>>Good evening everyone and welcome back to Sparkly Sin City, Las Vegas, Nevada, where we are here with the cube covering AWS Reinvent for the 10th year in a row. John Furrier has been here for all 10. John, we are in our last session of day one. How does it compare? >>I just graduated high school 10 years ago. It's exciting to be, here's been a long time. We've gotten a lot older. My >>Got your brain is complex. You've been a lot in there. So fast. >>Graduated eight in high school. You know how it's No. All good. This is what's going on. This next segment, wrapping up day one, which is like the the kickoff. The Mondays great year. I mean Tuesdays coming tomorrow big days. The announcements are all around the kind of next gen and you're starting to see partnering and integration is a huge part of this next wave cuz API's at the cloud, next gen cloud's gonna be deep engineering integration and you're gonna start to see business relationships and business transformation scale a horizontally, not only across applications but companies. This has been going on for a while, covering it. This next segment is gonna be one of those things that we're gonna look at as something that's gonna happen more and more on >>Yeah, I think so. It's what we've been talking about all day. Without further ado, I would like to welcome our very exciting guest for this final segment, trust from single store. Thank you for being here. And we also have him on from IBM Data and ai. Y'all are partners. Been partners for about a year. I'm gonna go out on a limb only because their legacy and suspect that a few people, a few more people might know what IBM does versus what a single store does. So why don't you just give us a little bit of background so everybody knows what's going on. >>Yeah, so single store is a relational database. It's a foundational relational systems, but the thing that we do the best is what we call us realtime analytics. So we have these systems that are legacy, which which do operations or analytics. And if you wanted to bring them together, like most of the applications want to, it's really a big hassle. You have to build an ETL pipeline, you'd have to duplicate the data. It's really faulty systems all over the place and you won't get the insights really quickly. Single store is trying to solve that problem elegantly by having an architecture that brings both operational and analytics in one place. >>Brilliant. >>You guys had a big funding now expanding men. Sequel, single store databases, 46 billion again, databases. We've been saying this in the queue for 12 years have been great and recently not one database will rule the world. We know that. That's, everyone knows that databases, data code, cloud scale, this is the convergence now of all that coming together where data, this reinvent is the theme. Everyone will be talking about end to end data, new kinds of specialized services, faster performance, new kinds of application development. This is the big part of why you guys are working together. Explain the relationship, how you guys are partnering and engineering together. >>Yeah, absolutely. I think so ibm, right? I think we are mainly into hybrid cloud and ai and one of the things we are looking at is expanding our ecosystem, right? Because we have gaps and as opposed to building everything organically, we want to partner with the likes of single store, which have unique capabilities that complement what we have. Because at the end of the day, customers are looking for an end to end solution that's also business problems. And they are very good at real time data analytics and hit staff, right? Because we have transactional databases, analytical databases, data lakes, but head staff is a gap that we currently have. And by partnering with them we can essentially address the needs of our customers and also what we plan to do is try to integrate our products and solutions with that so that when we can deliver a solution to our customers, >>This is why I was saying earlier, I think this is a a tell sign of what's coming from a lot of use cases where people are partnering right now you got the clouds, a bunch of building blocks. If you put it together yourself, you can build a durable system, very stable if you want out of the box solution, you can get that pre-built, but you really can't optimize. It breaks, you gotta replace it. High level engineering systems together is a little bit different, not just buying something out of the box. You guys are working together. This is kind of an end to end dynamic that we're gonna hear a lot more about at reinvent from the CEO ofs. But you guys are doing it across companies, not just with aws. Can you guys share this new engineering business model use case? Do you agree with what I'm saying? Do you think that's No, exactly. Do you think John's crazy, crazy? I mean I all discourse, you got out of the box, engineer it yourself, but then now you're, when people do joint engineering project, right? They're different. Yeah, >>Yeah. No, I mean, you know, I think our partnership is a, is a testament to what you just said, right? When you think about how to achieve realtime insights, the data comes into the system and, and the customers and new applications want insights as soon as the data comes into the system. So what we have done is basically build an architecture that enables that we have our own storage and query engine indexing, et cetera. And so we've innovated in our indexing in our database engine, but we wanna go further than that. We wanna be able to exploit the innovation that's happening at ibm. A very good example is, for instance, we have a native connector with Cognos, their BI dashboards right? To reason data very natively. So we build a hyper efficient system that moves the data very efficiently. A very other good example is embedded ai. >>So IBM of course has built AI chip and they have basically advanced quite a bit into the embedded ai, custom ai. So what we have done is, is as a true marriage between the engineering teams here, we make sure that the data in single store can natively exploit that kind of goodness. So we have taken their libraries. So if you have have data in single store, like let's imagine if you have Twitter data, if you wanna do sentiment analysis, you don't have to move the data out model, drain the model outside, et cetera. We just have the pre-built embedded AI libraries already. So it's a, it's a pure engineering manage there that kind of opens up a lot more insights than just simple analytics and >>Cost by the way too. Moving