>>Modernizing applications can be a complicated situation. For many folks, it's useful to have some best practices and tangible steps that can remove friction and yield some quick wins. We're now joined by couch based CTO, Ravi meam, who will cover how organizations can approach application modernization, what role the cloud plays and what you need to know about building a business case. Ravi, welcome back to the cube. Good to see you again. >>Very good to see you. Thanks for having me, Dave. >>Yes, our pleasure. Uh, according to a recent couch based digital transformation survey that you guys ran, it was about a 650 respondents, CIOs, CTOs, et cetera. The inertia of legacy technology held back according to the respondents, 82% of enterprises from modernizing their portfolios in 2021. So I wanna talk about the what and the why of modernization. Robbie, what does application modernization mean to you and why is it top of mind for organizations? >>Yeah, I think there have been multiple forces at work here for a while and they have all come to a tipping point with, uh, the pandemic and, uh, uh, it's a combination of factors and, uh, the legacy technologies were built for a different generation of applications. So it's a generational shift that we are undergoing. Uh, part of it is the, the consumption model, which is all cloud based and pay as you go kinda stuff. The other is edge is in the middle of a lot of these conversations together with, uh, the velocity variety, um, of data that you have to actually sort of consume and results that you need to produce. These were all not what the, sort of the, the infrastructure of hold on, which the applications were built on, uh, uh, stand for. So the infrastructure, the substrate requires modernization, uh, in order for the businesses to transform themselves, that's, what's going on. >>We call it digital transformation from a technology perspective, but it's businesses that are transforming, uh, the business models, uh, in front of our eyes. Uh, you know, we have seen the media go from, uh, set up boxes to streaming everywhere, um, like that every business eCommerce has changed, uh, the way we sort of, uh, do any business gaming has changed, uh, the, the banking industry, the healthcare, everything is changing, uh, in terms of the fundamental movement, if you, if you could, uh, sort of say that is to reach the consumer directly and sort of dis intermediate intermediaries. And in that process, the technologies that we had used to build the, the, you know, last previous generation of applications, no longer scale, no longer a nimble enough, uh, no longer cater to the modern, uh, the needs of the modern data and the infrastructure on which, uh, we are standing of these applications. So that's, what's driving the modernization effort. And, uh, in, in that, uh, you know, we have always started say that few years ago, that data is the new oil. Um, so that plays a very critical role in how the data silos and infrastructure that enterprises have is what's holding them back. And, uh, this whole effort is, uh, in, in, in terms of modernizing that infrastructure, uh, through the modern means of, uh, uh, the cloud computing, uh, the modern serverless architectures and microservices, and, uh, the edge and AI play play an important role in this. >>So we're gonna hear later from Amdocs, uh, about their modernization and where couch base helps and fits, but I'd love to hear your perspective as to how couch base helps organizations modernize. >>Right. I think one of the, uh, uh, fundamental things that has happened is that in the last 30, 40 odd years, the data infrastructure has sort of become, uh, a sprawl. Uh, we had built multiple systems, uh, uh, relational databases, cash is, uh, search systems, analytical systems, uh, all, uh, requiring for us to move the data, uh, from one system to the other, in order for you to get the value from those. And this is basically what we call as a data sprawl or database sprawl. And this leads to so many sort of, uh, downstream effects all the way from, uh, data not being available, uh, at the time when the engagement, uh, when the customer is engaged to data governance, security and all those issues, because the threat surface area is wide. And now you're putting all this infrastructure on the modern sort of cloud computing paradigm and, and the costs are sort of ballooning. >>And, uh, because those older infrastructures that were built, uh, when you deploy them on the cloud, uh, it, it creates its ads to the, uh, the complexity of this brawl and on top of the, the cost of this. So, uh, a system like couch base is what, um, uh, simplifies this brawl for, uh, our customers. And it is built for the modern, uh, sort of requirements of scale and performance, low latency, and the flexibility, uh, of being able to sort of not have to go through this whole sort of cycle of whenever you have to have a, a change in your application that touches your data, uh, that it, it actually creates a huge tool in those upgrades and all those life cycle having to CA carry pagers. Uh, I mean, that doesn't work anymore in these days of, I know, five, nine up times and, uh, 24 7, 365 availability of, uh, your services, uh, is so in that area is where couch base sort of helps, uh, our customers to modernize, uh, their sort of data infrastructure. >>It, uh, fuses, um, the multiple technologies that were spread across, uh, into one platform. So it gives a, a simpler programming paradigm, uh, that is one way to scale manage, administer, uh, patch, upgrade. All that mechanism is sort of not just thought through and automated, but it also sort of centralized this, uh, whole thing simplifies at the end of the day, uh, that total task of managing, uh, because that the volume of data that you have to manage now is, you know, orders of magnitude three to four orders of magnitude more than, uh, what it was just a few years ago. And, uh, so in that, uh, containing the sprawl, uh, agility of development, uh, are, are sort of, and the simplicity of deployment and management are some of the key capabilities that, uh, enterprises look to us to solve. And in that, bringing in all the way from cloud to multi-cloud to edge, uh, is how this sort of strategy evolves for enterprises. >>So square this circle for me, cuz in the panel we just had, there's a lot of agreement with what you just said, lift and shift of legacy platforms, doesn't work. Uh, it might work for the cloud vendor to get the data in the cloud, but it generally doesn't work for the customer. And you mentioned sprawl, we talked about this in the panel about, you know, data by its very nature is distributed. We talked about data mesh. There's a lot of skepticism around data mesh, but that that's cool. And you mentioned edge, so yes, I'm interested in the cloud's role here is the idea that you're actually putting all this stuff in one place. How does that fit with the edge? Maybe you could help us understand you're thinking of that and where the cloud fits. >>Yes. Um, you know, it's about, uh, centralizing a data up to a point and decentralizing it's in the magic of how you actually enable that. Um, uh, for example, just your traffic signal, your car, uh, or if you're on a cruise ship, each one is an edge, they all generate petabytes of data. And then you basically, uh, you can consume that, but if you're gonna stream all this data to a centralized place like a cloud that's, uh, you know, most of the data actually is not something that you're gonna store forever. Those are, you know, topical and that information is required at the edge. You should synthesize that information and take the noise from it and discard the signal. So that's where the edge, uh, typically the edge is not some, you know, personal device alone or uh, uh, or a IOT sensor sending data that is also, uh, sort of, uh, one, one element of the edge, but the edge is about decentralizing the cloud. >>So to say, so you can have mul your topologies of not having all your data sit in the cloud centralize someplace behind five firewalls. So when your application tries to reach that all the latency comes into place. So that's what you want to, uh, decentralize and have the data available as close to the engagement of the data with the consumer of it. So in that is the decentralization strategy where you can have multiple techologies, a three, a mesh, uh, however you choose to so that you get to get the data closest. Um, it could be a mobile device. Uh, it could be a, a smaller deployment of a server. It could be, uh, uh, a personal electronic device like watch, or it could be all the way in the IOT gateway. These are the various sort of decentralization of the data that has to happen. >>So it's about moving the data fastest. It's almost like CDN of the data is what, uh, sorry. Uh, for those it's, um, content delivery network is what CDN stands for, where we used to actually move static content in the good old days. That's what made, made our webpages faster. Now we can actually move live data that much faster by using replication technology. So when you move the data towards, towards the edge, what you're trying to do is bring that data closer, uh, to the compute where it's actually happening, as opposed to keeping the data centralized someplace back in the cloud and server and all your application