this out early. Uh, get feedback from customers because you know, we're often surprised what people do with these services and uh, you know, they take on a life of their own. But ultimately that's how we get the best. You guys are like, you guys are like the big gorilla in the industry, but I was talking to someone last night at a VIP event, San Francisco, all these CEO of venture capitalists, Oh, Amazon, they loaded with money. You know, I'm like guys, they're like a lean startup. So that's pretty much the case. We've validated in talking to some folks, you guys are like a startup. I mean you're huge, you got great resources, but it's not like you're like Swoon and money thrown it around. You guys are very tight on budgets. You don't like just throwing around money. If you want to know about Amazon's culture, just type into Google, Amazon leadership principles. >>And there's about a, is it about a dozen or so core values? One of them is frugality. It's kind of, you know, part of how we operate the company and believe in what it means is that we only spend money on things that are useful to our customers. And that's a real good grounding. And then you see, we don't have 80 foot tall posters of our products or our executives here. You know, we spend the time on computers for people to do training and when we're planning events, we want to have everything focused on stuff that's useful to customers. We build the service too. We try and be relentless and driving cost out of our suppliers so we can pass on those costs and these customers. And it's just, you know, when you, um, when you operate a frugal fashion where you really think about costs, you end up being scrappy or, and you end up innovating more, it sends a good signal to your customer base because it's like a probably a laundry list of things that you guys have laid out then you still need to do and do innovate. >>Yes, exactly. If you wasting money on, you know, weirdness people that say, Hey, we didn't, why aren't they spending that energy on building new stuff? Exactly. Like we didn't 10 Howard street and close off the road to have a rock concert held companies. I mean, we have our crowd chat. Have you've seen that? We built that all on Amazon would not be possible without it. We hear testimony and testimonial customers saying, Hey, Amazon would have been 15 people minimum just to actually manage the gear on an offside without avatars. So yeah, it's just pretty massive. So, so with that, I got to ask you, the marketing question is how do you roll up all that Goodwill, Tony, when this great, great case study data you have? I mean referenceability it's not about, I mean, the number one marketing strategy we have is let our customers do the marketing for us. >>So I mean, part of why we do these events is to let our customers and people who are not customers yet interact with each other. And even when we have a reception and one of the best marketing strategies, if you have a product that people like is you combined your customers, your prospects and alcohol, and then they, you let them talk, right? You haven't asked questions. And that's how you get the relevant. Like, okay, you don't wanna believe our salespeople talk to our customers and really get a sense of what's going on. All right, there's too much smoke and mirrors. But these old guard hardware and software companies for much more open, much more transparent, um, because we believe in our, in our products and they're available for anyone. Anytime. It's almost like it's not even worth making up things that aren't true because anyone in the world can evaluate any of our services anytime they want. >>It's almost boringly boringly good. And you hear Andy talking about, well we did this for that. We did definitely, it was like a laundry list. I was listening to the keynote. I'm like, okay, he's going to stop now. Yeah, no, I'm just like, it's more and more just dropping, dropping more and more feature releases. Um, so obviously you guys are shipping more product. You reducing the prices for shipping. I mean, pushing on services. Yeah. You push code in the cloud, we can create a box for you. You can ship that ship means, you know, Sam sends send to the cloud. But that's the dev ops culture that DevOps culture is to be scrappy but think differently. So you guys are thinking differently. Like I gotta ask you, how do you thinking differently because it's clear and ecosystems developing around me and that's something that you do have to nurture. >>You have to invest in this community and you're helping them as business partners now, not just customers. Your customer base now spans the partners. Yeah. Have you balanced it? Still? Same philosophy. What tweaks if you've made your job and an organization based upon the tsunami of an ecosystem growth. I mean our customer ecosystem is really important to our strategy and to our customers. The way we think about it as a um, cloud's new and people are gonna need help. So from consulting firms, systems integrators, managed service providers, which is a really fast growing space. We want to make sure that when our customers want to bet big on AWS, there are those trusted people with certified engineers who can help them either in the short term or longterm basis. And then on the technology partner ISV side, we spent a lot of time making sure that we work collaboratively with these companies to pre sort of certify these applications to run on AWS. >>And then we create pre configured versions of them that run in our marketplace where our customers can browse through a catalog of software pre-configured or run in AWS. They can install with one click of the button and then it just shows up on their AWS bill. So we're trying to make it a lot easier for people to use a lot of these partners technology. And you know what, we're not going to come out with everything. You know, we'd like the creativity of our partners. The customers like to know if they, if they bet on AWS and they say, huh, you know, I wonder if you know, there's some good no SQL databases that run on AWS. Oh there's Mongo, there's Cassandra and whatever space you pick, there may be something we offer and there may be four or five other solutions from our partners. We love that choice because that's what customers ask us. Well, >>congratulations on all your success now. And my final question for you is really probably the hardest question and you can answer it or not answer it. Um, obviously the competitive landscape has significantly increased the heat in the kitchen around you guys for a while you were uncontested. Yes. Some people kind of pick an ankle biting around Amazon's, you know, leadership. But now you've got some pretty big players. IBM, HP, Oracle, Google, EMC, pivotal, VMware gunning, Rackspace, trickles, OpenStack, all of those kind of going around and no, you don't focus on competition and you focus on the customer. We've heard that before, but like you gotta think about that. That's going to put some pressure. How is that affecting you guys? I see you're mindful of it. Are you guys doing anything different to address it? >>I've never seen a market before where it wasn't healthy for both the leader and for the customers to have competition. And we've always expected this to be a market that would have multiple vendors. We look at our, every other technology, a space that was new and became large. There's multiple vendors and it, you know, it enhances innovation, keeps people honest. It's a good thing. >>So the final question then is what will you tell the folks out there who are watching? Is Amazon enterprise ready, um, what's going on right now? This event, you get the big announcements, give them a recap of what you guys did today and comment on the, on the Amazon is enterprise ready or the enterprise may be ready, not ready for the Amazon. So how do you respond to all that FID out? >>Yeah, I mean that was a question people asked a lot about us in the enterprise three, four years ago. I think we've invested a pretty big deal of our R and D over the past four or five years on just maniacally going through all these enterprise features. I mean, if you look at Gartner's magic quadrant for infrastructure and service, which is 100% designed for enterprise decision makers, we're, we're the faraway leader. Uh, and um, you know, we Mark off their checklist pretty well. And I think that's one of the reasons why we're really becoming the safe choice for it managers and large organizations, large enterprises, large government agencies. Um, I mean, my biggest point of advice is to take a look at our website and we're constantly coming out with new services. And if you haven't looked at this recently, I bet you're going to go there and find some things that you didn't know. Randomness and you'll get some ideas about new projects, new workloads that you can run in the cloud. >>Okay. Final word on re-invent to now. Three major things were announced Canisius app stream and workspaces. Are you happy with what's happened since then and now? It gives a quick guys a feeling of >>yeah, I mean the, the uh, we did a private beta for all three of them. We had a lot of participation. Uh, we showed in the keynote some of the real creative applications people are building with app stream where they're streaming very graphically intensive applications out to a variety of devices. Really making it easier for developers, workspaces, the interest. I've never seen a product like this before. Um, where the customer is in the private beta are just so excited about giving us some features, talk about how we can make it better. Um, tons of, tons of energy, tons of excitement. And Canisius is one of these things where, you know, we didn't know what to expect. I mean it's, it's a, a, a realtime analytics service to ingest massive amounts of data and you can build all kinds of apps on top of it. And I think, uh, one of the things we talked about today, uh, was a gaming company. Supersolid makes classic plans to take all the click stream and usage data of their application to figure all these intelligent endgame offers and how to make their games more efficient and more fun. And uh, that's the best part is when we can come out with technology that is pretty broad and can be used for a lot of things. And then we let customers be creative and we can see what they do. >>Then they do Italia. Luckily they generally anymore, right there you'll come and you actually have the hardest and easiest job in the world kind of at the same time. One is you just have great customers. You have the sizzle and the steak, as we say, meat on the bone. Um, great product mix. You guys introducing that stuff here, prices dropping and functionality increasing and innovation having the same time. It's actually quite an amazing thing. So we're really impressed. Again, we're happy customer with Bouchut that's coming on the cube. Again, appreciate it for having me. This is the cube. This is what we do. We go out to the events, we go where the action is, and the action is at Amazon web services summit in San Francisco. This is the cube. We'll be right back with our next guest after this short break.

>>we're back. >>This is Dave Volante. I'm with Wiki bond dot Oregon. This is Silicon angle's the cube where we extract the signal from the noise. We go into the events, we're bringing you the best guests that we can find. And we're here at the AWS summit. Amazon is taking the cloud world by storm. He was on, invented the cloud in 2006. They've popularized it very popular of course with developers. Everybody knows that story. Uh, Amazon appealing to the web startups, but what's most impressive is the degree to which Amazon is beginning to enter the enterprise markets. I'm here with my cohost Jeff Frick and Jeff, we heard Andy Jassy this morning just laying out the sort of marketing messaging and progress and strategies of AWS. One of the things that was most impressive was the pace at which they put forth innovations. We talked about that earlier, but also the pace at which they proactively reduce prices. Uh, that's different than what you'd see in the normal sort of enterprise space. Talk about that a little bit. >>Yeah. Again, I think it really speaks to their strategy to lock up the customer. It's really a lifetime value of the customer and making sure that they don't have a really an opportunity or a reason to go anywhere else. So as we discussed a little bit earlier, they leverage, you know, kind of the pure hardware economics of, of decreasing a computing power, decreasing storage, decreasing bandwidth, but then they also get all the benefits of scale. And I think what's in one of the interesting things that Andy talked about and kind of his six key messages was that it's actually cheaper to rent from them because of the scale than it is to buy yourself. And I know that's a pretty common knock between kind of a build or buy, um, kind of process you go through and usually you would think renting at some scale becomes less economical than if you just did it yourself. But because their scale is so massive because of the flexibility that you can bring, uh, computing resources to bear based on what you're trying to accomplish really kind of breaks down the, uh, the old age old thought that, you know, at scale we need to do it ourselves. >>Well, and that's the premise. Um, I think, and, uh, let's Brits break down a little bit about that, that analysis and, and Andy's keynote. So he put forth some data from IDC which showed that, uh, the Amazon cloud is cheaper than the, uh, a, a so-called private cloud or an in house on premise installation. You know, I certainly, there's, it's, it's a, it's an, it's depends, right? It really depends on the workload. That's somewhat of an apples to orange is going on here and the types of workloads that are going down in the AWS cloud, granted he's right and that they're running Oracle, they're running SAP, but the real mission critical workloads, what he calls mission critical aren't the same as what, you know, Citi would call mission critical. Right? So to replicate that level of mission criticality, uh, would probably almost most certainly be more expensive rental versus owning the real Achilles heel of, of, of any cloud, not just Amazon. >>Cloud really is getting data out. Um, moving data, right? Amazon's going to charge you not to get data in. They're gonna charge you to store it there to exercise, you know, compute. Uh, and then, but they're also gonna charge it if you wanted to take it out. That's expensive. The bandwidth costs and the extrication costs are expensive. Uh, the other issue with cloud again is data movement. It takes a long time to move a terabyte, let alone multiple terabytes. So those are sort of the two sort of Achilles heels of, of cloud. But that's not specific to Amazon's cloud. That's any cloud. Yeah. So we've got a great lineup today. Um, let's see. We've got Ariel Kelman coming on, uh, and I believe he's in the house. So we're going to take a quick break. Quick break. Right now we right back with Ariel Kelman, who's the head of marketing at AWS. Keep right there. This is the cube right back. >>we lift out all the programs out there and identified a gap in tech news coverage. Those shows are just the tip of the iceberg and we're here for the deep dive, the market beg for our program to fill that void. We're not just touting off headlines. We also want to analyze the big picture and ask the questions that no one else is asking. We work with analysts who know the industry from the inside out. So what do you think was the source of this missing? So you mentioned briefly there are, that's the case then why does the world need another song? We're creating a fundamental change in news coverage, laying the foundation and setting the standard, and this is just the beginning. We looked on all the programs out there and identified a gap in tech news coverage. There are plenty of tech shows that provide new gadgets and talk about the latest in gaming, but those shows aren't just the tip of the iceberg. And we're here for the deep