How Hazelcast Delivers Real-Time Data Processing

ABOUT THIS EPISODE

Hazelcast is a real-time data platform that can run anywhere—from the edge to the data center to the cloud. In other words, it brings the computation to the data rather than moving the data to the computation. 

That enables the ability to connect insights to the real-time actions of the customer. 

Want to hear more about it? 

John DesJardins , Chief Technology Officer, and Mark Santos , Vice President of Worldwide Business Development, join the show to discuss how Hazelcast can accelerate business decisions, the role that Kubernetes plays in modern data processing, and why it’s important to have a good partner ecosystem.  

We discuss:

  • The variety of use cases for Hazelcast
  • Hazelcast’s strong alliances with other independent software vendors and hyperscalers
  • How Kubernetes is changing the way we process data
  • Advice for an up-and-coming technologist   

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You are listening to application modernization, a show that spotlights the forward thinking leaders of Highgro software companies. From scaling applications and accelerating time to market to avoiding expensive license and costs, we discuss how you can innovate with new technology and forward thinking processes and save some cash in the process. Let's get into it. Thanks for listening to the application modern position podcast, presented by shadow Sooft. I'm your host, Nick Marcarelli. Today on the podcast we spoke to CTO and VP of worldwide business development at Hazel cast. We spoke about the change in data collection and processing and how Hazel cast is helping customers perform these functions in one platform, well making sure the offering is accessible across a large variation of infrastructure, whether it is in the cloud, we're on premise. Thanks to red hat for supporting the PODCAST and enjoy this conversation with John a mark and Hazel cast. John Mark, thanks for joining the PODCAST. Glad to have you here today. Excited to be here. Thanks for thanks for having us so I always like to start with inner introductions. The listeners want to know who you are, why you're here. So why don't we start with a little background. Maybe John will start with you. Oh, how you got here, and then we'll go to mark and go from there. Sure of things. Sounds good. Yeah, so, John, days are Dan. I'm CTEO here at Hazel cast. I've been here about three and a half, coming up towards actually going to be four years this year. And Yeah, my travel or journey to Hazel cast really came through my time at cloud era and before that I was a software gem one methods, and really at cloud era we had a powerful suite of analytics and distributed computing and data capabilities, but it was very complex and challenging for a lot of our customers and when I saw how simple Hazel cast was really drew me to come here and and help, you know, drive further innovation. So excited about being at Hazel cast. You know, it's a company with a great background, which we'll talk more about for sure. Very good. Mark. Yeah, hi, Mark Saintis here, VP of worldwide business development, and spent my the vast majority of my career in this space, what could be characterized as enterprise middleware or integration and data spending a lot of time at a lot of the dominant vendors, like early on it was a tipicot software and be a systems got acquired, the Oracle, Mule soft g digital in here. So it's been it's, you know, been exciting to see the world change, or the the the market change, and in many ways it changes but then also remains the same and we're talking about really similar things or similar architectures but using different words. So great to be here today. Great, thanks so much. So obviously we want to talk about Hazel cast. So who would like to start the shot? We could probably go many minutes on this and that's fine, but you know, maybe elevator GTM and then also get in the technical concepts and use cases as well. Yeah, sure, absolutely. I'll kind of kick that off. So Hazel cast is a real time data platform that is unique in that it combines two sets of capabilities that are typically not combined into a single unified run time. So one of those capabilities is, you know, storing and processing data at rest. So Hazel cast is a very fast data store for real time applications and low latency, you know, highly scalable and resilient. And then the other component of Hazel cast, or capability set, I should say, is our streaming, analytics and sequel engine, which allows you to do either continuous processing of data and motion, and it can also do, you know, batch based processing of data. And really what it provides is distributed computation on, you know, changing data. So those two came capabilities are are typically, you know, optimized in different run times, and what the implication of that is is that it's your separating out and introducing more moving parts by having a different run times. We were able to unify our platform last year into a single run time that...