logic is actually sitting on the device or on the edge. So you're constantly, uh, shoveling the data from the cloud to the edge, from edge to the cloud at the time of compute, as opposed to having it available at the time of, uh, um, the consumption of the data. >>That's where the paradigm, uh, shift is actually happening. And, uh, this basically is not about better user experience. It's also about backend networking, other costs that you can actually, uh, gain from, by not having to sort of repeatedly sort of shovel data back and forth. So that's stage strategy that, uh, enterprises are adopting. Now, this is become so to say core part of the architecture of modernization, uh, uh, in terms of where everybody can see this has to move to and, uh, our edge and mobile product, um, also plays a role in, uh, that's one of the other elements aspects of it that customers to look us, uh, look to us >>For. So it's a balance and couch base can play in both places. A lot of the data, if I heard you correctly at the edge is ephemeral, but if I want to do, you know, AI inferencing in real time, I gotta do it at the edge. I can't send it back to the cloud and, and, and do the modeling, you know, post-proces, that's not gonna work. All right, let's talk about the business case, you know, we've, we we've hit on the what and the why, but, you know, how does it get paid for companies sometimes struggle to plan for and budget appropriately for their outcomes? Yes. What do customers need to know about how do they get this past the CFO's office for, in the other business decision makers? >>I think there is an opportunity cost, uh, with the sort of lack of modernization, uh, if, uh, people are doing their classic sort of, so to say it style budgeting, uh, then it will just look like we have to modernize, uh, you know, some older infrastructure. It's not about that. It's about modernizing or making your business relevant, uh, to, uh, to the consumers, because the way consumers, uh, go about consuming your services now is very different from the way you had originally imagined and built for. And in that lies the, the, the transformation, uh, not to see this as a, it, uh, just as an it infrastructure modernization, but more from the standpoint of business transformation and, uh, the tooling that is required for this business transformation to be successful. So it requires the involvement of, um, not leaving it to just, you know, uh, uh, it oriented sort of, uh, uh, thinking of modernizing, but from the standpoint of looking at the, the, the business and what are the transformations that they need to, if they don't keep up with the Jones, they, in this digital divide, they may find themselves in the sort of either the wrong side or in the chasm. >>So I think that mindset, uh, that I was, uh, sort of in addition to sort of, uh, it pushing for this, uh, it's got to have a C-suite, uh, sponsorship understanding and, uh, sort of champion of this, then those initiatives will succeed because, uh, it's not just the technology transformation. It is accompanied by business and sort of, so to say cultural transformation inside the enterprise. >>Yeah. And it's interesting in the survey, it was very much it, you know, survey, I get that and, and the, it pros, the CIOs, et cetera, felt that, that, that, that the it organization was largely responsible for the digital strategy. And I think that was largely a function of, we just came out of the, the pandemic or Hopely coming out of the pandemic. And so they had all these tactical needs, but now you're saying step back, align with the business, make sure the C suite's involved, and that's gonna reduce the friction of, of getting this stuff paid for. >>Correct. And, you know, the, uh, this observation was also there. If you, I must have noticed that, you know, many, uh, of these sort of transf strategies, if you just leave it to like an it thing, they end up being reactive. Uh, but the proactive strategies are the one that actually, uh, succeed because they understand that this is a sort of enterprise transformation. It could be disruptive. Uh, it is what is required for the enterprise to get to the, uh, to the next level, uh, or to be, uh, in this, to be relevant in this sort of modern economy, if you would. So I think that is what, uh, what people are reacting to is the fact that this pandemic has pushed people to modernize quickly. And that may have happened as a reaction to the reality of the situation, but more and more, uh, uh, even among these strategies and more and more initiatives that people are taking, they may have sort of a longer term sort of thinking in this, uh, that requires the, uh, definitely without it's not gonna succeed and they're gonna be in the middle and they'll be, uh, in the forefront of many technology decisions that we have to make, but having a, a C-suite level sponsorship. >>In addition to that, with the impetus of what is the business transformation, this is actually going to achieve, um, those you will see will succeed a lot more because otherwise you, we see that, you know, good, good number of what 80% of these projects fail or, or, or they suffer delays or scale back or never get started, uh, because, you know, uh, the understanding of what is the business value of it is perhaps not, not clearly articulated instead, it just becomes a, a technology modernization conversation without that company benefit. >>Yeah. Got it. Okay. Uh, you guys recently announced some updates to your platform. Can you run us through the, the highlights, you know, what the customers get and, and how it relates to this conversation modernizing application strategies? >>Yes. So, uh, well, we will be, uh, releasing our couch base server 7.1. And, uh, that is what will be the sort of underneath platform for our, the couch base, uh, Capella, which is the, our DBA both, uh, have exciting innovations, um, that we would be putting out. Uh, let me just run through a few things, uh, on the, uh, uh, couch based server seven one, because there are some, uh, amazing, uh, capabilities we have introduced there. We are really excited about the opportunities. This brings couch based into play. Uh, first is we have a, uh, a brand new storage engine that we put in there, which, uh, significant significantly, uh, reduces the, uh, the cost of running couch base. Uh, with this capability, we can actually consume lot less memory and that's, that is like a 10 X improvement on this one. So from that standpoint, we are 10 X more efficient in terms of resource consumption, the expensive memory oriented resource consumption. >>This now allows couch based to sort of not just cater to those high performance, um, you know, hyperscale scenarios that we are known for, but also the more, the classic BIS oriented, uh, applications, which are not that performance sensitive, but they're more cost sensitive. So that's a huge, uh, step forward for couch base because there are a lot more, uh, opportunities where sort of, we become, uh, that much more, uh, cost efficient for enterprises to run. And this is something that, uh, many enterprises have asked for, and we know, uh, many more use cases where we would be more relevant with that innovation. And this has been a, a sort of a long journey building storage engines is, uh, you know, uh, is a very difficult Endover. And we took that on knowing that, uh, what we can achieve here would be a game changer, uh, for couch base. >>And in terms of how, uh, uh, the consolidation of multiple things that you can do in our platform just got this sort of boost of being able to do a lot more with lot less resources. In addition to that, we have done enhancements to our analytics service, uh, with, uh, the work that we have done there. Uh, it, it can sort of do a lot more, um, uh, availability, uh, of the, of, of the analytics service, uh, which, uh, will strengthens the analytics side of the product, which now allows you to run analysis O on J O uh, straight up without requiring the operational side of the, uh, the database. So you can just simply do, uh, straight off analytics stuff, because it, it, it can now, uh, give you the higher availability and disaster recovery that you would want if you're gonna depend on these, uh, systems with that, we are done over some, uh, real good work with Tableau integration, which makes it easy to visualize this, um, uh, uh, and, uh, one other important capability we introduce here is the, um, on, in the entire platform is what we call as user defined functions. >>This now allows us to write custom logic and Java script in the server couch based server. This is, this helps you write procedural logic in the middle of, uh, SQL queries, which is a humongous capability that, you know, and the classical systems process. Now, with that, we have closed the gap. If you know, how to program to sort of classical operational systems, pretty much, you have one to one equivalence of that, uh, in couch. So if you come from the good relational world, uh, it would be very easy breeze for you to understand how to program in this modern, no SQL systems, which both supports, um, uh, SQL as well as the classic asset transaction capabilities. And last, uh, we expanded the support two arm processors, and typically, uh, arm processes, at least save you quarter of, uh, your budget because of it being that