dive. >>Okay, >>Dave Olanta. I'm with Wiki bond.org and this is Silicon angle's the cube where we extract signal from the noise. We bring you the best guest that we can find. We go into events like ESPN goes into sporting events, we go into tech events, we find the tech athletes and bring to you their knowledge and share with you our community. We're here at Moscone in San Francisco at the AWS summit. We're here with Arielle Kellman who's the head of worldwide marketing for AWS. Arielle, welcome to the cube. Thanks for having me, Dave. Yeah, our pleasure. I really appreciate you guys having us here. Great venue. Uh, let's see. What's the numbers? It looks like you know, many, many thousands, well over 5,000 people here by four or 5,000 people here. We're doing a about a dozen of these around the world, one to 4,000 people to help educate our customers about all the new things we're doing, all the new partners that are available to help them thrive in the AWS cloud. >>It's mind boggling the amount of stuff that you guys are doing. We just heard NG Jesse's keynote, for those of you who saw Andy's keynote at reinvent, a lot of similar themes with some, some new stuff in there, but one of the most impressive, he said, he said, other than security, one of the things that we're most proud of is the pace at which we introduce new services. And he talked about this fly wheel effect. Can you talk about that a little bit? Sure. Well, there's kind of two different things going on. The pace of innovation is we're really trying to be nimble and customer centric and ultimately we're trying to give our customers a complete set of services to run virtually any workload in the cloud. So you see us expanding a broader would additional services. And then as we get feedback we add more and more features. >>Yeah. So we're obviously seeing a big enterprise push. Uh, Andy was, was very, I thought, politically correct. He said, look, there's one model which is to keep charging people as much as you possibly can. And then there's our model, which is we proactively cut prices and we passed that on to customers. Um, and, and he also stressed that that's not something that's not a gimmick. It's not a sort of a onetime thing. Can you talk about that in terms of your philosophy and your DNA? It's just our philosophy. It's actually a lot less dramatic than is often portrayed in the press. Just the way we look at things as we're constantly trying to drive efficiencies out of our operations. And as we lower our cost structure, we have a choice. We can either pocket those savings as extra margin or we can pass those savings along to our customers in the form of lower prices. >>And we feel that the ladder is the approach that customers like and we want to make our customers happy. So this event, uh, we were talking off camera, you said you've been doing these now for about two years. You do re-invent once a year. That's your big conference out in Vegas and it's a very, very large event, very well attended. And you do these regionally and in and around the world, right. Talk about that a little bit. We do about a dozen of these a year. Um, we did, uh, New York a couple of weeks ago, London, Australia and Sydney. I'm going to go to India and Tokyo, really about a dozen cities in the world and it's a little tactic. I'm not going to beat all of them, but you know, the focus is to really, uh, deliver educational content. Uh, we'll do about maybe 12 to 16 technical breakout sessions all for free, uh, for, for customers and people who want to learn about AWS for the first time. >>And the, and the audience here is largely practitioners and partners, right? Can it talk about the makeup a little bit? Sure. It's a pretty diverse set of people. Um, we have a technical executives like CEOs and architects and we have lots of developers and then lots of people from our, our partner ecosystem of integrators wanting to, um, you know, brush up on the latest technologies and skills and a lot of people who just want to learn about the cloud and learn about AWS. I think there are a lot of misconceptions about AWS and I'd like to just tackle some of those with you if I may. So let me just sort of, let's list them off and you can respond. Yeah, we'll let our audience to sort of decide. So the first is that AWS has only tested dev workloads. Can you talk about that a little bit? >>Sure. Um, well test and dev local workloads are very popular. We saw, we covered that in the keynote. Um, and it's often a place where it organizations will start out with AWS, but it is by no means the most popular or most dominant workload. We have a lot of people migrating, uh, enterprise apps to the cloud. Um, if you look at, uh, in New York, uh, in our summit we talked about Bristol Myers Squibb, uh, running all of their, um, clinical trial simulations and reducing the amount of time it takes to run a simulation by 98%. Uh, if people are running Oracle, SharePoint, SAP, pretty much any workload in the cloud. And then another popular use is building brand new applications, uh, for the cloud. You can miss, some people call them cloud native applications. A good example is the Washington post who built an app called the social reader that delivers their content to Facebook and now as more people viewing their content, their than with their print magazines and they just couldn't have done that, uh, on premises. >>So, uh, the other one I want to talk about, we're going to do some serious double clicking on security so we don't have to go crazy on it, but, but there's a sort of common perception that the cloud is not secure. What do you guys say about that? Yeah, so, um, really our number one priority is security. You're looking at a security, operational performance, uh, and then our pace of innovation. But with security, um, what we want to do is to give enterprises everything they need to understand how our security works and to evaluate it and how it meets with their requirements for their projects. So it really all starts with our, our physical security, um, our network security, the access of our people. They're all the similar types of technologies that our customers are familiar with. And then they also tend to look at all the certifications and accreditations, SAS 70 type two SOC one SIS trust. >>I ATAR for our government customers. And then I think it was something a lot of people don't understand is how much work we've put into the security features. It's not just is the cloud secure, but can I interact and integrate, uh, your security functionality with all of my existing systems so we can integrate with people's identity and access systems. You could have a private dedicated connection from your enterprise to AWS with direct connect to, I really encourage anyone who has interest in digging into our security features to go to the security center and our website. It's got tons of information. So I'm putting on the spot. Um, what percent of data centers in the world have security that are, that is as good or better than AWS. It'd be an interesting thing for us to do a survey on. But if you think about security at the infrastructure layer down is what we take care of. >>Now when you build your application, you can build a secure app or non-secure app. So the customer has some responsibility there. But in terms of that cloud infrastructure, um, for a vast majority of our customers, they're getting a pretty substantial upgrade in their security. And here's something to think about is that, um, we run a multitenant service, so we have lots and lots of customers sharing that infrastructure and we get feedback from some of the most security conscious companies in the world and government agencies. So when our customers are giving us a enhancement request, and let's say it is, uh, an oil company like shell or financial services company like NASDAQ, and we implement that improvement because there's always new requirements. We implement that all of our hundreds of thousands of customers get those improvements. So it's very hard for a lot of companies to match that internally, to stay up to speed with all the latest, um, requirements that people need. >>Yeah. Okay. So, uh, and you touched on this as well as the compliance piece of it, but when you think of things like, like HIPAA compliance for example, I think a lot of people don't realize that you guys are a lead in that regard. Can you talk about that a little bit more? Yeah. So, uh, we have a lot of customers running HIPAA compliant, uh, workloads. Um, there's, there's one company or the, the Schumacher group, which does emergency room staffing out of Lafayette, Louisiana. And we, companies like that are going through the process. They have to follow their internal compliance guidelines for implementing a HIPAA compliant plan app. It's actually, it's more about how you implement and manage the application than the infrastructure, which is part of it. But we, we satisfy that for our customers. Let's talk a little bit about SLA. That didn't come up at least today in Andy's keynote, but it didn't reinvent and he made a statement at reinvent. >>He said, we've never lost a piece of business because of SLS. And that caught my attention and I said, okay, interesting. Um, talk about, uh, the criticisms of the SLA. So a lot of people say, wow, SLA, not just of Amazon's cloud, but any public cloud. I mean, SLA is a really a, in essence, a, an indication of the risk that you're able to take and willing to take. What are your customers tell you about SLS? The first thing is we don't hear a lot of questions about SLS from our customers. Some customers, it's very important that we have SLA is for most of our services, but what they're usually judging us on is the operational track record that we provide and doing testing and seeing how we operate and how we perform. Uh, and, uh, we had an analyst from IDC recently do a survey of a bunch of our customers and they found that on average the average app that runs on AWS had 80% less downtime than similar apps that are running on premises. >>So we have a lot of anecdotal evidence to suggest that our customers are seeing a reliability improvement by migrating their apps to AWS. You're saying don't judge us on the paper, judge us on our actual activities in production and in the field. Typically what most of our customers are asking for is they want to dig into the actual operational features and, and a track record. Now the other thing I want to address is the so called, you know, uh, uh, exit tax, right? It's no charge to get my data in there. I keep my data in there. You, you, you charged me for storing it for exercise and compute activity, but it's expensive to get it out. Um, how do you address that criticism? Well, our pricing is different for every service and we really model it around our customers to both really to really satisfy a broad set of use cases. >>So one example I think you may be talking about is I would Amazon glacier archive service, which is one penny per gigabyte per month. And for an archive service, we figured that most people want to keep their data in there for a long period of time so that we want to make it as cheap as possible for people to put it in. And if you actually needed to pull it out, the reason is because you may have had some disaster or you accidentally deleted something and that you are going to be, uh, you're going to be retrieving data on a far less frequent basis. So on an overall basis for most customers it makes sense that we could have done is made the retrieval costs lower and then made the storage costs higher. But the feedback we got from customers is, you know, archiving a majority of customers may never even retrieve that data at all. >>So it ended up being cheaper for a vast majority of our customers. I mean that's the point of glacier. If you put it there, you kind of hope you never have to go back and get it. Um, the other thing I wanted to ask you about is some of the innovations that we've seen lately in the industry, like a red shift, right? The data warehouse, you mentioned glacier. It was interesting. Andy said that glacier is the fastest growing service in terms of customers. Red shift was the fastest growing service, I guess overall at NAWS. So Redshift is an interesting move for you guys. Uh, that whole big data and analytics space. What if you could talk about that a little bit? If you talk to it, executives in the enterprise and even startups now, they have to analyze lots of data. Building a big data warehouse is, is one of the best examples of how much the pain of hardware and software infrastructure gets in the way of people. >>And there's also a gatekeeping aspect to it. If you're working in a big company and you want to run, you have a question and a hypothesis, you want to run queries against terabytes and petabytes of data, you pretty often have to go and ask for permission. Can I borrow some time from the data warehouse? No, no, no, no. You're not as important. Well, what are customers going to go, Hey, I'm going to go load the data, load a petabyte of data, run a bunch of analysis, and shut it down and only pay for a few hours. So it's not just about making a cheaper, it's about making use of technology possible where it was just not possible in feasible and cost prohibited before. Yeah, so that's an important point. I mean, it's not, it's not just about sort of moving workloads to the cloud, you know, the old saying a my mess for less. >>It's about enabling new business processes and new procedures and deeper business integration. Um, can you talk about that a little bit more? Add a little color to that notion of adding value beyond just moving workloads out of, you know, on premise into the cloud to cut costs, cut op ex, but enabling new business capabilities. When you remove the infrastructure burden between your ideas and what you want to do, you enable new things to be possible. I think innovation is a big aspect of this where if you think about if you reduce the cost of failure for technology projects so much that approaches zero, you change the whole risk taking culture in a company and more people can try out new ideas and companies can Greenlight more ideas because if they fail it doesn't cost you that much. You haven't built up all this infrastructure. So if you have more ideas that are, that are cultivated, you end up with more innovation. >>Whereas before people are too afraid to try new things. So I'm a reader of of Jeffrey's a annual letters. I mean I think they're great. They're Warren buffet like in that regard. One of the exact emphasizes, you know this year was the customer focus. You guys are a customer focused organization, not a competitive focused organization. And again, you got to recognize that both models can work, right? Can you talk about that a little bit? Just the church of the culture. Yeah, I mean when, you know, starts out with how we build our products. Anyone who has a new idea for a product, first thing they got to do is write the press release. So what our customers are going to see is it valuable to them. And then we get come get products out quickly and then we iterate with customers. We don't spend five years building the first version of something. >>We get it out quickly. Uh, sort of the, the, the lean startup, if you heard of the minimum viable product approach, get it out there and get feedback from customers. Uh, and iterate. We don't spend a lot of time looking at what our competitors are doing cause they're not the ones that pay our bill. They're not the ones that can hire and fire us. It's the customers. So I'm you've seen this thing come, you know, quite a ways. I mean, you were at Salesforce, right? Um, which I guess started at all in 99. You could sell that, look at that as the modern cloud sort of movement was, wasn't called cloud. And then you guys in 2006 actually announced what we now know is, you know, the cloud, where are we in terms of, you know, the cloud, you know, what ending is it? To use the sports analogy, I don't know what ending is it, but you know, it's an amazing time where there's such a massive amount of momentum of adoption of the cloud from every type of company, every type of government agency. >>But yet still, when you look at the percentage of it spend or you go talk to a large company and you say, even with all these projects, what percentage of your total projects, there's still tremendous growth ahead of us. Yeah. So, um, there's always that conversation about the pie charts. 