...provides distributed computation on data and storing of data. So, you know, you can kind of compare it to combining databases with streaming or data pipeline products. And so how does that, you know, help customers? How does that help customers? Is that you the customers can simultaneously be, you know, aggregating and preparing data and it can also be processing that data and then making it available to applications. And because it's also a very simple architecture running on top of Java and Jvm, that means it's very easy to operate and deploy and easy to, you know, integrate very closely with your application. So all of this is a longwinded way of saying that. You know, we have a unique, simple architecture for real time data processing to make applications more intelligent. So a layman's use case might be collecting data on the edge and processing it there. Yeah, so a fair way to say that why we run anywhere from the edge to data center to cloud, and so what we could do is process the data wherever is born, whether that's in the edge or whether it's in applications hosted in a cloud or data center. We can basically bring the computation to the data rather than having to move the data to the computation. And so what that allows you to do is very real time intelligent use cases like in the IOT world, maybe predictive monitoring or, you know, something like smart city use cases. In the traditional online application world, it means, you know, faster calculations supporting your online store, making inventory, you know, more accurate and and able to be calculated with more precision and real time. Or, you know, something like taking all of that data about what people are doing in your online store and then using it to inform real time offers. Yeah, so all of these are just kind of few examples of the types of use cases people leverage or technology. For Gotcha. So the hard way in the past. How were people doing that? And, you know, it all high level. Yeah, so mostly for me our way was, you know, you had something like, you know, a callder, you know platform where you could process a lot of data and and you know, do some of the ending and data pipelines using something like a patch, you spark, but you also needed a place to store that data. So you might be storing it in something like h base, if you're at Hutter, or, you know, Delta Lake, if you're using spark, at data bricks. And then, you know, now you've got this whole data architecture and then you have an application architecture, which is also where you kind of typically have your database. And so, you know, you may have, you know, applications that are, you know, hosting, you know, User Interactions, mobile APPS, all kinds of different applications. Those applications are built on a completely different architecture and those two architectures are maintained by people with different focus. Careerwise, you have people are like Java developers, dotnet developers, building applications, you know, mobile, you know and u x experts and then you have people building, you know, your data engineering and those your world's are separated by almost a chasm, and what that means is that your ability to really change the experience of the application is limited because of that data divide. So that was the old way, and so now we're bringing all of that together into one run time that's also distributed, and the fact that it's also one run time makes it very suitable for deployment on top of your kind of cloud architectures and cloud native technologies. So who ends up being your customer? Would it be like the person who maintains Hazelokas, is an OPS person, data engineer? Is it a combination? Yeah, so, well, you know. So the customer typically is a combination of your, you know, business stakeholders, the developers building applications and the OPPS team maintaining those applications. But in terms of, you know, who uses Hazel Cast, you know, it's really did kind of typically split into people who are either developers, data engineers or other people working with data and creating those, you know, data pipelines or real time, you know, data processing, real time machine learning and other types of...