much more, uh, uh, cost efficient in terms of, uh, its operational and power capabilities. >>So with that net net, uh, couch based server becomes a lot more, um, uh, cost efficient. And at the same time, it also in one, well becomes that database server, which can both handle your in memory, uh, capabilities that, that speed and hyperscale, as well as, uh, the classical use cases of being, uh, disk, uh, disoriented, uh, classical relational database use cases. Nice. So that, that, that rounds out our offering, it's been a long journey for us to get here from being the high performance, uh, low latency system to, uh, the classical database use case >>Assessment. Yeah. I mean, that's great. You got, you got memory optimization, you mentioned the, the, the, the arm base. Now you're on that curve, which is great software companies love when you get cheaper, faster hardware, uh, you making it easy to speak the language of, you know, traditional stuff. So that's awesome. Um, you and I, you mentioned, uh, Capella, you and I talked about, yes, at couch base connects Capella. You've been moving hard with your DBA strategy, how's it going? And then beyond these announcements, what's what should we look for from couch base? >>You know, uh, our fundamental, uh, mission is to make the developer experience, um, that much more easier, that much, uh, to move all the frictions that, that has existed for developers to adopt couch base. And, uh, the Capella strategy is to leverage the cloud. So you have number one, the ease of development, just bring your browsers, start to learn, develop even simple sample applications and deploy them from there. You can scale, and you can have production level deployments, that whole journey of a developer, along with the ability to sort of have your a, you know, metered billing and pay as you go, uh, uh, pricing, uh, so that it becomes easier for developers to sort of consume this and, uh, show the value of what they can build here. That is our, um, sort of journey of bringing it closer, uh, to our developers and make it simpler for them to sort of, uh, get started and build the, the mission critical applications that they have trusted to build on couch base, to become that much more simpler, faster, and easier for them. So that's the journey. So that's the kind of announcements you will see coming out in Capella. And for that this, this seven one server is, is the platform on which we, we are sort of adding those capabilities to make a Capella that much easier for developers to adopt >>Outstanding. You've been busy and it looks like you've got a lot of value. Yes. All right, we're gonna have to leave it there. Robbie, up next, we bring on the customer perspective with Amdocs. They've got a real world example of a modernization journey that they go through. They had to modernize legacy Oracle WebLogic infrastructure with a microservices architecture, and of course, couch base, keep it right there. You're watching the cube.

>>Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, or is modernized now. Yes, let's talk about that. And with me is Ravi, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >>Thank you so much. I'm so glad to be here with you. >>I asked you what the new requirements are around modern applications. I've seen some, you know, some of your comments, you gotta be flexible, distributed, multimodal, mobile edge. It, that those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >>Yeah, I think what has basically happened is that, uh, so far, uh, it's been a transition of sorts. And now we are come to a point where, uh, the tipping point and the tipping point has been, uh, uh, more because of COVID and there COVID has pushed us to a world where we are living, uh, in a sort of, uh, occasionally connected manner where our digital, uh, interactions, precede our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than, uh, in a digital manner, as opposed to sort of making a more specific human contact that has really been the, uh, sort of accelerant to this modernized. Now, as a team in this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. >>They're all sitting behind. Uh, they used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, uh, but they are all centralized still. Uh, but where our engagement happens with the data is, uh, at the edge, uh, at your point of convenience at your point of consumption, not where the data is actually sitting. So this has led to, uh, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? Uh, but it just basically comes down to the fact that the data needs to be where you are engaging with it. And that means if you are doing it on your mobile phone, or if you are sitting, uh, doing something in your body or traveling, or whether you are in a subway, whether you're in a plane or a ship, wherever the data needs to come to you, uh, and be available as opposed to every time you going to the data, which is centrally sitting in some place. >>And that is the fundamental shift in terms of how the modern architecture needs to think, uh, when they, when it comes to digital transformation and, uh, transitioning their old applications to, uh, the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Uh, otherwise people are basically waiting for that circle of death that we all know, uh, and blaming the networks and other pieces. The problem is actually, the data is not where you are engaging with. It has got to be fetched, you know, seven seas away. Um, and that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >>I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this, because date data by its very nature is distributed. It's always been distributed, but w w but distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, uh, of, uh, of a super rock solid database that can handle, you know, distributed data? Yes. >>So there are two issues that you're a little too over there with Forrest is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is, uh, like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data in one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, uh, when you have the data, you can first look at it to perform. >>Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five minutes. Again, this is a, there's a class of problem that we solve that same data. Now, eventually, without you ever having to, uh, sort of do a casting it to a different database, you can now do a solid, uh, acquire. These are classic sequel queries, which is our next magic. We are a no SQL database, but we have a full functional sequel. The sequel has been the language that has talked to data for 40 odd years successfully. Every other database has come and try to implement their own QL query language, but they've all failed only sequel as which stood the test of time of 40 odd years. >>Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is, uh, basically, uh, look at the data and any common tutorial, uh, any, uh, any which way you look at the data. All it will come, uh, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries, select star from where Canada stuff, because it's at an English level, it becomes easy to, so the same data, you didn't have to go move it to another database, do your, uh, sort of transformation of the data and all this stuff. Same day that you do this. >>Now, that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, but Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the ability to query the operational data in a different way. I'll talk budding. What was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. >>So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and find different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, uh, the database management system. And that's where the distributed, uh, platform that we have built enables us to get it to where you need the data to be, you know, in a classic way, we call it CDN in the data as in like content delivery networks. So far do static, uh, uh, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >>The first part of the, the answer to my question, are you saying you could do this without skiing with a no schema on, right? And then you can apply those techniques. >>Uh, fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read things. So because there is no schema, it is just a on document that is sitting inside. And Jason is the lingua franca of the web, as you very well know by now. So it just Jason that we manage, you can do key lookups of the Jason. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and the other sophisticated pieces of technology behind it. >>You can do searching on it, using the, um, the full textual analysis pipeline. You can do ad hoc wedding on the analytic side, and you can write your own custom logic on it using our eventing capabilities. So that's, that's what it allows because we keep the data in the native form of Jason. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing, uh, in the last 40 years because we developed various, uh, database systems and data processing systems of various points. In time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. >>We had queuing systems, all the systems, if you want to use any one of them, our answer has always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this is not one to fly instead, bring the logic to the data. So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this, >>As you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >>Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data casting because it required you to have it in seven schema in one sense at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably flooded, but it not really, uh, how do you say, um, keep to the promise that it actually meant to be? So that's why it was a swamp I need, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it, and you create different types of indexes to manage it. You distribute the index, you distribute the data you have, um, like we were discussing, you have acid semantics on top of, and when you, when you put all these things together, uh, it's, it's, it's a tough proposition, but they have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >>So you predicted the trend around multimodal and converged, uh, databases. Um, you kind of led Couchbase through that. I want to, I always ask this question because it's clearly a trend in the industry and it, it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, we have a little teeny scissors and a knife. That's not that sharp. How do you respond to that? Uh, >>A great one. Um, my answer is always, I use another analogy to tackle that, but is that, have you ever accused a smartphone of being a Swiss army knife? No. No. Nobody does that because it's actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Like as in a moment, it could be a Tom, Tom telling you all the directions, the next one, it's your PDA. >>Third one, it's a fantastic phone. Uh, four, it's a beautiful camera, which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment is a video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just taught that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, they missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app is the economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get the alert saying that today you got to leave home at eight 15 for your nine o'clock meeting. >>And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's gone there's notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place without that, you couldn't even do this simple function, uh, in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build, because half the time you're running sideline to sideline, just, you know, um, integrating data from one system to the other. >>So I love the analogy with the smartphone. I w I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? And so, so, but, but is there, is that a fair, where, in other words, those specialized databases, they say there still is a place for them, but they're getting >>Absolutely, absolutely great analogy and a great extension to the question. That's, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of the music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they haven't, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. >>Yes, it's 90% there or 80% there. It depends on your audio file mess of your, uh, I mean, you don't experience the super specialized ones do not go away. You know, there are, there are places where, uh, the specialized use cases will demand a separate system to exist, but even there that has got to be very closed. Um, how do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that, oh, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car and walk into my living room, that's same songs should continue and play in my living room speakers. Then it's a world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >>I love, I love that example too. When I was a kid, we used to go to Twitter, et cetera. And we'd to play around with, we take off the big four foot speakers. Those stores are out of business too. Absolutely. Um, now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi. >>I believe so. Uh, because I think, uh, what had happened was the relational systems. Uh, I've been where the norm, they rule the roost, if you will, for the last 40 odd years, and then gain this no sequel movement, which was almost as though a rebellion from the relational world, we all inhibited, uh, uh, because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee, they required your DBA and your data architect. And you have to call them just to add one column and stuff like that. And the world had moved on. This was the world of blogs and tweets and, uh, you know, um, mashups and, um, uh, uh, a different generation of digital behavior, digital, native people now, um, who are operating in these and the, the applications, the, the consumer facing applications. >>We are living in this world. And yet the enterprise ones were still living in the, um, in the other, the other side of the divide. So all came this solution to say that we don't need SQL. Actually, the problem was never sequel. No sequel was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations, and the inability for these, the system to scale, the relational systems were built like, uh, airplanes, which is that if, uh, San Francisco Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set in from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the alarm to somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. >>These are called vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world, uh, is make the system how it is only scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guests. I'll add one more coach to it, one more car to it. And the better part of the way we have done this year is that, and we have super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have ID only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. >>You can attach the kind of coaches we call this multi-dimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it quite. So that's the beauty of this architecture. Now, why is that important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because you would say that I cannot run this analytical Barre because then my operational workload will suffer. Then my friend, then we'll slow down millions of customers that impacted that problem. We will solve the same data in which you can do analytical buddy, an operational query because they're separated by these cars, right? As in like we, we fence the, the, the resources, so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or equity. >>And then yet you can run this analytical body, which will take a couple of minutes to run one, not impeding the other. So that's in one sense, sort of the, part of the, um, uh, problems that we have solved here is that relational versus, uh, uh, the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same quality language on top. Y it's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, uh, the internal combustion engine, uh, I think gas, uh, you says, these are the issues we really wanted to solve. Um, so solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, or that are for your shifters. >>Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So, uh, even when you feed people the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blue harder to go fast and lean back for, for it to, you know, uh, to apply a break that's, that's how we seem to define, uh, design software. Instead, we should be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, uh, and the gas bottle and the, um, and the gear shifter is by putting cul back on underneath the surface, we have completely solved, uh, the relational, uh, uh, limitations of schema, as well as scalability. >>So in, in, in that way, and by bringing back the classic acid capabilities, which is what relational systems, uh, we accounted on and being able to do that with the sequel programming language, we call it like multi-state SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up the salt in the modern times, but rather than get, um, sort of pedantic about whether it's, we have no SQL or sequel or new sequel, or, uh, you know, any of that sort of, uh, jargon, oriented debate, uh, this, these are the debates of computer science that they are actually, uh, and they were the solve and they have solved them with, uh, the latest release of $7, which we released a few months ago. >>Right, right. Last July, Ravi, we got to leave it there. I, I love the examples and the analogies. I can't wait to be face to face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >>Fantastic. Thanks for the time. And the Aboriginal Dan was, I mean, very insightful questions really appreciate it. Thank you. >>Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

>> Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, is modernize now. Yes, let's talk about that. And with me is Ravi mayor him, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >> Thank you so much. I'm so glad to be here with you. >> I want to ask you what the new requirements are around modern applications. I've seen some of your comments, you got to be flexible, distributed, multimodal, mobile, edge. Those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >> Yeah, I think what has basically happened is that so far it's been a transition of sorts. And now we are come to a point where that tipping point and that tipping point has been more because of COVID and there are COVID has pushed us to a world where we are living in a in a sort of occasionally connected manner where our digital interactions precede, our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than in a digital manner, as opposed to sort of making a more specific human contact. That does really been the sort of accelerant to this modernize Now, as a team. In this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. They're all sitting behind. They used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, but they are all centralized still, but where our engagement happens with the data is at the edge at your point of convenience, at your point of consumption, not where the data is actually sitting. So this has led to, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? But it just basically comes down to the fact that the data needs to be there, if you are engaging with it. And that means if you are doing it on your mobile phone, or if you're sitting, but doing something in your while you're traveling, or whether you're in a subway, whether you're in a plane or a ship, wherever the data needs to come to you and be available, as opposed to every time you going to the data, which is centrally sitting in some place. And that is the fundamental shift in terms of how the modern architecture needs to think when they, when it comes to digital transformation and, transitioning their old applications to the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Otherwise, people are basically waiting for that circle of death that we all know, and blaming the networks and other pieces. The problem was actually, the data is not where you are engaging with it. It's got to be fetched, you know, seven sea's away. And that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >> I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this because date data by its very nature is distributed. It's always been distributed, but with the distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, of, of a super rock solid database that can handle, you know, distributed data? >> Yes. So there are two issues that you alluded little too over there. The first is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data. In one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, when you have the data, you can first look at it to perform. Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five milliseconds, this is, this is a class of problem that we solve that same data. Now, eventually, without you ever having to sort of do a casting it to a different database, you can now do solid queries. Our classic SQL queries, which is our next magic. We are a no SQL database, but we have a full functional SQL. The SQL has been the language that has talked to data for 40 odd years successfully. Every other database has come and tried to implement their own QL query language, but they've all failed only SQL has stood the test of time of 40 odd years. Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is basically a look at the data and any common editorial, any, any which way you look at the data, all it will come, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries select star from where, kind of stuff, because it's at an English level becomes easy to so the same day that you didn't have to go move it to another database, do your sort of transformation of the data and all the stuff, same day that you do this. Now that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, what Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the, your ability to query the operational data in a different way. And talk querying, what was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and apply different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, the database management system. And that's where the distributed platform that we have built enables us to get it to where you need the data to be, you know, in the classic way we call it CDN'ing the data as in like content delivery networks. So far do static, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >> And on the first part of, of the, the, the answer to my question, are you saying you could do this without scheme with a no schema on, right? And then you can apply those techniques. >> Fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read thing. So, because there is no schema, it is just a Json document that is sitting inside. And Json is the lingua franca of the web, as you very well know by now. So it just Json that we manage, you can do key value look ups of the Json. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and other sophisticated pieces of technology behind it. You can do searching on it, using the, the full textual analysis pipeline. You can do ad hoc webbing on the analytics side, and you can write your own custom logic on it using or inventing capabilities. So that's, that's what it allows because we keep the data in the native form of Json. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, bring, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing in the last 40 years, because we developed various database systems and data processing systems at various points in time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. We had queuing systems, all these systems, if you want to use any one of them are answered. It always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this, it's not going to fly instead, bring the logic to the data, right? So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this. >> But as you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >> Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data recasting because it required you to have it in seven schema in one sense at, at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably related, but it not really, how do you say keep to the promise that it actually meant to be? So that's why it was a swamp I mean, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it. And you create different types of indexes to manage it. You distribute the index, you distribute the data you have, like we were discussing, you have ACID semantics on top of, and when you, when you put all these things together, it's, it's, it's a tough proposition, but we have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >> So you predicted the trend around multimodal and converged databases. You kind of led Couchbase through that. I, I want, I always ask this question because it's clearly a trend in the industry and it, and it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, where you have have a little teeny scissors and a knife, that's not that sharp. How, how do you respond to that? >> A great one. My answer is always, I use another analogy to tackle that, and is that, have you ever accused a smartphone of being a Swiss army knife? - No. No. >> Nobody does. That because it actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Right? As in a moment, it could be a TomTom, telling you all the directions, the next one, it's your PDA. Third one. It's a fantastic phone. Four. It's a beautiful camera which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment, it's the video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just thought that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, he missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app based economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get an alert saying that today you got to leave home at >> 8: 15 for your nine o'clock meeting. And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's got this notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place. Without that, you couldn't even do this simple function in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build. Because half the time you're running sideline to sideline, just, you know, integrating data from one system to the other. >> So I love the analogy with the smartphone. I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? So, so, but, but is there, is that a fair and where, in other words, those specialized databases, they say there still is a place for them, but they're getting. >> Absolutely, absolutely great analogy and a great extension to the question. That's like, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of my music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they, I mean, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. Yes, it's 90% there or 80% there. It depends on your audio file-ness of your, I mean, your experience super specialized ones do not go away. You know, there are, there are places where the specialized use cases will demand a separate system to exist. But even there that has got to be very closed. How do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that all, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car, walk into my living room, that same songs should continue and play in my living room speakers. Then it's a connected world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >> I love, I love that example too. When I was a kid, we used to go to Tweeter, et cetera. And we used to play around with three, take home, big four foot speakers. Those stores are out of business too. Absolutely. And now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi? >> I believe so, because I think what had happened was relational systems. I've mean where the norm, they rule the roost, if you will, for