data around >>Another big theme. Yeah. >>And latency and speed is everything about single store and you know, it couldn't have happened without this kind of a partnership. >>So you've been at IBM for almost two decades, don't look it, but at nearly 17 years in how has, and maybe it hasn't, so feel free to educate us. How has, how has IBM's approach to AI and ML evolved as well as looking to involve partnerships in the ecosystem as a, as a collaborative raise the water level together force? >>Yeah, absolutely. So I think when we initially started ai, right? I think we are, if you recollect Watson was the forefront of ai. We started the whole journey. I think our focus was more on end solutions, both horizontal and vertical. Watson Health, which is more vertically focused. We were also looking at Watson Assistant and Watson Discovery, which were more horizontally focused. I think it it, that whole strategy of the world period of time. Now we are trying to be more open. For example, this whole embedable AI that CICE was talking about. Yeah, it's essentially making the guts of our AI libraries, making them available for partners and ISVs to build their own applications and solutions. We've been using it historically within our own products the past few years, but now we are making it available. So that, how >>Big of a shift is that? Do, do you think we're seeing a more open and collaborative ecosystem in the space in general? >>Absolutely. Because I mean if you think about it, in my opinion, everybody is moving towards AI and that's the future. And you have two option. Either you build it on your own, which is gonna require significant amount of time, effort, investment, research, or you partner with the likes of ibm, which has been doing it for a while, right? And it has the ability to scale to the requirements of all the enterprises and partners. So you have that option and some companies are picking to do it on their own, but I believe that there's a huge amount of opportunity where people are looking to partner and source what's already available as opposed to investing from the scratch >>Classic buy versus build analysis for them to figure out, yeah, to get into the game >>And, and, and why reinvent the wheel when we're all trying to do things at, at not just scale but orders of magnitude faster and and more efficiently than we were before. It, it makes sense to share, but it's, it is, it does feel like a bit of a shift almost paradigm shift in, in the culture of competition versus how we're gonna creatively solve these problems. There's room for a lot of players here, I think. And yeah, it's, I don't >>Know, it's really, I wanted to ask if you don't mind me jumping in on that. So, okay, I get that people buy a bill I'm gonna use existing or build my own. The decision point on that is, to your point about the path of getting the path of AI is do I have the core competency skills, gap's a big issue. So, okay, the cube, if you had ai, we'd take it cuz we don't have any AI engineers around yet to build out on all the linguistic data we have. So we might use your ai but I might say this to then and we want to have a core competency. How do companies get that core competency going while using and partnering with, with ai? What you guys, what do you guys see as a way for them to get going? Because I think some people probably want to have core competency of >>Ai. Yeah, so I think, again, I think I, I wanna distinguish between a solution which requires core competency. You need expertise on the use case and you need expertise on your industry vertical and your customers versus the foundational components of ai, which are like, which are agnostic to the core competency, right? Because you take the foundational piece and then you further train it and define it for your specific use case. So we are not saying that we are experts in all the industry verticals. What we are good at is like foundational components, which is what we wanna provide. Got it. >>Yeah, that's the hard deep yes. Heavy lift. >>Yeah. And I can, I can give a color to that question from our perspective, right? When we think about what is our core competency, it's about databases, right? But there's a, some biotic relationship between data and ai, you know, they sort of like really move each other, right? You >>Need, they kind of can't have one without the other. You can, >>Right? And so the, the question is how do we make sure that we expand that, that that relationship where our customers can operationalize their AI applications closer to the data, not move the data somewhere else and do the modeling and then training somewhere else and dealing with multiple systems, et cetera. And this is where this kind of a cross engineering relationship helps. >>Awesome. Awesome. Great. And then I think companies are gonna want to have that baseline foundation and then start hiring in learning. It's like driving the car. You get the keys when you're ready to go. >>Yeah, >>Yeah. Think I'll give you a simple example, right? >>I want that turnkey lifestyle. We all do. Yeah, >>Yeah. Let me, let me just give you a quick analogy, right? For example, you can, you can basically make the engines and the car on your own or you can source the engine and you can make the car. So it's, it's basically an option that you can decide. The same thing with airplanes as well, right? Whether you wanna make the whole thing or whether you wanna source from someone who is already good at doing that piece, right? So that's, >>Or even create a new alloy for that matter. I mean you can take it all the way down in that analogy, >>Right? Is there a structural change and how companies are laying out their architecture in this modern era as we start to see this next let gen cloud emerge, teams, security teams becoming much more focused data teams. Its building into the DevOps into the developer pipeline, seeing that trend. What do you guys see in the modern data stack kind of evolution? Is there a data solutions architect coming? Do they exist yet? Is that what we're gonna see? Is it data as code automation? How do you guys see this landscape of the evolving persona? >>I mean if you look at the modern data stack as it is defined today, it is too detailed, it's too OSes and there are way too many layers, right? There are at least five different layers. You gotta have like a storage you replicate to do real time insights and then there's a query layer, visualization and then ai, right? So you have too many ETL pipelines in between, too many services, too many choke points, too many