...workloads. And then once that is built, then obviously it gets deployed and then you have your ops team who needs to kind of keep it, keep it running. And you know, the good part about the OPPS side of it is it's just a job application. It runs on top of anything from a raspberry pie or a bare metal server to Coubernetti's and so from an operational standpoint, and you know, I used to have to go to people and ops and be like who are your best ops people when I was at cloud era, and I'd say you can send them to a class on how to become a cloudera administrator, and once they start doing that they're not going to be doing other, you know, ops work, because it's such a huge complex thing to keep running, whereas with Hazel cast it's just a job application. So we go to the your OPS Toham and say, yeah, we have this many, you know, nodes in a cluster. How many clusters? She have a different data centers or cloud regions, and yeah, it's a job APP, has a certain amount of memory, uses certain disc you know, places it rights to. You know, it's it's no different than really maintaining any other a job application operational. And we have a great ops tools as well, so it becomes pretty easy to adopt. So the simplicity of of your platform is really what's winning, I guess in the field that, yeah, you're taking two very complex things, put him together in one platform and then, you know, make it it easy to collect and process that data. Exactly. It's it's dramatically simplifying your architecture and also simplifying the work that you have to do, is a developer or data engineer, because you know you're not working with all these different moving parts. Got It. Do you have a an interesting story or two you could share about out you know, customer success using Hazel Gass? Yeah, that the you know, listeners might be able to attach to you. So, you know, in terms of customer interesting customer use cases. So we've actually got a nice one with BMP puriba where they're actually, you know, looking at what people are doing while they're interacting, say, for example, at an ATM and identifying their account activity and real time and realizing that they are currently interacting with the bank and at that point in time they can identify what kind of lending products that person might be interested in or need at that moment, where, if you offer them the lending products later when they're busy doing other things. You're not making that offer at that moment in time. And so because we can process data in real time continuously, we can also then make that offer while you're banking, which means that you know you're going to get a lending offer that says, Oh, I may be you know, noticed that you're running low on your balances and you could benefit from, you know, some kind of sort of overdraft for protection type of a lending product. Or maybe certain things in your profile indicate that you'd be interested in some kind of you know, mortgage or car loan or something else. And but because when you make it ray at that moment in time, the conversion rate goes up dramatically, like four hundred percent. And so the ability to connect the insights to the action with the customer, that's really the key that this technology is enabling. Another good example of use of this technology is we have a company that is called CGI. They're actually a large, you know, kind of system integration and company that also builds various applications and host them, and they have a instant payments product that allows you to do instant payments that are the cross borders in, you know, in Europe and in Asia, where you may have many different countries and they may have different ways of doing instant payments or peer to peer payments, and this ability to quickly analyze what you're trying to do with the payment and figure out what network it needs to go on based on, you know, who you're trying to make the payment to and what countries and so on. You know, that requires again, this real time processing of data combined with injecting the intelligence into the the use case. Some other interesting use cases that we have. We've got, you know, companies using us to do real time analysis, for example, and warehouses and, you know, looking at all of the data around where boxes are moving, where good are getting packed into boxes and, you know, where orders are coming...

...in and being able to really ensure that efficient operation of a warehouse and provide real time insights to to you know, a smart warehouse system. That's another great use case. I mean it's a like location services probably in general, seems like a great fit for your platform, right, whether it's someone walking around a store and looking at display or, you know, like you said, you know, boxes here, inventory here. It goes to the other side of the warehouse or it gets picked or you know, yeah, exactly. Yeah, any kind of where data is constantly changing and there's an opportunity, but a window of time to take advantage of that opportunity. We call it the real time SLA and that SLA is a business SLA. Write it. It's based on when your customer is interacting or when the people are interacting within the warehouse environment. You know, it can vary from, you know, under a millisecond to seconds or minutes or longer. But you have an SLA with a lot of data and do you need to be able to get insights and then change the behavior of an application with that insight. And that's that's what we call our real time business and that's kind of the types of use cases where we get to apply and Gotcha. Very cool. So, you know, a lot of people in retail leg sticks. We've got, you know, government customers, looking at telemetry and logistics data, including fight fight data. We've had, you know. So it ranges the Gambat of different types of sources of data that we're processing with the platform. Got It. So I would imagine if you've got this, you know your platform seemingly runs anywhere. You probably have a strong alliance with other ISEBES or other hyperscale or you know hyperscalers out there, things like that. You know, Mark, this might be your area of expertise, but can you what can you tell us about some of your alliances at Hazelk ast and you know, organizations that you're working with? Sure, Nick, well, the customers that we have, whether they're large banking and financial institutions or retailers, recommerce solution providers, you know they're looking for solutions and these solutions are, you know, very heavily ecosystem plays. You know, they want their ecosystem of solution providers to work together and deliver the solutions that we're talking about, the deliver competitive advantage. So we have partners like IBM, for example, is our is is our is a global Om partner and we work very closely with their sales and technology leadership teams to identify opportunities to create competitive advantage for our customers together with them and by integrating the IBM cloud packs, for example, with Hazel cast. And we also have, you know, we also partner with obviously, we all know red hat was acquired by by IBM and you know, we make sure that our our platform is, you know, preintegrated and certified to work together, for example, with red hat open shift, and so we have partnerships like IBM Red Hat. We also work with companies like Intel to to make sure that the software in the hardware, we can drive as much efficiency as possible. But then, you know, if we also have other other companies that we that we work together with in the data integration space as well. And you know, our customers are expecting us to work with companies like confluent, Amonga DB and a lot of others. And then, of course, last but not least, arguably, some of the most strategic partners that we are prioritizing our our system integration partners, and this is where we're looking actively grow that ecosystem, because in the end, those are the ones that ultimately will deliver customer success. And so you know, we're working closely with the likes of Ivm Consulting, and then we have other other companies, you know, in the field that we work with like sort lab, MTC and Spain Tech Lever and a pack and others. So and then, of course, you mention the hyperscalars. That's critically important. You know, as we know, the journey of the cloud, we partnerships with the three major hyperscalers Ay to us as are and and Google cloud, and in fact we deploy and our solution on them and we work with them to make sure that the that the platform works together, in the word, delivering as much price performance as we possibly can. Not Jotra. So you're you're everywhere, making yourself available. Yeah, a couple of other partnerships I also wanted to...