the last 40 odd years and then gain this no SQL movement, which was almost as though a rebellion from the relational world, we all inhabited because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee. They required your DBA and your data architect. And you had to call them just to add one column and stuff like that. And the world had moved on. This was a world of blogs and tweets and, you know, mashups and a different generation of digital behavior, There are digital, native people now who are operating in these and the, the applications, the, the consumer facing applications. We are living in this world. And yet the enterprise ones were still living in the, in the other, the other side of the divide. So out came this solution to say that we don't need SQL. Actually the problem was never SQL. No SQL was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations and the inability for these, the system to scale, the relational systems were built like airplanes, which is that if a San Francisco, Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the allowance that you'll somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. These are all vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world is make the system horizontally scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guest. I'll add one more coach to it, one more car to it. And the better part of the way we have done this here is that, and we are super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have, I need only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. You can attach the kind of coaches we call this multidimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it, right? So that's the beauty of this architecture. Now, why is that architecture important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because, you would say that I cannot run this analytical query because then my operational workload will suffer. Then my front end, then we'll slow down millions of customers that impacted that problem. They'll solve the same data once again, do analytical query, an operational query because they're separated by these cars, right? As in like we, we, we fence the, the, the resources so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or a query. And then yet you can run this analytical query, which will take a couple of minutes to them. One, not impeding the other. So that's in one sense, sort of the part of the problems that we have solved it here is that relational versus the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same query language on top. Why? It's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, the internal combustion engine the gas, you says, these were the issues we really wanted to solve. So solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, over there your gear shifters. Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So even when you feed people, the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blow harder to go fast. And they lean back for, for it to, you know, to apply a break that's, that's how we seem to define design software. Instead, we shouldn't be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, and the gas pedal and the, and the gear shifters by putting SQL back on underneath the surface, we have completely solved the relational limitations of schema, as well as scalability. So in, in, in that way, and by bringing back the classic ACID capabilities, which is what relational systems we accounted on, and being able to do that with the SQL programming language, we call it like multi-statement SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up to solve in the modern times, rather than get sort of pedantic about whether it's we have no SQL or SQL or new SQL, or, you know, any of that sort of jargon oriented debate. This is, these are the debates of computer science that they are actually, and they were the solve, and they have solved them with the latest release of 7.0, which we released a few months ago. >> Right, right. Last July, Ravi, we got got to leave it there. I love the examples and the analogies. I can't wait to be face-to-face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >> Fantastic. Thanks for the time. And the opportunity I was, I mean, very insightful questions really appreciate it. - Thank you. >> Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

(upbeat music) >> Welcome to this CUBE Power Panel where we're going to talk about application modernization, also success templates, and take a look at some new survey data to see how CIOs are thinking about digital transformation, as we get deeper into the post isolation economy. And with me are three familiar VIP guests to CUBE audiences. Tony Bear, the principal at DB InSight, Doug Henschen, VP and principal analyst at Constellation Research and Sanjeev Mohan principal at SanjMo. Guys, good to see you again, welcome back. >> Thank you. >> Glad to be here. >> Thanks for having us. >> Glad to be here. >> All right, Doug. Let's get started with you. You know, this recent survey, which was commissioned by Couchbase, 650 CIOs and CTOs, and IT practitioners. So obviously very IT heavy. They responded to the following question, "In response to the pandemic, my organization accelerated our application modernization strategy and of course, an overwhelming majority, 94% agreed or strongly agreed." So I'm sure, Doug, that you're not shocked by that, but in the same survey, modernizing existing technologies was second only behind cyber security is the top investment priority this year. Doug, bring us into your world and tell us the trends that you're seeing with the clients and customers you work with in their modernization initiatives. >> Well, the survey, of course, is spot on. You know, any Constellation Research analyst, any systems integrator will tell you that we saw more transformation work in the last two years than in the prior six to eight years. A lot of it was forced, you know, a lot of movement to the cloud, a lot of process improvement, a lot of automation work, but transformational is aspirational and not every company can be a leader. You know, at Constellation, we focus our research on those market leaders and that's only, you know, the top 5% of companies that are really innovating, that are really disrupting their markets and we try to share that with companies that want to be fast followers, that these are the next 20 to 25% of companies that don't want to get left behind, but don't want to hit some of the same roadblocks and you know, pioneering pitfalls that the real leaders are encountering when they're harnessing new technologies. So the rest of the companies, you know, the cautious adopters, the laggards, many of them fall by the wayside, that's certainly what we saw during the pandemic. Who are these leaders? You know, the old saw examples that people saw at the Amazons, the Teslas, the Airbnbs, the Ubers and Lyfts, but new examples are emerging every year. And as a consumer, you immediately recognize these transformed experiences. One of my favorite examples from the pandemic is Rocket Mortgage. No disclaimer required, I don't own stock and you're not client, but when I wanted to take advantage of those record low mortgage interest rates, I called my current bank and some, you know, stall word, very established conventional banks, I'm talking to you Bank of America, City Bank, and they were taking days and weeks to get back to me. Rocket Mortgage had the locked in commitment that day, a very proactive, consistent communications across web, mobile, email, all customer touchpoints. I closed in a matter of weeks an entirely digital seamless process. This is back in the gloves and masks days and the loan officer came parked in our driveway, wiped down an iPad, handed us that iPad, we signed all those documents digitally, completely electronic workflow. The only wet signatures required were those demanded by the state. So it's easy to spot these transformed experiences. You know, Rocket had most of that in place before the pandemic, and that's why they captured 8% of the national mortgage market by 2020 and they're on track to hit 10% here in 2022. >> Yeah, those are great examples. I mean, I'm not a shareholder either, but I am a customer. I even went through the same thing in the pandemic. It was all done in digital it was a piece of cake and I happened to have to do another one with a different firm and stuck with that firm for a variety of reasons and it was night and day. So to your point, it was a forced merge to digital. If you were there beforehand, you had real advantage, it could accelerate your lead during the pandemic. Okay, now Tony bear. Mr. Bear, I understand you're skeptical about all this buzz around digital transformation. So in that same survey, the data shows that the majority of respondents said that their digital initiatives were largely reactive to outside forces, the pandemic compliance changes, et cetera. But at the same time, they indicated that the results while somewhat mixed were generally positive. So why are you skeptical? >> The reason being, and by the way, I have nothing against application modernization. The problem... I think the problem I ever said, it often gets conflated with digital transformation and digital transformation itself has become such a buzzword and