70% of our, our effort is spent on keeping the lights on. 30% is spent on, on innovation. And I don't know where that number came from but, but I think generally anecdotally it feels about right. Um, talk about that shift. Yeah. Well I mean your customer base, you talk to any CIO, they don't like the idea of having 80% of their staff and budget being focused on keeping the lights on and the infrastructure would they like to do is to really shift the mix of what people are working on within their organization. It's not about getting rid of it, it's about giving it tools so that every ounce of effort they're doing is geared towards delivering things to the business. >>And that, that, that's what gets CIO is excited about the cloud is really shifting that and having a majority of their people building and iterating with their end users and with their customers. So we talked about the competition a little bit. I want to ask you a question in general, general terms, you guys have laid out sort of the playbook and there's a lot more coming. We know that, uh, but you know this industry quite well. You know, it's very competitive. People S people see what leaders are doing and they all sort of go after it. Why do you feel confident that AWS will be able to maintain its lead and Kennedy even extend its lead in why? Well, there's a couple things that we sort of suggest for customers to look at. I think first of all is the track record and experience of when you're looking at a cloud provider, have they been in this business for a long time? >>Do they have a services mentality where they've had customers trust them for their, for applications that really they trust their business on? Um, and then I think secondly, is there a commitment to innovation? Is there a pace of new features and new technologies as requirements change? And I think the other, the other piece that our customers really give us a lot of feedback on is that they can count on us Lauren prices, they can count on a real partnership as we get better at this and we're always learning as we get better and we reduce our cost structure, they're going to get to benefit and lower their costs as well. So I think those are kind of big things. The other thing is, is the customer ecosystem I think is a big part of it where, um, you know, this is technology. Uh, people need advice, they need, uh, best practices. >>They often need help. And I'm in a kind of analogy I make is if I have a problem with my phone, with my iPhone, I can probably close my eyes and throw it, I'm going to hit someone who also has an iPhone. I can ask them for help. Well, if you're a startup in San Francisco or London or if you're an enterprise in New York or Sydney, odds are that your colleagues, if they're doing cloud, they're doing it with AWS and you have a lot of people to help you out. A lot of people to share best practices with. And that's a subtle but important point is as, as industry participants begin to aggregate within your cloud, there's a data angle there, right? Because there's data that potentially those organizations could share if they so choose to a, that is a, that is a value. And as you say, the best practice sharing as well. >>I have two last questions for you. Sure. First is, is what gets you excited in this whole field? I think it's like seeing what customers are doing. I mean, that's the cool thing about, uh, offering cloud infrastructure is that anything is possible. Like we met Ryan, uh, who spoke from atomic fiction. These guys are the world's first digital effects agency that's 100% in the cloud. And to see that they made a movie and all the effects like the Robertson mech, his flight film without owning a single server, um, it's just, it's amazing. And to see what these guys can do, how happy they are to have a group of 30, 40 artists that, um, can say yes when the director says I want it to do differently. I want to add, go from 150 to 300 shots and to see how happy and excited they are. >>I mean that, that's what motivates me. Yeah. Okay. And then my last question, Ariel, is, um, you know, what keeps you up at night? What worries you? Well, I think, you know, the most important thing that we can't forget is to really keep our fingers on the pulse of the customers and what they want, and also helping them to figure out what they want next. Because if we don't keep moving, then we're not going to keep pace with what the customers want to use the cloud for. All right, Ariel Kelman thanks very much. Congratulations on the Mason's progress and we'll be watching and, and really appreciate, again, you having us here. Appreciate your time coming on. Good luck with the rest of the tour. I hope you don't have to do every city. It sounds like you don't, but, uh, but if it sounds like you've enjoyed them, so, uh, congratulations again. Great. All right. This is Dave Milan to keep it right there. This is the cube. We'll be back with our next guest right after this word.

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE coming to you from our Palo Alto studios for a CUBE Conversation. We're talking about the Citrix Workspace Summit. It happened earlier today. And we've got one of the experts in the field, CUBE alumni and always a really fun guest to have on. Let's give a welcome to Tamara McCleary. She's coming to us from Colorado. She's the CEO of Thulium but you know her from social media and seeing her at all the conferences and speaking. And Tamara, it's great to see you again >> Jeff, it's so good to be here. Hey, next best thing to being in person, right? >> Absolutely. I mean, there is some good stuff. Neither of us had to get on an airplane today and we were able just to connect via the magic of the internet, which I think people forget how magic it truly is. So I looked up, we last spoke, it was mid-April. We were about a month into this thing after the kind of shutdown. And really the topic there was about this light switch moment on the work from home front. Now we're seven months into this, eight months into this, and clearly it's not going away anytime soon. And even when it does, it's not going to go back exactly to the way it was. So first off, how are you doing? 'Cause I know you spend a lot of time at conferences and traveling all over the world, so your life's been changed quite a bit. And then two, just your kind of perspective as we've moved from the light switch moment to the, that this is the new normal and will be the new normal going forward. Maybe not exactly how it is today, but we're not going back to the way that it was before. >> You couldn't be more spot on, Jeff. In fact, when you said April, to me, it almost feels like not seven months. It feels much longer ago. And since the last time I got on an airplane was the end of February, and that was a huge disruption to me in my life. I had always been in three, four cities a week, every week, and haven't traveled on an airplane since February. So the world is different, and it has shifted, and there's no going back. We can't step in the river twice and hit that same spot. I totally messed up that quote, but that's me. You're used to that already. >> Jeff: Exactly. >> But some things don't change. But I think when we look at work, and what we were talking about back in April is that now we're looking at the potential for kind of a hybrid approach, whether we're talking about work or even kids, some kids going back to school, there's a hybrid approach. And with that comes its own set of complexities that we have to consider. So not only has the culture shifted into a place where you have your workforce who has gotten used to working remotely, and there's a lot of things with working remotely that we didn't have when office was the centrical focus for the workplace. So there's a lot of flexibility when you work from home. And I think one of the interesting things with the Citrix Workspace Summit was when CEO David Henshall talked about how it's the people, right? So it's our workforce, our employees who are our most valuable, but also our most costly assets. So we have to make sure that the employee experience is one that is pleasing and helps us to have not only talent acquisition, but also talent retention in a really dynamic, competitive atmosphere. And I'm sure I just posed this question so we could go a million different places with this. Where do you want to go with it, Jeff? >> Well, I was going to say, and of course we can go forever, and we don't have forever, so at some point we'll have to stop talking at the end of this interview. But I just love having you on. And what I want to drill in is as we've talked about the new way to work for a very, very long time. This is not a new topic. And we've had remote work tools and we've had VPNs and we've had mobile phones now since 2007, but we didn't have this forcing function, and I think that's what's really different here is that now it wasn't a choice anymore. There was no more planning and talking about it and maybe or maybe not. Work from home was kind of a first-class citizen in terms of priority. COVID changed all that dramatically overnight. And it's driven home this other kind of concept which we talk a lot about generically in terms of the customer experience as they interact with our applications, which is the way that now they actually interact with the company. And we've talked a little bit about new way to work, but now it's really driven to the forefront, because as you said, there's a lot of benefits from working from home. You could eat dinner with your family, maybe can pick up a few more of the kids' activities, whether it's a sports game in the middle of the afternoon or something in the evening, but there's also a lot of stress. There's a lot of kind of this always on and this constant notifications, whether it's coming from email or text or Slack or Teams or Asana or whatever. So refocusing on the employee experience and elevating that up into a much more important thing, as you said, for both wellness and employee satisfaction, but also retention and getting new employees. It's really changed the priority of that whole set of, kind of point of view around the employee experience that wasn't there kind of pre-COVID. >> Absolutely. And I think you just tapped onto something that I think affects all of us who are juggling these multifaceted lives, and that is the constant interruption and distraction, and that costs money. And I think about that as the CEO of our organization is that how many of these distractions could be avoided to create efficiency and productivity. It also creates happiness for the individual. I don't think anybody likes to be constantly distracted, but when you have a bunch of different applications and you don't have them in one accessible place and you're constantly having to flip between these applications, it can cause a lot of friction and frustration. And I think genuinely that was my very first introduction to Citrix was the ability to really streamline and have everything in one place on a beautiful dashboard that was personalized to the individual. Not everybody in the organization needs to have all the applications, right? Some of your employees only need a few, and it just depends on who they are and what they're doing within the organization. And so I think decreasing that friction, making it easier for people, and certainly ensuring not only a frictionless experience at home but also ensuring security is huge. I mean, how many times have we talked about cybersecurity is not a bolt on afterwards. It has to be all the way up through the stack. And certainly we did have an increased threat landscape with work from home situations because there were all these security breaches and issues and vulnerabilities. So I know we're not talking security today, but I'm wild about it. But I think that all of these things, what I like about what Citrix is doing, and I enjoy the Summit, is the fact that they're blending everything into a single solution so that it just gets done. Work gets done from wherever you are, whether you're at home, you're in office, or in your car, work gets done. >> And not only work but I thought the theme that's interesting that came out in David's keynote is our best work. It's good work and high-value work. And there's really kind of two aspects of that. One, as you just said, is please help me with the distractions and use machine learning and artificial intelligence and this unified platform to decide whether I should or should not be distracted. Also help me prioritize what I should be working on kind of right now, which, again, a great opportunity for AI and ML to elevate that which is most important to the top of my inbox. But even more in one of the keynotes was integrating the concept of wellness, and not just wellness in the HR manual at the back after vision and dental and getting your health checks, but wellness even where the application suggests that you take a two-hour window in this particular period of time to be thoughtful and do some deep thinking. And someone mentioned the people we talk about in automation and getting rid of drudgery and errors and all the bad stuff that comes from doing crappy work, not only is it not fun, but super error prone. This is a really different to use technology to help the employee, as you said, not only just get work done, but get good work done, get high-value work done, prioritize good stuff, and not just deal with the incessant henpecking that is the notification world that it's really easy to fall into if you don't turn some of that stuff off or at least tone them down a little bit. >> That's so true. I don't know if you saw this, but there a study by Stanford of, I think it was 16,000 workers, and over a nine-month period, they did this study, and it was a study looking at work from home and whether productivity was increased. And every, 'cause at first you remember what it was, Jeff. I mean, in the old regime, we would thought, oh dear, we don't want a remote workforce because everybody's going to be hanging out in their pajamas and screwing around and not doing work. And that's not true. What ends up happening is that this study showed that productivity increased by 13%. And, I mean, that's huge, right? So there was a huge bump in performance. And in this particular study, the variables that they cited was perhaps that they had a quieter workspace. I mean, you're not getting barraged by all the endless meetings, unless you have endless Zoom meetings, but that's a whole nother conversation. But you're having more time to focus and flexibility on when you work, which also increases focus. But I thought what you mentioned, the wellness piece was important, because then if you look at other studies, there was a Forbes article that cited that the average worker starts at 8:32 a.m. or something like that and works until 5:38 p.m. And I think the days of the week that were the most productive were Tuesday, Wednesdays, and Thursdays. But this was interesting, I thought. Telephone calls were up by 230%, so the calls that employees were making, and CRM activity was up by 176% and email up by 57% and chats up by 9%. So what we're seeing is that people are trying to find creative ways to remain connected and communicate, but in different ways. And I think that's where the wellness piece comes in and kind of what you were saying with that. I think it's a microapp that Citrix has on their Workspace, their workspace dashboard that pops up a reminder and says, hey, you think you should take a break or get up from your desk. But I think that what's nice about that is it's easy to get sucked into your computer all day. I'm guilty. I will definitely say I can start off