>> And it'll be the fastest 15 minutes of your day from there. >> In three- >> We go Lisa. >> Wait. >> Yes >> Wait, wait, wait. I'm sorry I didn't pin the right speed. >> Yap, no, no rush. >> There we go. >> The beauty of not being live. >> I think, in the background. >> Fantastic, you all ready to go there, Lisa? >> Yeah. >> We are speeding around the horn and we are coming to you in five, four, three, two. >> Hey everyone, welcome to theCUBE's coverage of AWS re:Invent 2022. Lisa Martin here with you with Subbu Iyer one of our alumni who's now the CEO of Aerospike. Subbu, great to have you on the program. Thank you for joining us. >> Great as always to be on theCUBE Lisa, good to meet you. >> So, you know, every company these days has got to be a data company, whether it's a retailer, a manufacturer, a grocer, a automotive company. But for a lot of companies, data is underutilized yet a huge asset that is value added. Why do you think companies are struggling so much to make data a value added asset? >> Well, you know, we see this across the board. When I talk to customers and prospects there is a desire from the business and from IT actually to leverage data to really fuel newer applications, newer services newer business lines if you will, for companies. I think the struggle is one, I think one the, the plethora of data that is created. Surveys say that over the next three years data is going to be you know by 2025 around 175 zettabytes, right? A hundred and zettabytes of data is going to be created. And that's really a growth of north of 30% year over year. But the more important and the interesting thing is the real time component of that data is actually growing at, you know 35% CAGR. And what enterprises desire is decisions that are made in real time or near real time. And a lot of the challenges that do exist today is that either the infrastructure that enterprises have in place was never built to actually manipulate data in real time. The second is really the ability to actually put something in place which can handle spikes yet be cost efficient to fuel. So you can build for really peak loads, but then it's very expensive to operate that particular service at normal loads. So how do you build something which actually works for you for both users, so to speak. And the last point that we see out there is even if you're able to, you know bring all that data you don't have the processing capability to run through that data. So as a result, most enterprises struggle with one capturing the data, making decisions from it in real time and really operating it at the cost point that they need to operate it at. >> You know, you bring up a great point with respect to real time data access. And I think one of the things that we've learned the last couple of years is that access to real time data it's not a nice to have anymore. It's business critical for organizations in any industry. Talk about that as one of the challenges that organizations are facing. >> Yeah, when we started Aerospike, right? When the company started, it started with the premise that data is going to grow, number one exponentially. Two, when applications open up to the internet there's going to be a flood of users and demands on those applications. And that was true primarily when we started the company in the ad tech vertical. So ad tech was the first vertical where there was a lot of data both on the supply set and the demand side from an inventory of ads that were available. And on the other hand, they had like microseconds or milliseconds in which they could make a decision on which ad to put in front of you and I so that we would click or engage with that particular ad. But over the last three to five years what we've seen is as digitization has actually permeated every industry out there the need to harness data in real time is pretty much present in every industry. Whether that's retail, whether that's financial services telecommunications, e-commerce, gaming and entertainment. Every industry has a desire. One, the innovative companies, the small companies rather are innovating at a pace and standing up new businesses to compete with the larger companies in each of these verticals. And the larger companies don't want to be left behind. So they're standing up their own competing services or getting into new lines of business that really harness and are driven by real time data. So this compelling pressures, one, you know customer experience is paramount and we as customers expect answers in you know an instant, in real time. And on the other hand, the way they make decisions is based on a large data set because you know larger data sets actually propel better decisions. So there's competing pressures here which essentially drive the need one from a business perspective, two from a customer perspective to harness all of this data in real time. So that's what's driving an incessant need to actually make decisions in real or near real time. >> You know, I think one of the things that's been in short supply over the last couple of years is patience. We do expect as consumers whether we're in our business lives our personal lives that we're going to be getting be given information and data that's relevant it's personal to help us make those real time decisions. So having access to real time data is really business critical for organizations across any industries. Talk about some of the main capabilities that modern data applications and data platforms need to have. What are some of the key capabilities of a modern data platform that need to be delivered to meet demanding customer expectations? >> So, you know, going back to your initial question Lisa around why is data really a high value but underutilized or under-leveraged asset? One of the reasons we see is a lot of the data platforms that, you know, some of these applications were built on have been then around for a decade plus. And they were never built for the needs of today, which is really driving a lot of data and driving insight in real time from a lot of data. So there are four major capabilities that we see that are essential ingredients of any modern data platform. One is really the ability to, you know, operate at unlimited scale. So what we mean by that is really the ability to scale from gigabytes to even petabytes without any degradation in performance or latency or throughput. The second is really, you know, predictable performance. So can you actually deliver predictable performance as your data size grows or your throughput grows or your concurrent user on that application of service grows? It's really easy to build an application that operates at low scale or low throughput or low concurrency but performance usually starts degrading as you start scaling one of these attributes. The third thing is the ability to operate and always on globally resilient application. And that requires a really robust data platform that can be up on a five nine basis globally, can support global distribution because a lot of these applications have global users. And the last point is, goes back to my first answer which is, can you operate all of this at a cost point which is not prohibitive but it makes sense from a TCO perspective. 