so overused that it's really hard, if not impossible to pin down (coughs) what digital transformation actually means. And very often what you'll hear from, let's say a C level, you know, (mumbles) we want to run like Google regardless of whether or not that goal is realistic you know, for that organization (coughs). The thing is that we've been using, you know, businesses have been using digital data since the days of the mainframe, since the... Sorry that data has been digital. What really has changed though, is just the degree of how businesses interact with their customers, their partners, with the whole rest of the ecosystem and how their business... And how in many cases you take look at the auto industry that the nature of the business, you know, is changing. So there is real change of foot, the question is I think we need to get more specific in our goals. And when you look at it, if we can boil it down to a couple, maybe, you know, boil it down like really over simplistically, it's really all about connectedness. No, I'm not saying connectivity 'cause that's more of a physical thing, but connectedness. Being connected to your customer, being connected to your supplier, being connected to the, you know, to the whole landscape, that you operate in. And of course today we have many more channels with which we operate, you know, with customers. And in fact also if you take a look at what's happening in the automotive industry, for instance, I was just reading an interview with Bill Ford, you know, their... Ford is now rapidly ramping up their electric, you know, their electric vehicle strategy. And what they realize is it's not just a change of technology, you know, it is a change in their business, it's a change in terms of the relationship they have with their customer. Their customers have traditionally been automotive dealers who... And the automotive dealers have, you know, traditionally and in many cases by state law now have been the ones who own the relationship with the end customer. But when you go to an electric vehicle, the product becomes a lot more of a software product. And in turn, that means that Ford would have much more direct interaction with its end customers. So that's really what it's all about. It's about, you know, connectedness, it's also about the ability to act, you know, we can say agility, it's about ability not just to react, but to anticipate and act. And so... And of course with all the proliferation, you know, the explosion of data sources and connectivity out there and the cloud, which allows much more, you know, access to compute, it changes the whole nature of the ball game. The fact is that we have to avoid being overwhelmed by this and make our goals more, I guess, tangible, more strictly defined. >> Yeah, now... You know, great points there. And I want to just bring in some survey data, again, two thirds of the respondents said their digital strategies were set by IT and only 26% by the C-suite, 8% by the line of business. Now, this was largely a survey of CIOs and CTOs, but, wow, doesn't seem like the right mix. It's a Doug's point about, you know, leaders in lagers. My guess is that Rocket Mortgage, their digital strategy was led by the chief digital officer potentially. But at the same time, you would think, Tony, that application modernization is a prerequisite for digital transformation. But I want to go to Sanjeev in this war in the survey. And respondents said that on average, they want 58% of their IT spend to be in the public cloud three years down the road. Now, again, this is CIOs and CTOs, but (mumbles), but that's a big number. And there was no ambiguity because the question wasn't worded as cloud, it was worded as public cloud. So Sanjeev, what do you make of that? What's your feeling on cloud as flexible architecture? What does this all mean to you? >> Dave, 58% of IT spend in the cloud is a huge change from today. Today, most estimates, peg cloud IT spend to be somewhere around five to 15%. So what this number tells us is that the cloud journey is still in its early days, so we should buckle up. We ain't seen nothing yet, but let me add some color to this. CIOs and CTOs maybe ramping up their cloud deployment, but they still have a lot of problems to solve. I can tell you from my previous experience, for example, when I was in Gartner, I used to talk to a lot of customers who were in a rush to move into the cloud. So if we were to plot, let's say a maturity model, typically a maturity model in any discipline in IT would have something like crawl, walk, run. So what I was noticing was that these organizations were jumping straight to run because in the pandemic, they were under the gun to quickly deploy into the cloud. So now they're kind of coming back down to, you know, to crawl, walk, run. So basically they did what they had to do under the circumstances, but now they're starting to resolve some of the very, very important issues. For example, security, data privacy, governance, observability, these are all very big ticket items. Another huge problem that nav we are noticing more than we've ever seen, other rising costs. Cloud makes it so easy to onboard new use cases, but it leads to all kinds of unexpected increase in spikes in your operating expenses. So what we are seeing is that organizations are now getting smarter about where the workloads should be deployed. And sometimes it may be in more than one cloud. Multi-cloud is no longer an aspirational thing. So that is a huge trend that we are seeing and that's why you see there's so much increased planning to spend money in public cloud. We do have some issues that we still need to resolve. For example, multi-cloud sounds great, but we still need some sort of single pane of glass, control plane so we can have some fungibility and move workloads around. And some of this may also not be in public cloud, some workloads may actually be done in a more hybrid environment. >> Yeah, definitely. I call it Supercloud. People win sometimes-- >> Supercloud. >> At that term, but it's above multi-cloud, it floats, you know, on topic. But so you clearly identified some potholes. So I want to talk about the evolution of the application experience 'cause there's some potholes there too. 81% of their respondents in that survey said, "Our development teams are embracing the cloud and other technologies faster than the rest of the organization can adopt and manage them." And that was an interesting finding to me because you'd think that infrastructure is code and designing insecurity and containers and Kubernetes would be a great thing for organizations, and it is I'm sure in terms of developer productivity, but what do you make of this? Does the modernization path also have some potholes, Sanjeev? What are those? >> So, first of all, Dave, you mentioned in your previous question, there's no ambiguity, it's a public cloud. This one, I feel it has quite a bit of ambiguity because it talks about cloud and other technologies, that sort of opens up the kimono, it's like that's everything. Also, it says that the rest of the organization is not able to adopt and manage. Adoption is a business function, management is an IT function. So I feed this question is a bit loaded. We know that app modernization is here to stay, developing in the cloud removes a lot of traditional barriers or procuring instantiating infrastructure. In addition, developers today have so many more advanced tools. So they're able to develop the application faster because they have like low-code/no-code options, they have notebooks to write the machine learning code, they have the entire DevOps CI/CD tool chain that makes it easy to version control and push changes. But there are potholes. For example, are developers really interested in fixing data quality problems, all data, privacy, data, access, data governance? How about monitoring? I doubt developers want to get encumbered with all of these operationalization management pieces. Developers are very keen to deliver new functionality. So what we are now seeing is that it is left to the data team to figure out all of these operationalization productionization things that the developers have... You know, are not truly interested in that. So which actually takes me to this topic that, Dave, you've been quite actively covering and we've been talking about, see, the whole data mesh. >> Yeah, I was going to say, it's going to solve all those data quality problems, Sanjeev. You know, I'm a sucker for data mesh. (laughing) >> Yeah, I know, but see, what's going to happen with data mesh is that developers are now going to have more domain resident power to develop these applications. What happens to all of the data curation governance quality that, you know, a central team used to do. So there's a lot of open ended questions that still need to be answered. >> Yeah, That gets automated, Tony, right? With computational governance. So-- >> Of course. >> It's not trivial, it's not trivial, but I'm still an optimist by the end of the decade we'll start to get there. Doug, I want to go to you again and talk about the business case. We all remember, you know, the business case for modernization that is... We remember the Y2K, there was a big it spending binge and this was before the (mumbles) of the enterprise, right? CIOs, they'd be asked to