pretty darn early in the morning or usually around by five and go till late at night. But, and it's all in front of the computer screen. So maybe I need that Citrix workspace solution to tap me on the shoulder and tell me to go take a meditation break. >> At least one of those watches that'll tell you to get up and twist around. Well, let's shift gears a little bit. They had Satya Nadella on, and Satya is a phenomenal executive, been super successful turning that big, very large boat, Microsoft, into really a cloud company and a SaaS company, and nothing but great success. Always happy to hear him. He had some interesting comments I want to run by you. One of them he said is we were dogmatic about work before, but don't replace what we were with just a new dogma. And what he really highlighted, A, obviously without the technology platform and cloud and all these tools that we have in place, this couldn't have happened. But more importantly, he said it really highlights the need for flexibility and resiliency, and to really, again, kind of elevate those as the first class citizens as to what you should be optimizing for. And really the highlight within this sudden shift with COVID that if you've got those capabilities, you're going to be successful, and if you don't, you're in real trouble >> I'm glad you brought Satya up, because he also said something really cool that I think is true, and that is we are running right now, currently we are running a global scale experiment. Do you remember him saying that? >> Yeah. >> And it's so true. I think right now the social scientists are going wild because finally they've got their captive collection of their study, their guinea pigs. But the other thing he was saying, too, is that we're going to be harnessing all these technologies to be able to re-skill and up-skill. And how long have we been talking about this, Jeff, with the future of work, that it will be a re-skilling and up-skilling of the workforce. He even mentioned holographic technology. He didn't go into it, but just the mention of it got me thinking about how we are currently using some of those nascent technologies to be able to up-skill and re-skill our workforces and also protect a workforce that doesn't necessarily need to be on scene on the edge of it all. And then he gave an example of an engineer being able to communicate with a first-line worker without having to be actually in the physical presence. And so I think this crucible that we're in called a global pandemic, forcing our hand, really, to do all the things that we've been talking about at all these conferences that we've been to, for me, maybe the past two decades, is that it's show, don't tell. So we're not talking about it anymore. We actually have to do it. And another thing that Satya said was that nine to five is definitely not true anymore with work. It's flexibility. And it's really... He also mentioned this EEG study into meeting fatigue. >> Jeff: Yes. >> I thought it was pretty wild. An EEG study into meeting fatigue. And I bet even without reading that study, all of us who are on video conferencing systems can probably tell what the outcome of that was. But concentration wanes very quickly. In fact, I think in that study it was after 20 minutes. But, so kudos to Citrix for putting on their summits, because did you notice for once we had the enjoyment of all these just really contents, deliciously packed segments that were short. >> Jeff: Right. >> Whereas at live events, they went on way too long. I mean, even customer stories went on way too long. And I really love the staccato nature of these customer stories and partnerships and what was working, and I just thought that they did a really nice job, and it was interesting because it met perfectly with staying underneath that 20-minute window before attention wanes. >> Right, right. And they even broke it up into three conferences, right? It was Citrix Synergy before. >> Right. >> Now it's workspaces, it's cloud, and then the third one will be security. But I want to double down on another concept. We talked about it last time with you and with Amy about measuring work and about kind of old work paradigms in terms of measuring performance that were really based more on activity than output. And this concept that work is an output, not a place. And it kind of makes you think of talking about cloud and a cloud-centric way of thinking about things. It's not necessarily the delivery method. It's about adopting quick change and rapid pace and having everything available that you need anywhere you are at the same time. So it seems strange to me that it took this to drive people to figure out that they should be measuring output and not activity. And were some early applications that came out when this all went down that are going to report back as to how often are you looking at your Zoom calls and how often are you sitting in front of your desk and all this silly stuff that just, again, misses the point. And I think this whole employee experience is, as you said, make 'em happy, make 'em feel fulfilled. They want to do meaningful work. They want to do high-value work. They just don't want to be an integration machine between the email system and the accounts receivable system and the accounts payable system. There's so much of an opportunity to get more value from the people, which, oh, by the way, makes for happier people. So do you think finally we're at a point where we can start getting away from just measuring activity unless that's your job to put a widget on a screw and really focus on output and high-value output and innovative output and deep thinking output versus just checking another box and passing the paper down the line? >> You know, Jeff, that reminds me of what Erica Volini, I think she's global human capital practice at Deloitte. I really loved her presentation. I also like the fact that I felt like she was speaking from her home, and she mentioned she's a new mom, and so there was this warmth and connection there which also I think is something really that we don't think about being, but it is a gift since we've all had to work from home is being able to see kind of executive individuals in a regular environment, and it humanizes it all, right? She said something really interesting in her talk. She was talking about rearchitecting the future of work, and she was talking about essentially, the premise was that human beings need, crave, have to have work that's meaningful and real. And part of this whole experience piece, part of this removing the friction from the experience of the employee and providing opportunities, stimulating growth opportunities for employees to give them that sense of meaning. But also she talked about the relationships. I mean, work is a huge part of the relationships in our life. And so this meaningful relationships and connections and in her architecting the work of the future, it's harnessing technology in service to humans to do a better job. And I think the word she used was augmentation, right? So the augmentation piece would be as we think about reinventing or re-imagining or re-architecting, we look at what's going to happen when we have the human working with the machine, but the machine in service to augmenting that human being to do, potential is what she was talking about, to really reach their potential. And so it's not about being replaced by technology. It's not being replaced by artificial intelligence, with machine learning algorithms. It's actually working in tandem so that technology potentiates the human that is using the technology. And I think that was a really good way of putting it. >> Right, right. I mean, we talk, it's one of our taglines, right? To separate the signal from the noise. And the problem is with so many systems now, and I forget, you may know off the top of your head, the average number of applications that people have to interact with every day to get their job done. >> Too many. >> Too many. >> Too many. >> It's a lot. So, so there is a lot of noise, but there's also some signal. And so if you're not paying attention, you can miss the signal that might be super, super important because you're overwhelmed by the noise. And so I think it is a real interesting challenge. It's a technology challenge to apply the machine learning and artificial intelligence, to sort through the total flow, to be able to prioritize and separate the signal from the noise to make sure we're working on the stuff that we should be working on. And I think it's a growing challenge as we just seem to always be adding new applications and adding new notifications and adding new systems that we have to interact with versus taking them away. So Citrix has this approach where we're just going to bring it all in together under one place. And so whether it's your Salesforce notification or your Slack notification or Zoom meeting, whatever, to have it orchestrated as a single place so I don't have 18 tabs, 14 browsers, and two laptops running just to get my day job done. >> You're going to make me self-conscious of all the tabs I have right now. Thanks a lot, Jeff. But, it's kind of, I like hearing stories, right? I think stories communicate to me kind of these practical applications. And I think Citrix did a brilliant job in the Workspace Summit of highlighting some of these customer stories that were really inspiring during the pandemic. One of 'em was City National Bank and Ariel Carrion? This is a test of my memory. He's the CTO, right, of City National Bank. And he's talking about that they had already had a partial migration to the cloud prior to the pandemic. So obviously there was an advantage for those organizations that already had their toe in the water. So, but when the pandemic hit, then it really catalyzed that movement all the way into the cloud and essentially creating a digital bank. And what was really interesting to me is that they funded 9600 loans and taking on new clients during that time of transformation to a digital bank. And one of the coolest things that he said to me was that in a regular program, it would've taken, mind you, get this. It would've taken 14 years, 14 years to accomplish what they did in three months. >> That's a long time. >> I was blown away, right? Just to me, that speaks a lot, because what we're talking about here is their clients are small business, and who do you think was impacted most during the pandemic? Small business. So the ability to get loans was critically important to the survival of a lot of companies. And the same story they had with eBay and David Lessor was talking, he's a senior manager in the office of the CIO, I think I remember. And he was talking about how obviously eBay is a digital platform, right? But if you think about the pandemic when we were all had these shelter in place orders, lots of people were able to still make money and earn a living because they were able to do business on eBay. And both eBay and City National Bank are obviously customers of Citrix. But I just found this to be really inspiring, because for eBay pre-pandemic, it was like, I don't know. I think they said they had 11,000 connected users prior to the pandemic, and a lot of those were in physical call centers. >> Jeff: Right. >> And then post-pandemic, I think he was reaching, saying end of Q4 was going to be something like 14,000 connected users. That's huge from 11 to 14. >> Yeah. >> And again, to your point, it's kind of forcing our hand into really not only pivoting, but increasing our speed in this ever-changing dynamic environment. >> Right. >> You know, one of the other things that came up, before I let you go, that it's always nice to have frameworks. Sometimes it just helps us organize our thoughts and it's kind of a mental cheat sheet. And they talked about the four Cs, connectivity, content, collaboration, and culture. And I would have to say they're in inverse order of how I would potentially have prioritized them. But I just wanted to zero in on the culture piece, 'cause I don't think people focus enough on culture. And one of the things I think we talked about in April, and I've certainly talked about a number of times going through this thing in leadership in these crazy times is that the frequency and the type and the topics in communication within your internal world have gone up dramatically. I think we had the, we had a CMO on the other day, and she said internal comms, this is a big company, prior to COVID was important, but not that important within the list of the CMO's activity. But then once this thing hit, right, suddenly internal communications, again, in terms of frequency and the types of topics you're talking about and the forums that you talk about and the actual vehicles in which you talk about, whether it's a all hands Zoom call or it's more frequent one-on-ones with your manager, really, really increase the importance of culture, and then I think probably is going to show over time the people that have it right, getting some separation distance from the people that got it wrong. I wonder if you could just talk about, 'cause you're a big culture person and you know how important the people part of the whole thing is. >> Yeah, culture drives everything. You're right. And that was Citrix's CIO who gave those four Cs, I think, Meerah Rajavel. >> Yeah, yeah. >> She gave those four Cs. And you couldn't be, you couldn't have tapped into something that I think is the soft underbelly of the organization, which is what is the culture. And anyone who's worked in an organization with a sick culture knows that it's just, it's cancerous, right? It grows and it causes decay. And I don't care how much innovation you have. If the culture is sick, you just, you're going to lose your best people. It's hard to work in a sick culture. And so I think what we had to do is when we all started working remotely, that was a culture shift, because we were siloed off of it. We weren't actually hanging out in physical space. Some of the things that we enjoyed about meeting with other human beings physically changed. And so it really behooved organizations to take a look at how they were going to foster culture digitally, how they were going to create that sense of bonding between not only those within your departmental area, but cross over into other areas. And I think that creating that culture that says I don't have to be in the exact same physical space, but we can still connect. I mean, you and I are doing this. We're not in the same physical space. >> Jeff: Right. >> But I'm still going to feel like we met today. >> Jeff: Right. >> You can create that for your employees. And it also means that we learned that we don't have to be in that same physical space, right? And I thought that was a really interesting position when Hayden Brown, the CEO of Upwork, was talking at the summit and saying that even when we look at creating culture with employees who aren't necessarily, maybe it's a workforce from all over the world that you're using, a remote workforce. And when you're using things like employees, if you've got work to do and you can find a really good talent and you can grab them for what it is that you need, you're actually increasing your ability to be able to deliver on things versus having to worry about whether you have that person in house, but you still can create that culture where everyone is inclusive, where someone can be in Australia and someone's in San Francisco and someone's in the UK, and you still have to create a cohesive, inclusive culture. And it matters not anymore whether or not you are a full-time employee or if you're a contract worker. I think in today's space, and certainly in those future of work conversations, it's more about, to the very first thing you said at the beginning, it's more about output. How's that for tying it back up again? >> Jeff: Yeah, very good. >> And that was totally unplanned. But it is about output, and that's going to be the future of work culture. It's not going to be the title that you have, whether or not you're a full-time employee or a part-time employee or a contract worker. It's going to be who are you meeting with? Who are you having these digital interfaces with and Slacking with or using any sort of platform application that you want to use. It's remaining in touch and in communication, and no longer is it about a physical space. It's a digital space. >> Right, right. All right, well, I'm going to give you the last word. You are a super positive person, and there's reasons, and for people that haven't watched your TED Talk, they should. I think it's super impactful and it really changed the way I look at you. So of all the negatives, wrap us up with some positives that you see as we come out of COVID that going through this experience will make in our lives, both our work lives as well as our personal lives. >> Well, since you're going to allow me to go deep here, I would say one of the things that COVID has brought us is pause. It caused us to go in. And with any dark night of the soul, we have to wrestle with the things that are real for us, and the things that fall away are those that were false, false perceptions, false ideas, illusions of even thinking who we are, what we're doing. And we had to come home to ourselves. And I think one of the things that COVID gave us through uncertainty was finding a center in that uncertainty. And maybe we got to know our beloveds a bit more. Maybe we got to know our kids a bit more, even if they drive us crazy sometimes. But in the end, I think maybe we all got to know ourselves a little bit more. And for that, I think we can harness those seeds of wisdom and make better choices in the future to co-create together a future that we are all pleased to wake up in, one that is fair, one that is equal, one that is inclusive, and one that we can be proud to have contributed to. And that's what I hope we've taken from this extremely hard time. >> Well, Tamara, thanks for sharing your wisdom with us. Really appreciate it. And great to see ya. >> Good to see you, too, thank you. >> All right, she's Tamara, I'm Jeff. You're watching theCUBE. Thanks for watching. We'll see you next time. (bright music)