'Cause a lot of times what we see is people make choices of data platforms and as ironically their service or applications become more successful and more users join their journey the revenue starts going up, the user base starts going up but the cost basis starts crossing over the revenue and they're losing money on the service, ironically as the service becomes more popular. So really unlimited scale predictable performance always on a globally resilient basis and low TCO. These are the four essential capabilities of any modern data platform. >> So then talk to me with those as the four main core functionalities of a modern data platform, how does Aerospike deliver that? >> So we were built, as I said from day one to operate at unlimited scale and deliver predictable performance. And then over the years as we work with customers we build this incredible high availability capability which helps us deliver the always on, you know, operations. So we have customers who are who have been on the platform 10 years with no downtime for example, right? So we are talking about an amazing continuum of high availability that we provide for customers who operate these, you know globally resilient services. The key to our innovation here is what we call the hybrid memory architecture. So, you know, going a little bit technically deep here essentially what we built out in our architecture is the ability on each node or each server to treat a bank of SSDs or solid-state devices as essentially extended memory. So you're getting memory performance but you're accessing these SSDs. You're not paying memory prices but you're getting memory performance. As a result of that you can attach a lot more data to each node or each server in a distributed cluster. And when you kind of scale that across basically a distributed cluster you can do with Aerospike the same things at 60 to 80% lower server count. And as a result 60 to 80% lower TCO compared to some of the other options that are available in the market. Then basically, as I said that's the key kind of starting point to the innovation. We lay around capabilities like, you know replication, change data notification, you know synchronous and asynchronous replication. The ability to actually stretch a single cluster across multiple regions. So for example, if you're operating a global service you can have a single Aerospike cluster with one node in San Francisco one node in New York, another one in London and this would be basically seamlessly operating. So that, you know, this is strongly consistent, very few no SQL data platforms are strongly consistent or if they are strongly consistent they will actually suffer performance degradation. And what strongly consistent means is, you know all your data is always available it's guaranteed to be available there is no data lost any time. So in this configuration that I talked about if the node in London goes down your application still continues to operate, right? Your users see no kind of downtime and you know, when London comes up it rejoins the cluster and everything is back to kind of the way it was before, you know London left the cluster so to speak. So the ability to do this globally resilient highly available kind of model is really, really powerful. A lot of our customers actually use that kind of a scenario and we offer other deployment scenarios from a higher availability perspective. So everything starts with HMA or Hybrid Memory Architecture and then we start building a lot of these other capabilities around the platform. And then over the years what our customers have guided us to do is as they're putting together a modern kind of data infrastructure, we don't live in the silo. So Aerospike gets deployed with other technologies like streaming technologies or analytics technologies. So we built connectors into Kafka, Pulsar, so that as you're ingesting data from a variety of data sources you can ingest them at very high ingest speeds and store them persistently into Aerospike. Once the data is in Aerospike you can actually run Spark jobs across that data in a multi-threaded parallel fashion to get really insight from that data at really high throughput and high speed. >> High throughput, high speed, incredibly important especially as today's landscape is increasingly distributed. Data centers, multiple public clouds, Edge, IoT devices, the workforce embracing more and more hybrid these days. How are you helping customers to extract more value from data while also lowering costs? Go into some customer examples 'cause I know you have some great ones. >> Yeah, you know, I think, we have built an amazing set of customers and customers actually use us for some really mission critical applications. So, you know, before I get into specific customer examples let me talk to you about some of kind of the use cases which we see out there. We see a lot of Aerospike being used in fraud detection. We see us being used in recommendations engines we get used in customer data profiles, or customer profiles, Customer 360 stores, you know multiplayer gaming and entertainment. These are kind of the repeated use case, digital payments. We power most of the digital payment systems across the globe. Specific example from a specific example perspective the first one I would love to talk about is PayPal. So if you use PayPal today, then you know when you're actually paying somebody your transaction is, you know being sent through Aerospike to really decide whether this is a fraudulent transaction or not. And when you do that, you know, you and I as a customer are not going to wait around for 10 seconds for PayPal to say yay or nay. We expect, you know, the decision to be made in an instant. So we are powering that fraud detection engine at PayPal. For every transaction that goes through PayPal. Before us, you know, PayPal was missing out on about 2% of their SLAs which was essentially millions of dollars which they were losing because, you know, they were letting transactions go through