failures, >>Right? Etl, that's the dirty three letter word. >>Say no to ETL >>Adam Celeste, that's his quote, not mine. We hear that. >>Yeah. I mean there are different names to it. They don't call it etl, we call it replication, whatnot. But the point is hassle >>Data is getting more hassle. More >>Hassle. Yeah. The data is ultimately getting replicated in the modern data stack, right? And that's kind of one of our thesis at single store, which is that you'd have to converge not hyper specialize and conversation and convergence is possible in certain areas, right? When you think about operational analytics as two different aspects of the data pipeline, it is possible to bring them together. And we have done it, we have a lot of proof points to it, our customer stories speak to it and that is one area of convergence. We need to see more of it. The relationship with IBM is sort of another step of convergence wherein the, the final phases, the operation analytics is coming together and can we take analytics visualization with reports and dashboards and AI together. This is where Cognos and embedded AI comes into together, right? So we believe in single store, which is really conversions >>One single path. >>A shocking, a shocking tie >>Back there. So, so obviously, you know one of the things we love to joke about in the cube cuz we like to goof on the old enterprise is they solve complexity by adding more complexity. That's old. Old thinking. The new thinking is put it under the covers, abstract the way the complexities and make it easier. That's right. So how do you guys see that? Because this end to end story is not getting less complicated. It's actually, I believe increasing and complication complexity. However there's opportunities doing >>It >>More faster to put it under the covers or put it under the hood. What do you guys think about the how, how this new complexity gets managed or in this new data world we're gonna be coming in? >>Yeah, so I think you're absolutely right. It's the world is becoming more complex, technology is becoming more complex and I think there is a real need and it's not just from coming from us, it's also coming from the customers to simplify things. So our approach around AI is exactly that because we are essentially providing libraries, just like you have Python libraries, there are libraries now you have AI libraries that you can go infuse and embed deeply within applications and solutions. So it becomes integrated and simplistic for the customer point of view. From a user point of view, it's, it's very simple to consume, right? So that's what we are doing and I think single store is doing that with data, simplifying data and we are trying to do that with the rest of the portfolio, specifically ai. >>It's no wonder there's a lot of synergy between the two companies. John, do you think they're ready for the Instagram >>Challenge? Yes, they're ready. Uhoh >>Think they're ready. So we're doing a bit of a challenge. A little 32nd off the cuff. What's the most important takeaway? This could be your, think of it as your thought leadership sound bite from AWS >>2023 on Instagram reel. I'm scrolling. That's the Instagram, it's >>Your moment to stand out. Yeah, exactly. Stress. You look like you're ready to rock. Let's go for it. You've got that smile, I'm gonna let you go. Oh >>Goodness. You know, there is, there's this quote from astrophysics, space moves matter, a matter tells space how to curve. They have that kind of a relationship. I see the same between AI and data, right? They need to move together. And so AI is possible only with right data and, and data is meaningless without good insights through ai. They really have that kind of relationship and you would see a lot more of that happening in the future. The future of data and AI are combined and that's gonna happen. Accelerate a lot faster. >>Sures, well done. Wow. Thank you. I am very impressed. It's tough hacks to follow. You ready for it though? Let's go. Absolutely. >>Yeah. So just, just to add what is said, right, I think there's a quote from Rob Thomas, one of our leaders at ibm. There's no AI without ia. Essentially there's no AI without information architecture, which essentially data. But I wanna add one more thing. There's a lot of buzz around ai. I mean we are talking about simplicity here. AI in my opinion is three things and three things only. Either you use AI to predict future for forecasting, use AI to automate things. It could be simple, mundane task, it would be complex tasks depending on how exactly you want to use it. And third is to optimize. So predict, automate, optimize. Anything else is buzz. >>Okay. >>Brilliantly said. Honestly, I think you both probably hit the 32nd time mark that we gave you there. And the enthusiasm loved your hunger on that. You were born ready for that kind of pitch. I think they both nailed it for the, >>They nailed it. Nailed it. Well done. >>I I think that about sums it up for us. One last closing note and opportunity for you. You have a V 8.0 product coming out soon, December 13th if I'm not mistaken. You wanna give us a quick 15 second preview of that? >>Super excited about this. This is one of the, one of our major releases. So we are evolving the system on multiple dimensions on enterprise and governance and programmability. So there are certain features that some of our customers are aware of. We have made huge performance gains in our JSON access. We made it easy for people to consume, blossom on OnPrem and hybrid architectures. There are multiple other things that we're gonna put out on, on our site. So it's coming out on December 13th. It's, it's a major next phase of our >>System. And real quick, wasm is the web assembly moment. Correct. And the new >>About, we have pioneers in that we, we be wasm inside the engine. So you could run complex modules that are written in, could be C, could be rushed, could be Python. Instead of writing the the sequel and SQL as a store procedure, you could now run those modules inside. I >>Wanted to get that out there because at coupon we covered that >>Savannah Bay hot topic. Like, >>Like a blanket. We covered it like a blanket. >>Wow. >>On that glowing note, Dre, thank you so much for being here with us on the show. We hope to have both single store and IBM back on plenty more times in the future. Thank all of you for tuning in to our coverage here from Las Vegas in Nevada at AWS Reinvent 2022 with John Furrier. My name is Savannah Peterson. You're watching the Cube, the leader in high tech coverage. We'll see you tomorrow.