>> Narrator: From around the global its theCUBE, covering HPE Discover Virtual Experience, brought to you by HPE. >> Hi, I'm Stu Miniman and this is theCUBE's coverage of HPE, discover the virtual experience for 2020, getting to talk to Hp executives, their partners, the ecosystem, where they are around the globe, this session we're going to be digging in about artificial intelligence, obviously a super important topic these days. And to help me do that, I've got two guests from Nvidia, sitting in the window next to me, we have Paresh Kharya, he's director of product marketing and sitting next to him in the virtual environment is Kevin Deierling, who is this senior vice president of marketing as I mentioned both with Nvidia. Thank you both so much for joining us. >> Thank you, so great to be here. >> Great to be here. >> All right, so Paresh when you set the stage for us? AI, obviously, one of those mega trends to talk about but just, give us the stages, where Nvidia sits, where the market is, and your customers today, that they think about AI. >> Yeah, so we are basically witnessing a massive changes that are happening across every industry. And it's basically the confluence of three things. One is of course, AI, the second is 5G and IOT, and the third is the ability to process all of the data that we have, that's now possible. For AI we are now seeing really advanced models, from computer vision, to understanding natural language, to the ability to speak in conversational terms. In terms of IOT and 5G, there are billions of devices that are sensing and inferring information. And now we have the ability to act, make decisions in various industries, and finally all of the processing capabilities that we have today, at the data center, and in the cloud, as well as at the edge with the GPUs as well as advanced networking that's available, we can now make sense all of this data to help industrial transformation. >> Yeah, Kevin, you know it's interesting when you look at some of these waves of technology and we say, "Okay, there's a lot of new pieces here." You talk about 5G, it's the next generation but architecturally some of these things remind us of the past. So when I look at some of these architectures, I think about, what we've done for high performance computing for a long time, obviously, you know, Mellanox, where you came from through NVIDIA's acquisition, strong play in that environment. So, maybe give us a little bit compare, contrast, what's the same, and what's different about this highly distributed, edge compute AI, IOT environment and what's the same with what we were doing with HPC in the past. >> Yeah, so we've--Mellanox has now been a part of Nvidia for a little over a month and it's great to be part of that. We were both focused on accelerated computing and high performance computing. And to do that, what it means is the scale and the type of problems that we're trying to solve are just simply too large to fit into a single computer. So if that's the case, then you connect a lot of computers. And Jensen talked about this recently at the GTC keynote where he said that the new unit computing, it's really the data center. So it's no longer the box that sits on your desk or even in Iraq, it's the entire data center because that's the scale of the types of problems that we're solving. And so the notion of scale up and scale out, the network becomes really, really critical. And we're doing high-performance networking for a long time. When you move to the edge, instead of having, a single data center with 10,000 computers, you have 10,000 data centers, each of which as a small number of servers that is processing all of that information that's coming in. But in a sense, the problems are very, very similar, whether you're at the edge or you're doing massive HPC, scientific computing or cloud computing. And so we're excited to be part of bringing together the AI and the networking because they are really optimizing at the data center scale across the entire stack. >> All right, so it's interesting. You mentioned, Nvidia CEO, Jensen. I believe if I saw right in there, he actually could, wrote a term which I had not run across, it was the data processing unit or DPU in that, data center, as you talked about. Help us wrap our heads around this a little bit. I know my CPU, when I think about GPUs, I obviously think of Nvidia. TPUs, in the cloud and everything we're doing. So, what is DPUs? Is this just some new AI thing or, is this kind of a new architectural model? >> Yeah. I think what Jensen highlighted is that there's three key elements of this accelerated disaggregated infrastructure that the data center has becoming. And so that's the CPU, which is doing traditional single threaded workloads but for all of the accelerated workloads, you need the GPU. And that does massive parallelism deals with massive amounts of data, but to get that data into the GPU and also into the CPU, you need really an intelligent data processing because the scale and scope of GPUs and CPUs today, these are not single core entities. These are hundreds or even thousands of cores in a big system. And you need to steer the traffic exactly to the right place. You need to do it securely. You need to do it virtualized. You need to do it with containers and to do all of that, you need a programmable data processing unit. So we have something called our BlueField, which combines our latest, greatest, 100 gig and 200 gig network connectivity with Arm processors and a whole bunch of accelerators for security, for virtualization, for storage. And all of those things then feed these giant parallel engines which are the GPU. And of course the CPU, which is really the workload at the application layer for non-accelerated outs. >> Great, so Paresh, Kevin talked about, needing similar types of services, wherever the data is. I was wondering if you could really help expand for us a little bit, the implications of it AI at the edge. >> Sure, yeah, so AI is basically not just one workload. AI is many different types of models and AI also means training as well as inferences, which are very different workloads or AI printing, for example, we are seeing the models growing exponentially, think of any AI model, like a brain of a computer or like a brain, solving a particular use case a for simple models like computer vision, we have models that are smaller, bugs have computer vision but advanced models like natural language processing, they require larger brains or larger models, so on one hand we are seeing the size of the AI models increasing tremendously and in order to train these models, you need to look at computing at the scale of data center, many processors, many different servers working together to train a single model, on the other hand because of these AI models, they are so accurate today from understanding languages to speaking languages, to providing the right recommendations whether it's for products or for content that you may want to consume or advertisements and so on. These models are so effective and efficient that they are being powered by AI today. These applications are being powered by AI and each application requires a small amount of acceleration, so you need the ability to scale out or, and support many different applications. So with our newly launched MPR architecture, just couple of weeks to go that Jensen announced, in the virtual keynote for the first time, we are now able to provide both, scale up and scale out both training data analytics as well as imprints on the single architecture and that's very exciting. >> Yeah, so look at that. The other thing that's interesting is you're talking about at the edge and scale out versus scale up, the networking is critical for both of those. And there's a lot of different workloads. And as Paresh was describing, you've got different workloads that require different amounts of GPU or storage or networking. And so part of that vision of this data center as the computer is that, the DPU lets you scale independently, everything. So you can compose, you desegregate into DPUs and storage and CPUs, and then you compose exactly the computer that you need on the fly container, right, to solve the problem that you're solving right now. So these new way of programming is programming the entire data center at once and you'll go grab all of it and it'll run for a few hundred milliseconds even and then it'll come back down and recompose itself onsite. And to do that, you need this very highly efficient networking infrastructure. And the good news is we're here at HPE Discover. We've got a great partner with HPE. You know, they have our M series switches that uses the Mellanox hundred gig and now even 200 and 400 gig ethernet switches, we have all of our adapters and they have great platforms. The Apollo platform for example, is break for HPC and they have other great platforms that we're looking at with the new telco that we're doing or 5G and accelerating that. >> Yeah, and on the edge computing side, there's the edge line set of products which are very interesting, the other sort of aspect that I wanted to touch upon, is the whole software stack that's needed for the edge. So edge is different in the sense that it's not centrally managed, the edge computing devices are distributed remote locations. And so managing the workflow of running and updating software on it is important and needs to be done in a very secure manner. The second thing that's, that's very different again, for the edges, these devices are going to require connectivity. As Kevin was pointing out, the importance of networking so we also announced, a couple of weeks ago at our GTC, our EGX product that combines the Mellanox NIC and our GPUs into a single a processor, Mellanox NIC provides a fast connectivity, security, as well as the encryption and decryption capabilities, GPUs provide acceleration to run the advanced DI models, that are required for applications at the edge. >> Okay, and if I understood that, right. So, you've got these throughout the HPE the product line, HPE's got long history of making, flexible configurations, I remember when they first came out with a Blade server it was, different form factors, different connectivity options, they pushed heavily into composable infrastructure. So it sounds like this is just a kind of extending, you know, what HP has been doing for a couple of decades. >> Yeah, I think HP is a great partner there and these new platforms, the EGX, for example that was just announced, a great workload there is a 5G telco. So we'll be working with our friends at HPE to take that to market as well. And, you know, really, there's a lot of different workloads and they've got a great portfolio of products across the spectrum from regular servers. And 1U, 2U, and then all the way up to their big Apollo platform. >> Well I'm glad you brought up telco, I'm curious, are there any specific, applications or workloads that, where the low hanging fruit or the kind of the first targets that you use for AI acceleration? >> Yeah, so you know, the 5G workload is just awesome. We're introduced with the EGX, a new platform called Ariel which is a programming framework and there were lots of partners there that were part of that, including, folks like Ericsson. And the idea there is that you have a software defined hardware accelerated radio area network, so a cloud RAM and it really has all of the right attributes of the cloud and what's nice there is now you can change on the fly, the algorithms that you're using for the baseband codex without having to go climb a radio tower and change the actual physical infrastructure. So that's a critical part. Our role in that, on the networking side, we introduced the technology that's part of EGX then are connected, It's like the DX adapter, it's called 5T for 5G. And one of the things that happens is you need this time triggered transport or a telco technology. That's the 5T's for 5G. And the reason is because you're doing distributed baseband unit, distributed radio processing and the timing between each of those server nodes needs to be super precise, 20 nanosecond. It's something that simply can't be done in software. And so we did that in hardware. So instead of having an expensive FPGA, I try to synchronize all of these boxes together. We put it into our NIC and now we put that into industry standard servers HP has some fantastic servers. And then with the EGX platform, with that we can build, really scale out software to client cloud RAM. >> Awesome, Paresh, anything else on the application side you'd like to add in just about what Kevin spoke about. >> Oh yeah, so from application perspective, every industry has applications that touch on edge. If you take a look at the retail, for example, there is, you know, all the way from supply chain to inventory management, to keeping the right stock units in the shelves, making sure there is a there is no slippage or shrinkage. So to telecom, to healthcare, we are re-looking at constantly monitoring patients and taking actions for the best outcomes to manufacturing. We are looking to automate production detecting failures much early on in the production cycle and so on every industry has different applications but they all use AI. They can all leverage the computing capabilities and high-speed networking at the edge to transform their business processes. >> All right, well, it's interesting almost every time we've talked about AI, networking has come up. So, you know, Kevin, I think that probably ease up a little bit why, Nvidia, spent around $7 billion for the acquisition of Mellanox and not only was it the Mellanox acquisition, Cumulus Networks, very known in the network space for software defined really, operating system for networking but give us strategically, does this change the direction of Nvidia, how should we be thinking about Nvidia in the overall network? >> Yeah, I think the way to think about it is going back to that data center as