...highlight on the technology side, data data stacks and data breaks are are a couple of other key partners for us, and so data stacks, you know, offers. You know, of course Cassandra and pulse are which you know are great places for kind of data to land for a longer periods of time, and of course, pulsars an alternative to Kafka. So we work with both data stacks, with pulsar and with confluent, with with the Kafka Technology, which is where you kind of can move and deliver events or real time messages throughout your enterprise. But you need to be able to then analyze that data, and that's where hazelcast comes into play and and so you know, confil and and data stacks are also key partners for us. Technology. You wise, we also have partnerships across you know, range of other technologies. You know, such as you know, machine learning partnerships, and so data breaks, for example, and data robot and and you know, in other you know, adjacent technology spaces. Another big partner for us is vmwhere and pivotal. So you probably may have be familiar with, you know, pivotals, Spring Framework and of course, Tan Zoo and so you know, we certify, for example, on all of the flavors of Cuber netties that are widely adopted, including Tanzoo, red hat, open shift and also the couber end. He's on the three major cloud providers. But but we also have a strong relationship with, you know, Spring and the whole you know, Java ecosystem that use the spring and then similarly with with red hat, you know corcus and j boss ecosystem of Java users at Red Hat are also an important ecosystem for us. And then Microsoft, not only as a cloud partner but also as a partner for us around dotnet, and that ecosystem is another key kind of partnership. So what we're really trying to do is look at, you know, how do you deliver a solution and, you know, solve a problem and what are those technologies that are involved? And we want to make sure that we have the partnerships in place so that our customers have comfort that, you know, we can work effectively across that ecosystem to make them successful. Duty. Yes, I mean you're really removing the barrier for, you know, a customer to go do I want to use Hazel Cass while you know, I run on this this thing, and it sounds like you guys probably support it. So yeah, I mean makes it easier. Lots of connectors for all the different sort of data sources out there, whether it's things like s three, you know, for or your other cloud stores or, you know, things like, you know, htfs and big data technologies or cou provider technologies like Cannes says and so on. So, you know, we're really trying to make sure that we have the basis covered for our customers. So you'd mentioned your relationship with IBM and you'd mentioned cloud packs. So is Hazel cast a part of one of the embedded tools and like clap pack for data or is it just work well with those technologies? As a ucarisition point? We are certified, as you know, a tested, validated component within the cloud packs. So you know, whether it's cloud pact for Integration or cold pact, you know, for data. You know, we have tested that we can interoperate with components of that platform and provided enablement to IBM technology experts around how we fit into that ecosystem and we're engaged with the solution managers of those cloud packs. IBM has sort of a concept of instead of a product manager, they call it a solution manager or an offering manager, but it's basically a product manager and we're engaged with those guys and have regular calls with them across, you know, the cloud peak Pak ecosystem. And then we also are very committed to, you know, independent strong relationship with red hat, because they are still an independent subsidiary and and have, you know, their own, you know, ecosystem that we want to make sure we play well with, and so we've recently launched a more advanced operator for our COUBERNETTI's that allows you to do more full life cycle types of operations with Hazel cost rather than just trying to kind of deploy and stand up a cluster or add a remove notes from a cluster, but starting to kind of look at, you know, Cougarnetti's is a control plane and what do...