and taking the risk that it's not a fraudulent transaction. With Aerospike they can now actually get a much better SLA and the data set on which they compute the fraud score has gone up by you know, several factors. So by 30X if you will. So not only has the data size that is powering the fraud engine actually gone up 30X with Aerospike but they're actually making decisions in an instant for, you know, 99.95% of their transactions. So that's- >> And that's what we expect as consumers, right? We want to know that there's fraud detection on the swipe regardless of who we're interacting with. >> Yes, and so that's a really powerful use case and you know, it's a great customer success story. The other one I would talk about is really Wayfair, right, from retail and you know from e-commerce. So everybody knows Wayfair global leader in really in online home furnishings and they use us to power their recommendations engine. And you know it's basically if you're purchasing this, people who bought this also bought these five other things, so on and so forth. They have actually seen their cart size at checkout go up by up to 30%, as a result of actually powering their recommendations engine through Aerospike. And they were able to do this by reducing the server count by 9X. So on one ninth of the servers that were there before Aerospike, they're now powering their recommendations engine and seeing cart size checkout go up by 30%. Really, really powerful in terms of the business outcome and what we are able to, you know, drive at Wayfair. >> Hugely powerful as a business outcome. And that's also what the consumer wants. The consumer is expecting these days to have a very personalized relevant experience that's going to show me if I bought this show me something else that's related to that. We have this expectation that needs to be really fueled by technology. >> Exactly, and you know, another great example you asked about you know, customer stories, Adobe. Who doesn't know Adobe, you know. They're on a mission to deliver the best customer experience that they can. And they're talking about, you know great Customer 360 experience at scale and they're modernizing their entire edge compute infrastructure to support this with Aerospike. Going to Aerospike basically what they have seen is their throughput go up by 70%, their cost has been reduced by 3X. So essentially doing it at one third of the cost while their annual data growth continues at, you know about north of 30%. So not only is their data growing they're able to actually reduce their cost to actually deliver this great customer experience by one third to one third and continue to deliver great Customer 360 experience at scale. Really, really powerful example of how you deliver Customer 360 in a world which is dynamic and you know on a data set which is constantly growing at north of 30% in this case. >> Those are three great examples, PayPal, Wayfair, Adobe, talking about, especially with Wayfair when you talk about increasing their cart checkout sizes but also with Adobe increasing throughput by over 70%. I'm looking at my notes here. While data is growing at 32%, that's something that every organization has to contend with data growth is continuing to scale and scale and scale. >> Yap, I'll give you a fun one here. So, you know, you may not have heard about this company it's called Dream11 and it's a company based out of India but it's a very, you know, it's a fun story because it's the world's largest fantasy sports platform. And you know, India is a nation which is cricket crazy. So you know, when they have their premier league going on and there's millions of users logged onto the Dream11 platform building their fantasy league teams and you know, playing on that particular platform, it has a hundred million users a hundred million plus users on the platform, 5.5 million concurrent users and they have been growing at 30%. So they are considered an amazing success story in terms of what they have accomplished and the way they have architected their platform to operate at scale. And all of that is really powered by Aerospike. Think about that they're able to deliver all of this and support a hundred million users 5.5 million concurrent users all with, you know 99 plus percent of their transactions completing in less than one millisecond. Just incredible success story. Not a brand that is, you know, world renowned but at least you know from what we see out there it's an amazing success story of operating at scale. >> Amazing success story, huge business outcomes. Last question for you as we're almost out of time is talk a little bit about Aerospike AWS the partnership Graviton2 better together. What are you guys doing together there? >> Great partnership. AWS has multiple layers in terms of partnerships. So, you know, we engage with AWS at the executive level. They plan out, really roll out of new instances in partnership with us, making sure that, you know those instance types work well for us. And then we just released support for Aerospike on the Graviton platform and we just announced a benchmark of Aerospike running on Graviton on AWS. And what we see out there is with the benchmark a 1.6X improvement in price performance. And you know about 18% increase in throughput while maintaining a 27% reduction in cost, you know, on Graviton. So this is an amazing story from a price performance perspective, performance per watt for greater energy efficiencies, which basically a lot of our customers are starting to kind of talk to us about leveraging this to further meet their sustainability target. So great story from Aerospike and AWS not just from a partnership perspective on a technology and an executive level, but also in terms of what joint outcomes we are able to deliver for our customers. >> And it sounds like a great sustainability story. I wish we had more time so we would talk about this but thank you so much for talking about the main capabilities of a modern data platform, what's needed, why, and how you guys are delivering that. We appreciate your insights and appreciate your time. >> Thank you very much. I mean, if folks are at re:Invent next week or this week come on and see us at our booth and we are in the data analytics pavilion and you can find us pretty easily. Would love to talk to you. >> Perfect, we'll send them there. Subbu Iyer, thank you so much for joining me on the program today. We appreciate your insights. >> Thank you Lisa. >> I'm Lisa Martin, you're watching theCUBE's coverage of AWS re:Invent 2022. Thanks for watching. >> Clear- >> Clear cutting. >> Nice job, very nice job.