the computer. And if you're thinking about the data center as computer then networking becomes the back plane, if you will of that data center computer and having a high performance network is really critical. And Mellanox has been a leader in that for 20 years now with our InfiniBand and our Ethernet product. But beyond that, you need a programmatic interface because one of the things that's really important in the cloud is that everything is software defined and it's containerized now and there is no better company in the world then Cumulus, really the pioneer and building Cumulus clinics, taking the Linux operating system and running that on multiple homes. So not just hardware from Mellanox but hardware from other people as well. And so that whole notion of an open networking platform more committed to, you need to support that and now you have a programmatic interface that you can drop containers on top of, Cumulus has been the leader in the Linux FRR, it's Free Range Routing, which is the core routing algorithm. And that really is at the heart of other open source network operating systems like Sonic and DENT so we see a lot of synergy here, all the analytics that Cumulus is bringing to bear with NetQ. So it's really great that they're going to be part here of the Nvidia team. >> Excellent, well thank you both much. Want to give you the final word, what should they do, HPE customers in their ecosystem know about the Nvidia and HPE partnership? >> Yeah, so I'll start you know, I think HPE has been a longtime partner and a customer of ours. If you have accelerated workloads, you need to connect those together. The HPE server portfolio is an ideal place. We can combine some of the work we're doing with our new amp years and existing GPUs and then also to connect those together with the M series, which is their internet switches that are based on our spectrum switch platforms and then all of the HPC related activities on InfiniBand, they're a great partner there. And so all of that, pulling it together, and now as at the edge, as edge becomes more and more important, security becomes more and more important and you have to go to this zero trust model, if you plug in a camera that's somebody has at the edge, even if it's on a car, you can't trust it. So everything has to become, validated authenticated, all the data needs to be encrypted. And so they're going to be a great partner because they've been a leader and building the most secure platforms in the world. >> Yeah and on the data center, server, portfolio side, we really work very closely with HP on various different lines of products and really fantastic servers from the Apollo line of a scale up servers to synergy and ProLiant line, as well as the Edgeline for the edge and on the super computing side with the pre side of things. So we really work to the fullest spectram of solutions with HP. We also work on the software side, wehere a lot of these servers, are also certified to run a full stack under a program that we call NGC-Ready so customers get phenomenal value right off the bat, they're guaranteed, to have accelerated workloads work well when they choose these servers. >> Awesome, well, thank you both for giving us the updates, lots happening, obviously in the AI space. Appreciate all the updates. >> Thanks Stu, great to talk to you, stay well. >> Thanks Stu, take care. >> All right, stay with us for lots more from HPE Discover Virtual Experience 2020. I'm Stu Miniman and thank you for watching theCUBE. (bright upbeat music)

(funky guitar music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California, This is theCUBE conversation. >> Everyone welcome to this CUBE conversation here in Palo Alto, California CUBE studios, I'm John Furrier, the host of theCUBE. Were with CUBE alumni, special guest Amit Walia, President of Products & Marketing at Informatica. Amit, it's great to see you. It's been a while. It's been a couple of months, how's things? >> Good to be back as always. >> Welcome back. Okay, Informatica worlds is coming up, we have a whole segment on that but we have been covering you guys for a long long time, data is at the center of the value proposition again and again, it's more amplified now, the fog is lifting. >> Sure. >> And the world is now seeing what we were talking about four years ago. (giggles) >> Yeah. >> With data, what's new? What's the big trends that going on that you guys are doubling down on? What's new, what's changed? Give us the update. >> Sure. I think we have been talking the last couple of years, I think your right, data has becoming more and more important. I think, three things we see a lot. One is obviously, you saw this whole world of digital transformation. I think that has de faintly has picked up so much steam now. I mean, every company is going digital and obviously that creates a whole new paradigm shift for companies to carry out almost recreate themselves, rebuild them, so data becomes the new definition. And that's what we call those things you saw at Infomatica even before data3.org, but data is the center of everything, right? And you see the volume of data growth, you know, the utilization of data to make decisions, whether it's, you know, decisions on the shop floor, decisions basically related to cyber security or whatever it is. And the key to what you see different now is the whole AI assisted data management. I mean the scale of complexity, the scale of growth, you know, multi-cloud, multi-platform, all the stuff that is in front of us, it's really difficult to run the old way of doing things, so that's why we see one thing that we see a whole lot is AI is becoming a lot more mainstream, still early days but it's assisting the whole ability for companies, what I call, exploit data to really become a lot more transformative. >> You have been on this for a while, again we can go back to theCUBE archives, we can almost pull out clips from two years ago, be relevant today, you know, the data control, understanding >> Yeah. >> Understanding where the data governance is-- >> Sure. >> That's always a foundational thing but you guys nailed the chat bots, you have been doing AI was previous announcements, this is putting a lot of pressure on you, the president of the products, you got to get this out there. >> What's new? What's happening inside Informatica? pedaling as fast as you can? What is some of the updates? >> No. >> Gives us the-- >> The best example always is like a duck, right? Your really swimming and feel things are calm at the top and then you are really paddling. No, I think it's great for us. I think, I look at AI's, AI is like, there is so much FUD [fear, uncertainty and doubt] around it and machine learning AI. We look at it as two different ways. One is how we leverage machine learning within our products to help our customers. Making it easy for them, like I said, so many different data types, think of IOT data, unstructured data, streaming data, how do you bring all that stuff together and marry it with your existing transactional data to make sense. So, we're leveraging a lot of machine learning to make the internal products a lot more easier to consume, a lot more smarter, a lot more richer. The second thing is that, we're what we call it our AI, CLAIRE, which we unveiled, if you remember, a couple of years ago at the Informatica World. How that then helps our customers make smarter decisions, you know, in data science and all of these data workbenches, you know, the old statistical models is only as good as they can ever be. So, we leveraging helping our customers see the value proposition of our AI, CLAIRE, then to what I make things that, you know, find patterns, you know, statistical models cannot. So, to me I look at both of those really, leveraging ML to shape our products, which is where we do a lot of innovation and then creating our AI, CLAIRE, to help customers to make smarter decisions, easier decisions, complex decisions, which I called the humans or statistical models, really cannot. >> Well this is the balance with machines and humans. >> Right. >> working together, you guys have nailed this before and I'm, I think this was two years ago. I started to hear the words, land, adopt, expand, form you guys, right? Which is, you got to get adoption. >> Right. >> And so, as you're iterating on this product focus, you got to getting working, making secure your products-- >> Big, big maniacal focus on that one. >> So, tell me what you have learned there because that's a hard thing. >> Right. >> You guy are doing well at it. You got to get adoption, which means you got to listen customers, you got to do the course correction. >> Yeah. >> what's the learnings coming out of that piece of that. >> That's actually such a good point. We've made such, we've always been a customer centric company but as you said, like, as whole world shifted towards a new subscription cloud model, we've really focused on helping our customers adopt our products and you know, in this new world, customers are struggling with new architectures and everything, so we doubled down on what we called customer success. Making sure we can help our customers adopt the products and by the way it's to our benefit. Our customers get value really quickly and of course we believe in what we call a customer for life. Our ability to then grow with our customers and help them deliver value becomes a lot better. So, we really focused, so, we have globally across the board customers, success managers, we really invest in our customers, the moment a customer buys a product from us, we directly engage with them to help them understand for this use case, how you implement the product. >> It's not just self service, that's one thing that I appreciate 'cause I know how hard it is to build products these days, especially with the velocity of change but it's also when you have a large scale data. >> Yeah. >> You need automation, you got to have machine learning, you got to have these disciplines. >> Sure. >> And this is both on your end and but also on the customer. >> Yes. >> Any on the updates on the CLAIRE and some customer learnings you're seeing that are turning into use cases or best practices, what are some of them? >> So many of them. So take a simple example, right? I mean, we think of, we take these things for granted, right? I mean, take note, we don't talk about IOB these days right? All these cell cells, we were streaming data, right? Or even robots on the shop floor. So much of that data has no schema, no structure, no definition, it's coming, right? Netflix data and for customers there is a lot of volume in it, a lot of it could be junk, right? So, how do you first take that volume of data? Create some structure to it for you to do analytics. You can only do analytics if you put some structure to it, right? So, first thing is I've leverage CLAIRE, we help our customers to create, what I call, schema and you can create some structure to it. Then what we do allow is basically CLAIRE through CLAIRE, it can naturally bring what we have the data quality on top of it, like how much of it is irrelevant, how much of it is noise, how much of it really makes sense, so, then, as you said it, signal from the noise We are helping our customers get signal from the noise of data. That's where it AI comes very handy because it's very manual, cumbersome, time consuming and sometimes very difficult to do. So, that's a area we have leveraged creating structure and data quality on top and finding rules that didn't naturally probably didn't exist, that you and me wouldn't be able to see. Machines are able to do it and to your point, our belief is, this is my 100% belief, we believe AI assisting the humans. We have given the value of CLAIRE to our users, so it complements you and that's where we are trying to help our users get more productive and deliver more value to you faster. >> Productivity is multifold, it's like, also, efficiency, people wasting time on project that can be automated, so you can focus that valuable resource somewhere else. >> Yeah. >> Okay, let's shift gears onto Informatica World coming up. Let's spend some time on that. What's the focus this year, the show, it's coming up, right around the corner, what's going to be the focus? What's going to be the agenda? What's on the plate? >> Give you a quick sense on how it's shape up, it's probably going to be our Informatica World. So, it's 20th year, again back in Waze, you know, we love Waze of course. We have obviously, a couple of days lined up over there, I know you guys will be there too. A great set of speakers. Obviously, we will have me on stage, speakers like, we'll have some, the CEO of Google Cloud, Thomas Kurian is going to be there, we'll have on the main stage with Anil, we'll have the CEO of Databricks, Ali, with me, we'll also have CMO of AWS, Ariel, there, then we have a couple of customers lined up, Simon from Credit Suisse, Daniel is the CDO of Nissan, we also have the Head of AI, Simon Guggenheimer from Microsoft as well as the Chief Product Officer of Tableau, Francois Ajenstat, so, we have a great line up of speakers, customers and some of our very very strategic partners with us. If you remember last year, We also had Scott Guthrie there main stage. 80 plus sessions, pretty much 90% lead by customers. We have 70 to 80 customers presenting. >> Technical sessions or going to be a Ctrack? >> Technical, business, we have all kinds of tracks, we have hands on labs, we have learnings, customers really want to learn our products, talk with the experts, some want to the product managers, some want to talk to the engineers, literally so many hands on labs, so, it's going to be a full blown couple of days for us. >> What's the pitch for someone watching that never been Informatica World? Why should they come for the show? >> I'll always tell them three things. Number one is that, it's a user conference for our customers to learn all things about data management and of course in that context they learn a lot about. So, they learn a lot about the industry. So, day one we kick it off by market perspectives. We are giving a sense on how the market is going, how everybody is stepping back from the day to and understanding, where are these digital transformation, AI, where is all the world of data going. We've got some great annalists coming, talkings, some customers talking, we are talking about futures over there. Then it is all about hands on learning, right?, learning about the product. Hearing from some of these experts, right?, from the industry experts as well as our customers, teaching what to do and what not to do and networking, it's always go to network, right, it's a great place for people to learn from each other. So, it's a great forum for all those three things but the theme this year is all about AI. I talked about CLAIRE, I'll in fact our tagline this year is, Clarity Unleashed. We really want, basically, AI has been developing over the last couple of years, it's becoming a lot more mainstream, for us in our offerings and this year we're really taking it mainstream, so, it's kind of like, unleashing it for everybody can genuinely use it, truly use it, for the day to day data management activities. >> Clarity is a great theme, I mean, it plays on CLAIRE but this is what we're starting to see some visiblility into some clear >> Yeah. >> Economic benefits, business benefits. >> Yep. >> Technical benefits, >> Yep. >> Kind of all starting to come in. How would you categorize those three areas because you know, generally that's the consensus these days that what was once a couple years ago was, like, foggy when you see, now you're starting to see that lift, you're seeing economic, business and technical benefits. >> To me it's all about economic and business. So, technology plays a role in driving value for the business, right, I'm a full believer in that, right, and if you think about some of the trends today, right, a billion users are coming into play that will be assisted by AI. Data is doubling every year, you know the volume of data, >> Yep. >> The amount