...we need to do to enable people who are using it that way? Gotcha. That was actually going to be my next question, but you you got ahead of me. It was great. So why don't we? Why don't I ask this one then? How do you see Cubernetti's in general changing the way that we process data? Because I think everybody's trying to trying to figure out how do I leverage COUGARNETTI's, and you know this is something we focus on, a shadow soft something customers through that journey. How do you see that from, you know, the aperture of your seat? Yeah, well, it's a great question. We see really COUBERNETTI's kind of emerging as more than just a kind of runtime orchestration but really providing, you know, control plane for managing a lot of capabilities, and what that enables you to do is to really operationally automated your infrastructure so that you can meet the changing needs of your data processing. And so I'll throw out of a few examples. You know, this without naming any names because I don't want to, you know, necessarily divulge too many. Don't get in trouble, be careful because sometimes, you know, some people want us to check with them before they mentioned the right but understood now. So, you know, some large retailers are using this technology and need to be able to scale up at particular peak times of the year and you know, so, obviously black Friday being a key one, but there's there's, you know, other days throughout the holiday shopping period that might be busier, and then there's also other times a year for some retailers. So, for example, in the home improvement industry, we're actually heading into an uplift busy time for you know, home improvement types of retailers where they start to see a lot more people thinking about, you know, Haigh, spring is coming or, if you're here in Atlanta, springs already here. Right. So yeah, so you know, we're already going to those, you know or or Home Depot or one of the other hardware type companies and and planning while are doing this spring in our yards. And that kind of you know, creates another peak. And another example would be, you know, in food delivery. The last weekend was or not last weekend, but the weekend before was quite a busy day. Was the busiest day for food delivery in the US, which is Super Bowl, you know. So people love to just order pizza for the Super Bowl and not cook. So, you know, your busiest day of the year. You need to be able to scale up and then scale back down, and you want to be able to do that without wasting resources and while taking advantage of whether it's a public cloud or a private cloud type of infrastructure. You want to be able to take advantage of the flexibility that that COUBARNETTI's delivers, and to me that means, you know, now you can process data differently, because if I can sort of scale up when I need the extra resources, then you know, that means that I can put more advanced use cases in that might need that additional compute power. It also means that there's other interesting things that we're seeing, which is sometimes, you know, you have spare capacity in some regions or data centers and you know, another thing that Coubernetti's can be leveraged for is spinning up workloads that maybe or are taking advantage of excess capacity when it's not us, particularly if you're in a private con scenario and you know, or maybe you have more of a Dr Environment, public cloud, but you have a cluster that's, you know, more of a standby or or it's in a region where it needs less resources, and one of the things you can do is you could run other workloads. You know, for example, if I'm needing to do retraining of my machine learning, you know, to take advantage of you know, knowing and having the let us insights on more recent data. You know, that's something you could be doing, or other kind of batch processing could be spun up. So to me, Cubernitti's really enables you to kind of maximize the value you're getting out of the infrastructure that you're running on, while also dynamically or responding to and handling those peaks. Now makes sense. The I would like to see cuber duties have an effect on more delivery drivers, as that seems to be the biggest especially in Atlanta. You know, you older food and like yeah, no one's no one's delivering food. Sorry, HMM, you can pick it up. Well, you know, coubernet these can't solve the people's I don't,...