develop new applications and the business maybe helps pay for it or offset the cost with the initial work and deployment then IT got stuck managing the sprawling portfolio for years. And a lot of the apps had limited adoption or only served a few users, so there were big pushes toward rationalizing the portfolio at that time, you know? So do I modernize, they had to make a decision, consolidate, do I sunset? You know, it was all based on value. So what's happening today and how are businesses making the case to modernize, are they going through a similar rationalization exercise, Doug? >> Well, the Y2K era experience that you talked about was back in the days of, you know, throw the requirements over the wall and then we had waterfall development that lasted months in some cases years. We see today's most successful companies building cross functional teams. You know, the C-suite the line of business, the operations, the data and analytics teams, the IT, everybody has a seat at the table to lead innovation and modernization initiatives and they don't start, the most successful companies don't start by talking about technology, they start by envisioning a business outcome by envisioning a transformed customer experience. You hear the example of Amazon writing the press release for the product or service it wants to deliver and then it works backwards to create it. You got to work backwards to determine the tech that will get you there. What's very clear though, is that you can't transform or modernize by lifting and shifting the legacy mess into the cloud. That doesn't give you the seamless processes, that doesn't give you data driven personalization, it doesn't give you a connected and consistent customer experience, whether it's online or mobile, you know, bots, chat, phone, everything that we have today that requires a modern, scalable cloud negative approach and agile deliver iterative experience where you're collaborating with this cross-functional team and course correct, again, making sure you're on track to what's needed. >> Yeah. Now, Tony, both Doug and Sanjeev have been, you know, talking about what I'm going to call this IT and business schism, and we've all done surveys. One of the things I'd love to see Couchbase do in future surveys is not only survey the it heavy, but also survey the business heavy and see what they say about who's leading the digital transformation and who's in charge of the customer experience. Do you have any thoughts on that, Tony? >> Well, there's no question... I mean, it's kind like, you know, the more things change. I mean, we've been talking about that IT and the business has to get together, we talked about this back during, and Doug, you probably remember this, back during the Y2K ERP days, is that you need these cross functional teams, we've been seeing this. I think what's happening today though, is that, you know, back in the Y2K era, we were basically going into like our bedrock systems and having to totally re-engineer them. And today what we're looking at is that, okay, those bedrock systems, the ones that basically are keeping the lights on, okay, those are there, we're not going to mess with that, but on top of that, that's where we're going to innovate. And that gives us a chance to be more, you know, more directed and therefore we can bring these related domains together. I mean, that's why just kind of, you know, talk... Where Sanjeev brought up the term of data mesh, I've been a bit of a cynic about data mesh, but I do think that work and work is where we bring a bunch of these connected teams together, teams that have some sort of shared context, though it's everybody that's... Every team that's working, let's say around the customer, for instance, which could be, you know, in marketing, it could be in sales, order processing in some cases, you know, in logistics and delivery. So I think that's where I think we... You know, there's some hope and the fact is that with all the advanced, you know, basically the low-code/no-code tools, they are ways to bring some of these other players, you know, into the process who previously had to... Were sort of, you know, more at the end of like a, you know, kind of a... Sort of like they throw it over the wall type process. So I do believe, but despite all my cynicism, I do believe there's some hope. >> Thank you. Okay, last question. And maybe all of you could answer this. Maybe, Sanjeev, you can start it off and then Doug and Tony can chime in. In the survey, about a half, nearly half of the 650 respondents said they could tangibly show their organizations improve customer experiences that were realized from digital projects in the last 12 months. Now, again, not surprising, but we've been talking about digital experiences, but there's a long way to go judging from our pandemic customer experiences. And we, again, you know, some were great, some were terrible. And so, you know, and some actually got worse, right? Will that improve? When and how will it improve? Where's 5G and things like that fit in in terms of improving customer outcomes? Maybe, Sanjeev, you could start us off here. And by the way, plug any research that you're working on in this sort of area, please do. >> Thank you, Dave. As a resident optimist on this call, I'll get us started and then I'm sure Doug and Tony will have interesting counterpoints. So I'm a technology fan boy, I have to admit, I am in all of all these new companies and how they have been able to rise up and handle extreme scale. In this time that we are speaking on this show, these food delivery companies would have probably handled tens of thousands of orders in minutes. So these concurrent orders, delivery, customer support, geospatial location intelligence, all of this has really become commonplace now. It used to be that, you know, large companies like Apple would be able to handle all of these supply chain issues, disruptions that we've been facing. But now in my opinion, I think we are seeing this in, Doug mentioned Rocket Mortgage. So we've seen it in FinTech and shopping apps. So we've seen the same scale and it's more than 5G. It includes things like... Even in the public cloud, we have much more efficient, better hardware, which can do like deep learning networks much more efficiently. So machine learning, a lot of natural language programming, being able to handle unstructured data. So in my opinion, it's quite phenomenal to see how technology has actually come to rescue and as, you know, billions of us have gone online over the last two years. >> Yeah, so, Doug, so Sanjeev's point, he's saying, basically, you ain't seen nothing yet. What are your thoughts here, your final thoughts. >> Well, yeah, I mean, there's some incredible technologies coming including 5G, but you know, it's only going to pave the cow path if the underlying app, if the underlying process is clunky. You have to modernize, take advantage of, you know, serverless scalability, autonomous optimization, advanced data science. There's lots of cutting edge capabilities out there today, but you know, lifting and shifting you got to get your hands dirty and actually modernize on that data front. I mentioned my research this year, I'm doing a lot of in depth looks at some of the analytical data platforms. You know, these lake houses we've had some conversations about that and helping companies to harness their data, to have a more personalized and predictive and proactive experience. So, you know, we're talking about the Snowflakes and Databricks and Googles and Teradata and Vertica and Yellowbrick and that's the research I'm focusing on this year. >> Yeah, your point about paving the cow path is right on, especially over the pandemic, a lot of the processes were unknown. But you saw this with RPA, paving the cow path only got you so far. And so, you know, great points there. Tony, you get the last word, bring us home. >> Well, I'll put it this way. I think there's a lot of hope in terms of that the new generation of developers that are coming in are a lot more savvy about things like data. And I think also the new generation of people in the business are realizing that we need to have data as a core competence. So I do have optimism there that the fact is, I think there is a much greater consciousness within both the business side and the technical. In the technology side, the organization of the importance of data and how to approach that. And so I'd like to just end on that note. >> Yeah, excellent. And I think you're right. Putting data at the core is critical data mesh I think very well describes the problem and (mumbles) credit lays out a solution, just the technology's not there yet, nor are the standards. Anyway, I want to thank the panelists here. Amazing. You guys are always so much fun to work with and love to have you back in the future. And thank you for joining today's broadcast brought to you by Couchbase. By the way, check out Couchbase on the road this summer at their application modernization summits, they're making up for two years of shut in and coming to you. So you got to go to couchbase.com/roadshow to find a city near you where you can meet face to face. In a moment. Ravi Mayuram, the chief technology officer of Couchbase will join me. You're watching theCUBE, the leader in high tech enterprise coverage. (bright music)