of, and I always say business users today, I mean, I run a business, I want, I always say, tomorrow data, yesterday to make a decision today. It's just in time and that's where AI comes into play. So our goal is to help organizations transform themselves, truly be more productive, reduce operation cost, by the way governance and compliance, that's becoming such a mainstream topic. It's not just basically making analytical decisions. How do you make sure your data is safe and secure, you don't want to get basically get hit by all of these cyber attacks, they're all are coming after data. So, governance, compliance of data that's becoming very, so, those-- >> Again you guys are right on the data thing. >> Yeah. >> I want to get your reaction, you mentioned some stats. >> Sure. >> I've got some stats here. Data explosion, 15.3 zettabytes per year >> Yeah, in global traffic. >> Yeah. >> 500 million business data users and growing 20 billion in connected devices, one billion workers will be assisted by machine learning, so, thanks for plugging those stats but I want to get your reaction to some of these other points here. 80% of enterprises are looking at multicloud, their really evaluating where the data sits in that equation >> Sure. And the other thing is the responsibility and role of the Chief Data Officer >> Yes. >> These are new dynamics, I think you guys will be addressing that into the event. >> Absolutely, absolutely. >> Because organizational dynamics, skill gaps are issues but also you have multicloud. So your thoughts on those to. >> That's a big thing, look at, in the old world, John, Hidrantes is always still in large enterprises, right, and it's going to stay here. In fact I think it's not just cloud, think of it this way, on-premise is still here, it's not going a way. It's reducing in scope but then you have this multicloud world, SAS apps, PAS apps, infrastructure, if I'm a customer, I want to do all of it but the biggest problem is that my data is everywhere, how do I make sense of it and then how do I govern it, like my customer data is sitting somewhere in this SAS app, in that platform, on this on-prem application transaction app I'm running, how do I connect the three and how do I make sense it doesn't get, I can have a governance control around it. That's when data management becomes more important but more complex but that's why AI comes in to making it easier. What are the things we've seen a lot, as you touched upon, is the rise of CDO. In fact we have Daniel from Nissan, she is the CDO of Nissan North America, on main stage, talking about her role and how they have leveraged data to transform themselves. That is something we're seeing a lot more because you know, the role of the CDO is making sure that is not only a sense of governance and compliance, a sense of how do we even understand the value of data across an enterprise. Again, I see, one of the things we going to talk about is system thinking around data. We call it System Thinking 3.0, data is becoming a platform. See, there was OSA-D hardware layer whether it is server, or compute, we believe that data is becoming a platform in itself. Whether you think about it in terms of scale, in terms of governance, in terms of AI, in terms of privacy, you have to think of data as a platform. That's the other big thing. >> I think that is a very powerful statement and I like to get your thoughts, we had many conversations on camera, off camera, around product, Silicon Valley, Venture Capital, how can startups create value. On of the old antigens use to be, build a platform, that's your competitive strategy, you were a platform company and that was a strategic competitive advantage. >> Yes. >> That was unique to the company, they created enablement, Facebook is a great example. >> Yeah. >> They monetized all the data from the users, look where they are. >> Sure. >> If you think about platforms today. >> Sure. >> It seems to be table steaks, not as a competitive advantage but more of a foundational. >> Sure. >> Element of all businesses. >> Yeah. >> Not just startups and enterprises. This seems to be a common thread, do you agree with that, that platforms becoming table steaks, 'cause of if we have to think like systems people >> Mm-hmm. >> Whether it's an enterprise. >> Sure. >> Or a supplier, then holistically the platform becomes table steaks on premer or cloud. Your reaction to that. Do you agree? >> No, I think I agree. I'll say it slightly differently, yes. I think platform is a critical component for any enterprise when they think of their end to end technology strategy because you can't do piece meals otherwise you become a system integrator of your own, right? But it's no easy to be a platform player itself, right, because as a platform player, the responsibility of what you have to offer your customer becomes a lot bigger. So, we obviously has this intelligent data platform but the other thing is that the rule of the platform is different too. It has to be very modular and API driven. Nobody wants to buy a monolithic platform. I don't want to, as a enterprise, I don't buy all now, I'm going to implement five years of platform. You want it, it's going to be like a Lego block, okay you, it builds by itself. Not monolithic, very API driven, maybe microservices based and that's our belief that in the new world, yes, platform is very critical for to accelerate your transformational journeys or data driven transformational journeys but the platform better be API driven, microservices based, very nimble that is not a percussor to value creation but creates value as you go along. >> It's all, kind of up to, depends on the customer it could have a thin foundational data platform, from you guys for instance, then what you're saying, compose. >> Of different components. >> On whatever you need. >> For example you have data integration platform, you can do data quality on top, you can do master data management on top, you can provide governance, you can provide privacy, you can do cataloging, it all builds. >> Yeah. >> It's not like, oh my gosh, I have go do all these things over the course of five years, then I get value. You got to create value all along. >> Yeah. >> Today's customers want value like, in two months, three months, you don't want to wait for a year or two. >> This is the excatly the, I think, the operating system, systems mindset. >> Yes. >> You were referring too, this is kind of how enterprises are behaving now. There is the way you see on-premise, >> Yep. >> Thinking around data, cloud, multicloud emerging, it's a systems view distributed computing, with the right Lego blocks. >> That's what our belief is. That's what we heard from customers. See our, I spend most of my time talking to customers and are we trying to understand what customers want today and you know, some of this latent demands that they have, sometimes can't articulate, my job, I always end up on the road most of the time, just hearing customers, that's what they want. They want exactly to your point, a platform that builds, not monolithic, but they do want a platform. They do want to make it easy for them not to do everything piece meal. Every project is a data project. Whether it's a customer experience project, whether it's a governance project, whether it's nothing else but a analytical project, it's a data project. You don't repeat it every time. That's what they want. >> I know you got a hard stop but I want to get your thoughts on this because I have heard the word, workload, mentioned so many more times in the past year, if there was a tag cloud of all theCUBE conversations where the word workload was mentioned, it would be the biggest font. (laughs) >> Yes. >> Workload has been around for a while but now you are seeing more workloads coming on. >> Yeah. >> That's more important for data. >> Yes. >> Workloads being tied into data. >> Absolutely. >> And then sharing data across multiple workloads, that's a big focus, do you see that same thing? >> We absolutely see that and the unique thing we see also is that newer workloads are being created and the old workloads are not going away, which is where the hybrid becomes very important. See, we serve large enterprises and their goal is to have a hybrid. So, you know, I'm running a old transaction workload order here, I want to have a experimental workload, I want to start a new workload, I want all of them to talk to each other, I don't want them to become silos and that's when they look to us to say connect the dots for me, you can be in the cloud, as an example, our cloud platform, you know last time, we talked about a 5 trillion transactions a month, today is double that, eight to ten trillion transactions a month. Growing like crazy but our traditional workload is also still there so we connect the dots for our customers. >> Amit, thank you for coming on sharing your insights, obviously you guys are doing well. You've got 300,000 developers, billions in revenue, thanks for coming on, appreciate the insight and looking forward to your Informatica World. >> Thank you very much. >> Amit Walia here inside theCUBE, with theCUBE conversation, in Palo Alto, thanks for watching.

>> Announcer: Live from San Francisco, it's theCUBE! Covering Informatica World 2017. Brought to you by Informatica. >> Welcome back, everyone. We're live in San Francisco for CUBE's exclusive coverage of Informatica World 2017. I'm John Furrier, SiliconANGLE. Our next guest, Anil Chakravarthy who's the CEO of Informatica, CUBE alumni multiple times, but the chief executive officer leading the charge of a great private company doing very well. Welcome back to theCUBE. >> It's great to be here, John. Thanks very much. >> We've got a couple of things to talk about, but I want to just jump in. Behind us you see the new logo, Informatica. Really kind of the last leg of the stool, if you will, you guys have gone private, >> Yep. >> Great product work over the years. You know I've been pretty complimentary of you guys, although we've had a critical analysis session yesterday. But all the big bets were very well done playing off. You've got a great product team, great leadership team, new CIO hire. But the last leg of the stool is the brand. >> Anil: That's right. >> You guys haven't been showboating much. Now you got to kind of brag and be humble about it and get the word out. New marketing program, what's that all about? >> Yeah that's exactly right. So you just said, the transformation that we are going through, three big steps is the transformation. The product portfolio transformation, we've been talking about that. This is all driven by cloud, by big data, and machine learning, and all of that. Then the transformation of the business model, from license to subscription and cloud services. And now the brand transformation. And we see the brand transformation as actually catching up to where the company actually was. We were just talking about that right before we got started. We actually have done a lot of things. Like for instance, did you know that we are doing 1 trillion transactions a month in the cloud? I mean, very few people knew about that. >> Yeah, what's more impressive on that, I found that out earlier it was 1 billion in January. >> Anil: It's unbelievable. It's-- >> I mean how do you do that? It's a growth hockey stick, straight up. >> It's a hockey stick, it's huge, it's huge growth, and that's driven by the fact that we are the leader in cloud data management for the biggest ecosystems, for Salesforce, for Amazon, for Azure, and that drives a lot of the data volume across the cloud. >> Before we get in the keynotes, on that note, one of the big bets you know I've been very impressed on is the cloud play, right? The data architecture of things, the winning formula. But you got cloud presses, you had Amazon Web Services. Google just announced span or horizontally scalable database, generally available. You were on the of the three data partners on the front end of that. >> Anil: That's correct. >> And part of the launch of Google. >> That's correct, yeah. >> I didn't know that. >> So you know, the way we think of the world is from our customer's perspective. It really is the best way to think about it is as the enterprise cloud. Put it together. All the data you have in the enterprise that you have generated over the years, that's still very valuable data. And then the data you have in the cloud. And you can't think of those two things as separate. For instance, you could have customer data, the same customer. John, you're the customer of a retailer. Some of that data about you is in their on-premises systems, and some of the data may be in a cloud system, but it is all interconnected data and you can't have two separate silos. We believe that we are the only ones that can really manage that. And that's why we are supporting every major cloud platform or cloud system, just like we are supporting every major on-premises system. >> Yeah, you guys call it Switzerland. It was a great way to describe it. But really to me it puts bigger than that, is that you guys make data ready. And that's really the value of what I call the tier two data layer that's building, where you've got stuff in memory, I get that, it's some odyssey streaming stuff, and things going on there. But now, then you have third tier, archive, but data tier two is just like all the data: IoT, structured data. That's growing, but the cost of storage is getting lower and lower. Now companies are incented to store. How is that impacting your business? We heard that at DellEMC World over and over and obviously they're in the storage business, but the tier two storage is significantly growing. >> Well data is still growing at over 25% a year. That's a huge number given all the way the size that you have, so it's going to be within by 2020, it'll be over 15 zetabytes, and a zetabyte, for those of you who are interested, is 10 to the 21. That's a huge amount of data. And what we're seeing is, the value comes from being able to first of all see your way through the data, being able to understand what data is valuable and what's not, and then connect the data. If you have customer data, product data, location data, et cetera, being able to put all of that together. That's really where the value comes from. >> So I've got to ask you about your keynote. You talk about the digital transformation's unfolding and data is the critical foundation for digital transformation. Okay, we've heard digital transformation. I mean, I'm not to say it's played, I know you guys have your theme, but this business transformation going on. So digital transformation is a known trend, but it kind of is played in my mind. I want to know what's different about Informatica now. Why is it unfolding now versus two years ago when we started talking about digital transformation? What's the most relevant thing now? >> Well I think the biggest relevance is, two years ago, as you exactly said, people were talking about digital transformation. Now they're doing digital transformation. Now you're seeing, you know, we talk about our own customers like Tesla or GE or Amazon doing it, but lots of other customers are actually doing the digital transformation. Now when you first take the first step toward the digital transformation, that's when you realize, my data, I got to fix the data foundation. If I can't have a data foundation, then I just, you know, everybody cares about a good customer experience. If I can't tell all the interactions a customer has with my company, and that data is in different places, there is no way I can provide a good customer experience because the customer knows what they're doing with me and I don't know what they're doing with me. And that's really the foundation for the data foundation. >> I want you to take a minute to just re-explain that because this is something comes up all the time and I get different answers and people have different definitions. What does it mean to have a digital data foundation and what are some of the impacts to the customers when they do have that? >> Think of it the