...maybe you can just start making people, you know, on we can dream, right. So yeah, I feel you. So the I always like to ask this and to John to you or mark. What something you've learned along the way? Maybe it's at Hazelt cast, maybe it's in the past, but you know something that maybe is an inflection point in your career where you're like wow, that was meaningful and and I'm going to remember that and I'm going to share that with people. Yeah, it's a great question. You know, one of the I think, inflection point early in my very early in my career that I always come back to was my time at web methods when it was a startup and Philip Marek, the founder, really stressed for us the importance of making customers successful and and when working with customers to make them successful, of doing the right thing. So he had, you know, he really embodied and focused us towards that and you know, he would talk about the golden rule a lot and if you're not really sure what to do when you're working with a customer, think about like how you would want to be treated and think about what's the right thing to do, just regardless of where you work. And may you can customers successful. In that way, I'm really putting them first. It really earns you tremendous loyalty and I think the payback on that is huge and that's something that I've taken with me everywhere in my career is this. You know, if you can make customers successful, you know, the rest sort of unfaults. That's great, John, and I would that would maybe take that a little even further regarding just along that trajectory of you know, this all begins and ends with with the customer. One of the things that I learned was that, and I think this really became a especially apparent when I was at at Oracle, where we had, like, you know, Ninezero, different products from databasis, the data management and analytics and integration. Everything is that customers by simplicity and so, you know, complexity equals risk, equals cost and delays and that's not what they want. So the more that we can you know, what customers really want is to simplify what we know the problems that they have, whether their business or in Ta and ultimately, technical problems are rooted in the technologies. Simplify the bits and bites that we all ultimately are plat products and platforms are based on, but into solutions that really work for them. You know, that's why one of the reasons I'm really excited to work here and Hazel cast lately is, you know, the silplification of of the platform, the two parts of it, the real time bringing together the world of real time with the world of, you know, data as we've known it, which is, you know, people think about data, data being at rest. You know, that's one of the things that that I learned that I think keeps coming back around and I'm reminded of almost every day. No, it's good. I mean, I think the you know, at the end of the day, there's a lot of people out there just, you know, sling and solutions, and that's fine, I guess. If your solutions great, but at the end of the day, you know, I found that it's it's more about, you know, building credibility with a customer and going hey, you know, I have this, I have this thing on my line card. It's not the best thing for you. This actually is. And guess what, I don't sell that or I don't represent that brand. So I agree with both. You know, it's really about the customer, journeying and building, building trust. And you know, some of that has been, you know, I think, taken as kind of a buzzword term. Right, I want to be a trusted advisor the customer. Well, that's great, but what are your actions? Say? Just being a trusted advisor doesn't mean they just open them the check book for You, right, so you got to go earn you get to earn that trust all the time. You know, that's you know, we we sell open source alutions. So for us, every year we have to go compete for the customers business because there's a out of options out there. There's you know, the product has to be performant whatever we've put in front of them. So, you know, when you don't drop a couple million on a perpetual license and you got to earn that every year through a subscription, which I think is really been kind of an innovation and it itself. The way we can assume technology really makes us stay on the edge and work hard for our customers,...

...which I think is pretty cool. Yeah, I would agree with that, I think. You know, it's been exciting to see the open source succeed the way it has and how that's helped really, you know, reward people who are committed to the customer success. And Yeah, it's an interesting point you make around sometimes what you have is not the right fit for what they need and I think it's important to focus. You don't want to stretch yourself and contort yourself to try and solve a use case. And because we are fundamentally set of building blocks or capabilities for building something as a developer or, you know, for a company, that means that you know, you could always just write more code, you know, in the kite right, you know, so we can always say, yeah, we could do that because, because you know, we're at the end of the day, a job of a product and even though you know we also have other language Apis and sequel and everything, but you know, you you could always just write some customer Drava to like bridge whatever is missing and Hanzel cost. And we have to be you know, we always try to make sure we're transparent and this is our direction, this is our strengths and you know, you could use us for fairy his things, but it may not necessarily be the best fit. And that's why it's also important to have a good partner ecosystem, because we can kind of go actually, well, we could help you, but only if we also had, you know, confluent or data stackts or mango or somebody in the architecture, or maybe it's right out, you know. And really I think that, you know, is the to key to kind of really delivering for customers. What's a maybe, what's your guy to Hazel Cass for making that decision right, like, we could do this, we could write some code, we could build this feature. What what do you use internally to go? No, no, this is this is what we do, this is what we're focused on. HMM, you know, this is this is, you know, partnership opportunity. How do you have a guide around that? That's a good question. I think what is important there is to have a strategic vision and direction that is, you know, a multi year direction, so you have that kind of north star guiding you and then you can and you know, ask yourself, does this fit into that direction, this request from this customer? Is it something we were already planning and we just need to move it up in prioritize it, or is it something we hadn't thought of? And what I found most of the time is that, you know, customers ask for things that we're already thinking about doing and maybe haven't done yet, you know, and it's more of a prioritization. But then that that creates also an opportunity to go on with a customer and say, all right, you want, you know, this additional capability within our sequel or this additional capability in terms of how we're, you know, storing and processing data. What's the use case and why do you have that need, and making sure we're capturing that and I think by doing that we're actually making sure our product is solving problems in a meaningful way, whereas when you just build something in a vacuum based on your past experience, it might be a great technology, but it still might not necessarily be a complete solution for what your customers are going to need. And so, you know, I think that that's an important part of it, is to engage your customer and have, you know, that conversation. I think at the end of the day, the other thing is really making sure you have that North Star to find. For us it's real time. That is kind of our our north star. You know, our vision is to enable the world to act on data instantaneously and you know, so that is really for us. You know, we want to be able to act on data anywhere where the data is being born and to be able to act on it as it's being born and rather than having to wait for you to kind of bring it back to a data database or, you know, to something like Kafka and then process it, we want to be able to kind of, you know, act on things as soon as the insights come. Got It. So a is a parting question. I use this a lot on the podcast. A bit of advice to baby and upand coming technologist, someone who's very interested in the field, you know, wants to spend time, you know, spend twenty years doing this like I have. God, God bless them. So you know, what would you what kind of, you know, advice...

