>>The cube presents, Coon and cloud native con Europe, 2022. Brought to you by red hat, the cloud native computing foundation and its ecosystem partners. >>Welcome to Licia Spain and cube con cloud native con 2022 Europe. I'm Keith Townsend, along with Paul Gillon senior editor, enterprise architecture for Silicon angle. We're gonna talk to some amazing folks. Day two coverage of Q con cloud native con Paul. We did the wrap up yesterday. Great. A great back and forth about what en Rico about yesterday's, uh, session. What are you looking for to today? >>I'm looking for, uh, to understand better, uh, how Kubernetes is being put into production, the types of applications that are being built on top of it. Yesterday, we talked a lot about infrastructure today. I think we're gonna talk a little bit more about applications, including with our first guest. >>Yeah, I was speaking our first guest. We have ish Degan CPO chief product officer at Hazelcast Hazelcast has been on the program before, but you, this is your first time in the queue, correct? >>It, it is Keith. Yeah. Well, >>Welcome to been Cuban. So we're talking data, which is always a fascinating topic. Containers are, have been known for not being supportive of stateful applications. At least you shouldn't hold the traditional thought. You shouldn't hold stateful data in containers. Tell me about the relationship between Hazel cast and containers we're at Cuan. >>Yeah, so a little bit about, uh, Hazelcast. We are a real time data platform and, uh, we are not a database, but a data platform because we basically allow, uh, data at rest as well as data in motion. So you can imagine that if you're writing an application, you can basically query and join a data coming in events, as well as data, which might have been persisted. So you can do both stream processing as well as, you know, low latency data access. And, and this platform of course, is supported on all the clouds. And we kind of delegate the orchestration of this kind of scale out system to Kubernetes. Um, and you know, that provides a resiliency and many things which go along with that. >>So you say you don't, you're not a database platform. What are you used for to manage the data? >>So we are, uh, we are memory first. So we are, you know, we started with low latency applications, but then we realized that real time has really become a business term. It's it's more of a business SLA mm-hmm, <affirmative>, it's really the, we see the opportunity, the punctuated change, which is happening in the market today is about real time data access to real time. I mean, there are real time applications. Our customers are building around real time offers, um, realtime thread detection. I mean, just imagine, you know, one of our customers like B and P par bars, they have, they basically originate a loan while the customer is banking. So you are in an ATM machine and you swipe your card and you are asking for, you know, taking 50 euros out. And at that point they can actually originate a custom loan offer based on your existing balance you're existing request and your credit score in that moment. So that's a value moment for them and they actually saw 400% loan origination go up because of that, because nobody's gonna be thinking about a credit, uh, line of credit after they're done banking. So it's in that value moment and we allow basically our data platform allows you to have fast access to data and also process incoming streams. So not before they get stored, but as they're coming in. >>So if I'm a developer and cuon is definitely a conference for developer and I, I come to the booth and I hear <inaudible>, that's the end value. I, I hear what I can do with my application. I guess the question is, how do I get there? I mean, uh, if it's not a database, how do I make a call from a container to, from my microservice to Hazel cath? Like, do I think of this as a, uh, a CNI or, or C CSI? How do I access >>PA care? Yeah. So, so we, uh, you know, we are, our server is actually built in Java. So a lot of the application which get written on top of the data platform are basically accessing through Java APIs. Or as you have a.net shop, you can actually use.net API. So we are basically an API first platform and SQL is basically the polyglot way of accessing data, both streaming data, as well as it store data. So most of the application developers, a lot of it is run done in microservices, and they're doing these fast get inputs for data. So they, they have a key, they want to get to a customer, they give a customer ID. And the beauty is that, um, while they're processing the events, they can actually enrich it because you need contextual information as well. So going back to the ATM example, you know, at that event happened, somebody swiped the card and ask for 50 euros, and now you want more information like credit score information, all that needs to be combined in that, in that value moment. >>So we allow you to do those joins and, you know, the contextual information is very important. So you see a lot of streaming platform out there, which just do streaming, but if you're an application developer, like you asked, you have to basically do call out to a streaming platform to get, um, to do streaming analytics and then do another call to get the context of that. You know, what is the credit score for this customer? But whereas in our case, because the data platform supports both streaming as well as data at rest, you can do that in one call and, you know, you don't want to have the operational complexity to stand out. Two different scale out servers is, is, is, is humongous, right? I mean, you want to build your business application. So, >>So you are querying data streaming data and data rest yes. In the same query >>Yes. In the same query. And we are memory first. So what happens is that we store a lot of the hot data in memory. So we have a scale out Ram based server. So that's where you get the low latency from. In fact, last year we did a benchmark. We were able to process a billion events a second, uh, with 99% of the latency under 30 milliseconds. So that kind of processing and that kind of power is, and, and the most important thing is determinism. I mean, you know, there's a lot of, um, if you look at real time, what real time is, is about this predictable latency at scale, because ultimately your, your adhering to a business SLA is not about milliseconds or microsecond. It's what your business needs. If your business needs that you need to deny or, uh, approve a credit credit card transaction in 50 milliseconds, that's your business SLA, and you need that predictability for every transaction. >>So talk to us about how how's this packaged in consumed. Cause I'm hearing a, a bunch of server Ram I'm hearing numbers that we're trying to adapt away from at this conference. We don't wanna see the onlay. We just want to use it. >>Yeah. So, so we kind of take a bit that, that complexity of managing this scale out, um, uh, uh, cluster, which actually utilizes Rams from each server. And then, you know, if you, you can configure it so that the hard set of data is in Ram, but the data, which is, you know, not so hard can actually go into a tiered storage model. So we are memory first. So, but what you are doing is you're doing simple, it's an API. So you do basically a