simplest way. Let's say you have a customer and a lot of the new customers are like that. You are a bank, and you have a customer who doesn't want to talk to anybody. They only want to do everything through a mobile application. They want to file a loan application through the mobile, they want to check their balance through mobile, they want to deposit a check through mobile, et cetera, et cetera. If they have a problem, they might talk to somebody through a chat on a mobile, but they don't really want to talk to a live person. And this is, by the way, a common scenario now. Now they are doing probably 20 different things through the mobile. But when you get into your back end, that's the front end. You can put 20 things on the mobile, but the back end you've got 20 different things. But you have to have a single picture across those 20 things. When did the customer interact with us? What did they do? What is the pattern of that customer? How do you profile what the customer is doing? If you don't have that picture, everything that you do with the customer is going to just appear disconnected to them. It's going to frustrate them even more. And that's really the reason we have to have the data foundation. >> Okay, so, that's kind of a data layer, I get that, and believe me, horizontally-scalable data, making it accessible only helps the apps. The question to you is, your reaction to people saying, "Hey, Anil, I got to be innovative. "I got to free the data up and I got to let it grow "and you know a thousand flowers bloom, all this goodness. "But hey, I got to control it." So that's a huge issue. I've got governance, I've got compliance, there's laws now. So am I stuck in the mud? I want to be innovative and go fast, but now I've got to govern it and control it. How do you answer that question? >> You can do both now and that's the reason why we're announcing CLAIRE and all these innovations that we announced this. The advent of machine learning and metadata let's you do both. You basically say, look, I can use all these new technologies to find out what data I have. It's not going to slow you down. In fact, if you set up something like an intelligent data lake, because it has the metadata layer, you are actually opening up the data you have to the end user without having to come through IT for every piece of data, which means they can go faster. That's where the innovation happens. So you can do both. >> John: So it's a control catalog, basically. >> It's exactly right. It's a controlled catalog and you basically get to define different levels of trust. You can say, this data is curated data, it's trusted data and we can vouch for it. And maybe other data that's just shared collaboratively, and you can just flag it and then that way the user knows, okay here's data that I'm getting from a central system and this is what I need to use when I'm talking about something like revenue. And I'm tying something like a trend of what's going on. I might be able to use other data and that's the key there. >> Talk about the trend around CLAIRE. A lot of buzz here at the show. CLAIRE stands for clairvoyant. It's got the word AI in it. It's a name. SAP's got Leonardo, Salesforce has Einstein, all these different terms, but it's a clever way to point to AI, augmented intelligence, and machine learning. >> Anil: Correct. >> What does that mean for Informatica as a company? Certainly it kind of humanizes it. >> Anil: Correct. >> Shows the access of data should be democratized. What does it mean for you guys and the customers? How does that play out in your mind as the CEO? What do you see CLAIRE doing? >> Well the three big points I'll make about CLAIRE. First of all, when we built CLAIRE, we did not invent the artificial intelligence or the machine learning. A lot of that is already available. So we took a lot of the best algorithms in machine learning and applied them to metadata and applied them to data management. That's the secret sauce. It's not the building the AI itself, it's the use of the AI for data management. That's number one. Second, we defined CLAIRE very clearly and we said it's not a product. It's an engine, it's an AI-powered engine. In fact, I call for CLAIRE, I say it's cloud-scale, AI-powered real time engine, that's CLAIRE. Right, so it's an acronym, but it's the engine that powers other products. The third big thing is we're telling customers, you're going to get the benefit of CLAIRE, but you don't need to deploy CLAIRE. When you buy any of our products that are powered by CLAIRE or any of our solutions that are powered by CLAIRE, that will automatically come in there. So it means once you have any product like our enterprise information catalog or our secured source or data governance, you're starting to use CLAIRE and then you can use CLAIRE for other use cases as well. >> What's been the reaction? You know, and obviously you get nervous, CEO, probably got these things out there, probably wonder what the reaction is. What's your take on the reaction? >> People are very intrigued. I know that's what they, they look at CLAIRE and go, what is CLAIRE? How are you guys using it? I think people are asking us, tell us a little bit more about how AI is being used in the world of data and data management. So it's absolutely the reaction we wanted. >> So I got to ask you this question. I asked Mark Hurd the same question at the Oracle media day a few weeks ago. I want to ask you the same question. Everyone's number one at everything now. You guys are number one in six quadrants. Oracle's number one, the Dell E's. Everyone's number one at something. So the question really is, not so much about being number one, congratulations, you've got some magic quadrant wins that was highlighted in the keynote. But you guys are going through a transformation. You're telling your customers that they're going through a transformation. Wouldn't it make sense that the transformation scoreboard looks different than the old way? And I want to get your thoughts on this because, not that we have the answer, but there's one answer in customer wins, but as this new world transforms and unfolds, what's the scoreboard look like? How, because it's not as clean to say, this is the category, you're starting to see a little blending, as you mentioned how data is evolving. What's the new scoreboard look like? >> Is it the scoreboard for us or for the customer? >> John: You guys, the industry. How do I know if you're doing well? Obviously customer wins is obviously number one. >> Yeah, I think the best way to. I'll give you a couple of metrics, financial and nonfinancial, okay. From a nonfinancial perspective, as you said, a couple of key metrics. One is customers. How many new customers, how many new customers, reference customers do we have? Second one that you want to look at is just mind share or when people think about digital transformation, do they think of, hey, Informatica, they have a key role in my digital transformation. Just looking at mind share and so on, because that's a good leading indicator. In terms of the nonfinancial, or the financial metrics for us, obviously as more customers do what we call enterprise cloud data management, you're going to see our subscription revenue grow dramatically and you know, that's something that when you look at our subscription revenue, you'll see that impact of the enterprise cloud data management. >> And you guys made the move to subscription, obviously went private. Bruce Chizen and Jerry Held, your board members talked about this. You can do a lot of things 'cause it doesn't, it impacts the P&L but that it's still baking out, it's evolving, you're private, not public, but you want to get it right before you go public. >> That's correct. >> How do you feel about the progress on that front now? >> Oh we're making fabulous progress. We're very pleased with where we are. From my perspective, we are ahead of where we thought we would be by this time. I think customer buying behavior has converged really nicely with where we are in terms of where we want to go. So I think that's definitely been a big plus. >> Sally Jenkins, your new CMO, you got to feel good about her coming on the board-- >> Anil: Oh she's done a great job. >> High impact. She said on theCUBE that you guys are the hottest privately held pre-IPO startup. >> Anil: That's right. >> Twenty years in the making, whatever. I mean, but you guys are private. >> Billion dollar startup. >> But you act like a startup, which is why we like you guys a lot. You guys are like a very hustling like a startup. But now you're growing and you're getting beyond the 200 million, over a billion dollars now. When's the IPO coming? >> Yeah, I mean, you know look, I can tell you the factors that will be the lead to the perfect timing for the IPO. When those factors come together, I don't have a crystal ball right now, but I can tell you it weighs both on us and the market. From our perspective, we are making this big shift in the business model. We want to make sure that we can say, hey look, now the shift is very clear and stable and we can see where they where you know we'll be able to project out our own forecast for the next three, four quarters. So that's one key indicator for us. The second key indicator that we look at is the total revenue growth of the company and what percent of the growth of the revenue is recurring revenue for us. So we're going to be looking at those two factors. And of course from the market perspective, we want to make sure that the market wants to, continues to be. >> If you wait four years til we have a new president, and then heard all the politics from the Kara Swisher thing was, got a lot of people stirred up, in the conversation. But in all seriousness now, you also have private equity so you have to make the company worth money after they go public so you've got to have some growth left in you, right, I mean you guys are, you feel good about the? >> Oh we really do because you know, we look, that's where these six categories that we talked about make a lot of sense. You look at data integration, data quality, master data management, these are all categories that are well established. We know the patterns and we are seeing very good growth in those categories. Then you look at the new categories: cloud, big data management, data security. Those are all coming into their own right now. So that's why when you look at our portfolio, you go, wow, there are some that you already have great, well established and going well. These other ones, they're well established but they also have a lot of promise and future growth. >> Great chatting with you. You're a great, insightful, and inspiration. You guys have done a great job. But I've got to ask you the question because I think you have an interesting role. I mean, you have, you're acting like a startup, but you're not a startup. You went private from a public company. You've got a great board of directors. You've got Jerry Held and Bruce Chizen on there, but you've also got private equity sharks on the board. So, that's my definition, I won't say you said that. >> No, no, but I was actually in the private equity world, to my pleasant surprise, I've seen the whole spectrum of investors and our guys on the board are very much growth-oriented. They know that the value gets created for them through growth so it's well aligned. >> Yeah, but you're not sitting back having pizza and drinking wine. These PE guys, they're financially driven. >> Anil: That's right. >> So the question is, advice to other startups, whether they're venture backed or other companies going through innovation strategy. How do you manage the success of having such good product excellence? I know you've got good people, so that's an easy one answer. How as a CEO do you maintain the disciple to have the cadence of the financial performance? Because those guys look, they're probably not going to give you, hey how we doing? Numbers matter, but you're transforming technology and products. >> That's right, so what we do is-- >> How do you do it? >> We have a scorecard which has both the short term and the longterm metrics and we look at both of those. You know, we do monthly business reviews. So the pulse of the company has definitely quickened. We're operating at a new level of intensity. But when we look at the scorecard, it's not just the immediate financial metrics. It's things like, for example, are we building the back end infrastructure to be a subscription company? That doesn't get done in a month. >> John: That's an IT challenge, right? >> That's an IT challenge, a process challenge, it takes 12 months, 18 months, the kind of things that you talk with Graeme about. But that is an example of, you can have a scorecard. You don't necessarily have to look at a scorecard just for the short term metrics. You look at it for both short term and what makes you successful over the longterm. And that's, you know, that's what we're doing is just keep our eye on the ball, focus on a few things, both short term and longterm, and make sure we're doing them well. >> How about customer wins? To me, that's the scoreboard ultimately as we look at it at our team. How are you doing on customer wins? Can you share some, I see you have a lot of great customers. I met a few last night, obviously big wigs, big names. >> Anil: Yeah, exactly. >> What are some of the big wins look like and why are you winning? >> Well you know, we have 7,000 plus customers. We have a great customer base. Just at this show we've had 85% of our sessions here at the show have had customer or partner speakers. That gives you a sense of customers want to talk about us. A couple of ones that I would highlight for example, which are fairly recent for example, Amazon is one. They just spoke at the show and in fact the CMO of Amazon was here, Ariel Kelman. And he spoke about he is a customer of Informatica and how he's using Informatica for his own marketing systems and the marketing data analytics that he is doing. Another example is Tesla. You know, we talked about them at the show. >> I got a test drive on Friday with one. >> There you go, exactly, and then they are using us for the Tesla and the Solar City acquisition and driving synergies there. So lots of great examples. >> John: Tough customers, by the way, very, very finicky. >> Oh they are very demanding, very demanding customers and we are really proud to be serving them. >> Okay, final question, Anil. What's next? How do you look forward. Obviously this event, congratulations on getting the branding out. Peggy and the team did a great job. Sally and the team did a great job. What about next? What's next? >> Yeah, you know, what's next for us is simply work with customers to first of all get our story out, understand their priorities, and make sure that they understand that we can be a great partner for them. So we believe that this is the beginning of that journey. We talked about digital transformation and how we help them. Now we take the show on the road to our customers, make sure that we help them at their pace to transform. >> So bring the message out, build the brand. >> Absolutely. >> That's the key priority. >> And then continue. >> Product side, what's going on the products? >> Well on the product side, for instance, you saw a teaser of all the big trends. Machine learning, cloud, big data, security, all of these have full-fledged roadmaps that we're going to be working on over the course of the next six months. >> Anil, great to see you. Congratulations, you can tell, you're still intense. You've got the intensity, it's not going to stop by the way. >> Anil: No it's not. >> It's not like you're not going to get more intense as you guys grow. And congratulations. >> Thank you for having me on your show. >> We are here live in San Francisco for Informatica World 2017 with the CEO here, Anil Chakravarthy, inside theCUBE. I'm John Furrier. Thanks for watching. Stay with us for more coverage from Informatica World after this short break. (techno music)

>> 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)