...would you give someone like that? Yeah, so I think for people who are, you know, earlier in their careers with technology then myself certainly. You know, I would say number one is find things that you excite you with the technology and work with technologies that you know can kind of get you pumped up, because your energy for learning is much higher if if you can kind of relate and connect to the technology on whatever level. You know, for me, when I was starting my career, I actually had studied economics and so a lot of my computer science work was more around predictive analytics and modeling and simulations related to you know, economic you know sort of projections and forecasting and things like that. We call that data science now, right. Wasn't that their sexy when I went to school? But you know, when I got out, like people who had a statistics and economics background, were parked in front of a screen and just sort of told to kind of crunch numbers all day and and it didn't really appeal to me. And so that's what gravitated me towards be like building applications that solve business problems, because I was attracted to the business problem side of it and, you know, collaborating as a team and that. At the same time, I also liked the whole banking sort of dimension and I think that kind of gravitated me towards electronic transaction processing. So I got very involved with the if working groups that were involved with setting standards for doing secure banking and secure trade over the Internet. Back when you didn't used to be done in the Internet, but it was done electronically and the s and s and then they kind of shifted it to the Internet. And so being involved in that whole kind of movement towards being able to do banking transactions and do that type of thing over the Internet that really, you know, appealed to me. And so I think find something that you like, that you're interested in. For some people maybe it's augmented reality or something like that. For other people maybe it's you know, you know, but like people love online games and stuff and that's a an incredibly fast growth space and technology. Some people are gravitating also towards technologies that are more focused on consumers and how you know, people interactive technologies. Other people like technologies that can be leveraged by business, and so I think that's another dimension to think about earlier in your career, is do you want to be working at like bober and facebook and Google on the you know, BC world, or do you really want to go and work more, you know, on kind of business to business types of use cases? So I think that's another way to kind of think about your career as a developer and technologist. Yeah, it's funny how that has many years ago when I would when I started recruiting talent this space. The there's two tracks. You were a computer science track or you're like video game development. You know, they didn't have a very still I don't think there's a very clear path and education on you know, what you're actually ended, you know, aiming for. I think you did a great job and describing that. You know, do you want to be, you know, a consumer driven technology, or do you want to be a business to business technology? It's almost like our education program shop packaged like that, because I think it'll help people understand these. You know, everybody wants to be a video game developer until they find out it's a grind and most games fail. So it's Um. You know, now we look into this whole augmented reality, you know, the metaverse. You know that that's that's the thing that I'm talking to everybody about. Veline. You know, what is that going to do? In fact, I've had, I think, two conversations on this podcast about lightly around the metaverse. You know, it's like this. You know, it's not even a new concept, it's just new branding. Yeah, it's kind of funny. Yeah, it is. It's kind of interesting. You know this. So there's going to be a whole online world, and it's like, you know, I can remember back at college, like you know, playing various kind of very simple games online, but there were a multiplayer and I can remember, you know, interacting and communities and chat and all kinds of stuff, and it was all a lot of us command line and stuff that people today would be...