crud, right? You create records, you read them through SQL. So for you, it's, it's, it's kind of like how you access that database. And we also provide you, you know, real time is also a journey. I mean, a lot of customers, you know, you don't want to rip their existing system and deploy another kind of scale out platform. Right? So we, we see a lot of these use cases where they have a database and we can sit in between the database, a system of record and the application. So we are kind of in between there. So that's, that's the journey you can take to real time. >>How does Kubernetes, uh, containers and Kubernetes change the game for real time analytics? >>Yeah. So, uh, Kubernetes does change it because what's hap first of all, we service most of the operational workloads. So it's, it's more on the, a lot of our customers. We have most, most of the big banks credit card companies in financial services and retail. Those are the two big sectors for us. And first of all, you know, a lot of these operational workloads are moving to the cloud and with move to the cloud, they're actually taking their existing applications and, and moving to, you know, one of the providers and to kind of orchestrate this scale out platform, which does auto scaling, that's where the benefit comes from mm-hmm <affirmative>. And it also gives them the freedom of choice. So, you know, the Kubernetes is, you know, a standard which goes across cloud providers. So that gives them the benefit that they can actually take their application. And if they want, they can actually move it to a different, a different cloud provider because we take away the orchestration complexity, you know, in that abstraction layer. >>So what happens when I need to go really fast? I mean, I, I, I need, uh, I'm looking at bare metal and I'm looking at really scaling a, a, a homogeneous application in a single data center set of data centers. Is there a bare metal play here? >>Yes. There, there, there are some very, very, uh, like if you want microsecond latency, mm-hmm, <affirmative>, um, you know, we have customers who actually store two to four terabytes in Ram and, and they can actually stand up. Um, you know, again, it depends on what kind of deployment you want. You can either scale up or scale out, scaling up is expensive, you know, because those boxes are not cheap, but if you have a requirement like that, where there is sub millisecond or microphone latency requirement, you could actually store the entire data set. I mean, a lot of the operational data sets are under four terabytes. So it's not uncommon that you could actually take the entire operational transactional data set, actually move, move that to a pure Ram. But, uh, I think now we, we also see that these operational workloads are also, there's a need for analytics to be done on top as well. >>I mean, we, going back to the example I gave you, so this, this, uh, customer is not only doing stream crossing, they're also influencing a machine learning algorithm in that same, in the same kind of cycle in the life cycle. So they might have trained a machine learning or algorithm on a data lake somewhere, but once they're ready, they're actually influencing the ML algorithm in our kind of life cycle right there. So, you know, that that really brings analytics and transactions kind of together because after all transactions are where the real, you know, insights are. >>Yeah. I'm, I'm struggling a little bit with this, with these two different use cases where I have transactional basically a transactional database or transactional data platform alongside a analytics platform. Those are two, like they're two different things. I have a, you know, I, I have spinning rust for one, and then I have memory and, and MBME for another. Uh, and that requires tuning requires DBAs. It requires a lot of overhead, there seems to be some type of secret sauce going on here. >>Yeah. Yeah. So, I mean, you know, we, we basically say that if you are, if you have a business case where you want to make a decision, you know, you, the only chance to succeed is where you are not making a decision tomorrow based on today's data. Right? I mean, the only way to act on that data is today. So the act is a keyword here. We actually let you generate a realtime offer. We, we let you do credit card fraud detection. In that moment, the analytics is about knowing less about acting on it. Right? Most of our applications are machine critical. They're acting on real time. I think when you talk about like the data lakes there, there's actually a real time there as well, but it's about knowing, and we believe that the operational side is where, you know, that value moment is there, you know, what good is, is to know about something tomorrow, you know, if something wrong happened, I mean, it, yeah, so there's a latency squeeze there as well, but we are on, on more on the kind of transaction and operational side. >>I gotcha. Yeah. So help me understand, like integrations. A lot of the, the, when I think of transactions, I'm thinking of SAP, Oracle, where the process is done, or some legacy banking or not legacy or new modern banking app, how does the data get from one platform to a, to Hazel cast so I can make those >>Decisions? Yeah. So we have, uh, this, the streaming engine, we have has a whole bunch of connectors to a lot of data sources. So in fact, most of our use cases already have data sources underneath there, their databases there's KA connectors, you know, joining us because if you look at it, events is, are comprised of transactions. So something, a customer did, uh, a credit card swipe, right. And also events events could be machine or IOT. So it's really unique connectivity and data ingestion before you can process that. So we have, uh, a whole suite of connectors to kind of bring data in, in our platform. >>We've been talking a lot, these last couple of days about, uh, about the edge and about moving processing capability closer to the edge. How do you enable that? >>Yeah. So edge is actually very, very relevant because of what's happening is that, um, you know, if you, if you look at like a edge deployment use case, um, you know, we have a use case where data is being pushed from these different edge devices to cloud data warehouse. Right. But just imagine that you want to be filtering data at the, at, at where it is being originated from, and you wanna push only relevant data to, to maybe a central data lake where you might want to do, you know, train your machine learning models. Mm-hmm <affirmative> so that at the edge, we are actually able to process that data. So Hazel cast will allow you to actually write a data pipeline and do stream processing so that you might want to just push, you know, a part or a subset of data, which applies by the rules. Uh, so there's, there's a big, um, uh, I think edge is, you know, there's a lot of data being generated and you don't want like garbage and garbage out there's there's, there is there's filtration done at the edge. So that only the relevant data lands in a data, data lake or something like that. >>Well, Monash, we really appreciate you stopping by realtime data is an exciting area of coverage for the queue overall from Valencia Spain, I'm Keith Townsend, along with Paul Gillon, and you're watching the queue, the leader in high tech coverage.