 Hey everyone, welcome to theCUBE's coverage of AWS re-invent 2022. Lisa Martin here with you with Subhu Ayer, one of our alumni, who's now the CEO of Aerospy. Subhu, 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, grosser, an 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's 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 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? 100 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 the data is actually growing at, you know, 35% kager. 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, if you will. 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, you know, 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 side 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 there's compelling pressures. One, the customer experience is paramount and we as customers expect answers in an instant in real time. And on the other hand, the way they make decisions is based on a large dataset because larger datasets actually propel better decisions. So there's competing pressures here which essentially drive the need, one from a business perspective to 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 as patients, we do expect as consumers, whether we're in our business lives or personal lives that we're going to 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 underleveraged 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 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 the concurrent user on that application or 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 because 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 that 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 AirRespect 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 operation. So we have customers 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 globally resilient services. The key to our innovation here is what we call the hybrid memory architecture. So 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 AirRespike 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 layer on capabilities like replication, change data notification, synchronous, anti-synchronous replication, the ability to actually stretch a single cluster across multiple regions. So for example, if you're offering a global service, you can have a single AirRespike 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 there's a strongly consistent, very few NoSQL data platforms are strongly consistent. Or if they are strongly consistent, they will actually suffer performance degradation. And what strongly consistent means is, all your data is always available. It's guaranteed to be available. There is no data loss anytime. 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 when London comes up, it rejoins the cluster and everything is back to kind of the way it was before, 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 out 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 modern kind of data infrastructure, we don't live in the silo. So Aerospy gets deployed with other technologies like streaming technologies or analytics technology. So we built connectors into Kafka, Pulsar, so that as you ingesting data from a variety of data sources, you can ingest them at very high ingest speeds and store them persistently into Aerospy. Once the data is in Aerospy, 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 because I know you have some great ones. Yeah, I think we have built an amazing set of customers and the customers actually use this for some really mission critical applications. So before I get into specific customer examples, let me talk to you about some of the use cases which we see out there. We see a lot of Aerospy 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, multiplayer gaming and entertainment. These are kind of the repeated use case of digital payments. We power most of the digital payment systems across the globe. Specific example, from a specific example perspective, the first one I'd love to talk about is PayPal. So if you use PayPal today, then when you actually paying somebody, your transaction is being sent through Aerospy to really decide whether this is a fraudulent transaction or not. And when you do that, you and I as a customer not going to wait around for 10 seconds for PayPal to say yay or nay. We expect 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, PayPal was missing out on about 2% of their SLAs, which was essentially millions of dollars, which they were losing because they were letting transactions go through and taking the risk that it's not a fraudulent transaction. With Aerospy, they can now actually get a much better SLA. And the data set on which they compute the fraud score has gone up by several factors. So by 30X, if you will. So not only has the data size that is powering the fraud engine actually grown up 30X with Aerospy, but they're actually making decisions in an instant for 99.95% of their transactions. So 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 it's a great customer, great customer success story. The other one I would talk about is really Wayfair, from retail and from e-commerce. So everybody knows Wayfair global leader in really online home furnishings. And they use us to power their recommendations engine. And it's basically, if you're purchasing this people who bought this, but also bought these five other things, so on and so forth, they have actually seen their card size at checkout go up by up to 30% as a result of actually powering their recommendations engine through Aerospy. And they were able to do this by reducing their server account by 9x. So on 1 9th of the servers that were there before Aerospy, they're now powering their recommendation engine and seeing card size checkout go up by 30%. Really, really powerful in terms of the business outcome and what we are able to 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 another great example, you asked about customer stories, Adobe, who doesn't know Adobe? They are on a mission to deliver the best customer experience that they can. And they're talking about great customer 360 experience at scale, and they're modernizing their entire edge compute infrastructure to support this with Aerospy. Going to Aerospy, basically what they have seen is their throughput go up by 70%. Their cost has been reduced by 3x. So essentially doing it at 1 3rd of the cost. While their annual data growth continues at 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 1 3rd to 1 3rd 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 on a data set which is constantly growing at north of 30% in this case. These 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. It's continuing to scale and scale and scale. Yep, I'll give you a fun one here. So you may not have heard about this company, it's called Dream 11. And it's a company based out of India, but it's a very, it's a fun story because it's the world's largest fantasy sports platform. And India is a nation which is cricket crazy. So when they have their premier league going on, there's millions of users logged on to the Dream 11 platform building the fantasy league teams and playing on that particular platform. It has 100 million users, 100 million plus users on the platform, 5.5 million concurrent users, and they've been growing at 30%. So they are considered an amazing success story in terms of what they have accomplished and the way they've architected their platform to operate at scale. And all of that is really powered by Aerospy. Where think about that they are able to deliver all of this and support 100 million users, 5.5 million concurrent users, all with 99 plus percent of their transactions completing in less than one millisecond. Just incredible success story, not a brand that is world renowned, but at least 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 Aerospy, AWS, the partnership, Graviton 2, Better Together. What are you guys doing together there? Great partnership, AWS has multiple layers in terms of partnerships. So, we engage with AWS at the executive level, they plan out really roll out of new instances in partnership with us, making sure that those instance types work well for us. And then we just released support for Aerospy on the Graviton platform. And we just announced a benchmark of Aerospy running on Graviton on AWS. And what we see out there is with the benchmark, a 1.6X improvement in price performance. And about 18% increase in throughput while maintaining a 27% reduction in cost 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 targets. So great story from Aerospy 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 to 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 green wind next week or this week, come on and see us at our booth. We are in the data and analytics pavilion. You can find us pretty easily. We'd love to talk to you. Perfect, we'll send them there. Subbu Ayer, 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.