...like not seeing us the same thing, but reality was, you know, like or even something like America Online. Myself up for there, you know, yeah, that's where I came up in. It was they well, you could interact with people, you know, in a virtual way and you know, I remember vrml was a big thing too, like now we talked about R and Vr and all this stuff. But you know, I played around a lot with Vur Mel and the S and thought it was really going to be cool and then it just didn't take off at that. So, you know, I think these things kind of get born again and sometimes they're a little too too early. And it seems like a lot of the stuff is really picking up steam now and it's exciting and I think that be another big area for someone right now is do you really want to get into more the VR end of things and or maybe you want to get into more of that metaverse world online, or maybe you want to get into, you know, blockchain and how you can help with processing transactions in a different way. You know, everybody has their their different thing that kind of gets some excited. Yeah, absolutely. And you know, you know, who would have thought that real time processing of data would be important fifteen years ago? Right, as fifteen years ago we didn't have a way to consume that data that quickly as a consumer. But now I walk to my car and I go how long is it going to take me to get to this restaurant I'm getting all that data is there's all this traffic data that's flown into Google maps and it's pretty crazy the amount of data that flows into our iphone or android. It's just out of control. Yeah, it is. And at the same time new technologies are being bored, just born. To simplify it, and that's a big thing that we're focused on is, you know, how do you enable the average developer and the average company to do something cool like what what Google does? Because right, you know, we can all be you know, having their resources of Google and facebook and the like, not all of us are top of the class, myself included, just even from a scale of resources, right, you know, the amount of people that these guys employ and you know, and the infrastructure that they have running and everything. It's it's not a scale that you know, your average company, you know, is going to execute on. And that's why you know tech, I mean let's buy you like Coubernetti's is a great technology because it's making this whole idea of orchestrating containers and managing cloud infrastructure something that, you know, more democratized and and the fact that it's built on open sources another way of further democratizing things and and making it accessible two more people. So that's exciting for me, very cool. Will John Go ahead. Mark, I'm sorry, Nick, I was just gonna you made me think about something when you were talking about is crazy how, you know, years ago, who would have thought of real time data being so, you know, so pervasive and important and everything else. You know, made me think about my first job, but one of my first jobs at least. I was I started my career in the in the navy, and I was a program manager for this component called the shipboard gridlock system. which was part of the age is combat system that took in all the real time data is written in eight and ninety five on a rack mounted two hundred pounds computer that was, you know, could be you know absorb shock and everything else, but it basically it was the thing that created situation, situational awareness for the ages combat system in the context of a battle group. So data coming in create creating a common grid that all of the other information and contacts from different sensors, whether it's other ships, satellites or whatever, could be snapped into a common grid to create that situally situational awareness which, when you think about the higher order mission, that's one of the biggest challenges of any you know, in the military, just understanding what's going on. You know, that notion of situational awareness, being able to evaluate real time information coming in in the context of what's known on the being able to predict what will happen next based on that. And I think a lot of those principles apply to business as well. You know, what we help customers do, whether they're, you know, trying to figure out, you know, garner value and insight from streams of data that are coming in, whether their credit card transactions or, you know, trades. They are trying to figure out what their risk situation is, and so it's I just the history is sometimes repeats itself at Multiple Levels. From for...

...me personally, it certainly has. We come full circle. I find myself here twenty five years later trying to solve similar problems but with much more capable technology. So it's always been an exciting, exciting field. Yeah, I mean, you know, you think about back in the day, if you applied for a credit card, that was not an instant decision. Yeah, it took time. I don't even I don't even remember what it may have been in a paper application for all I know. Yeah, I mean, you know you can even nowadays, you can, you can get a near real time a preapproval, like for for a mortgage. You know, right, that used to be such a pain to kind of figure out how much, you know, you weren't even applying for the actual approval, you're just applying for to find out how much house could you buy. Right, I have a letter or something that you could then take to, you know, people to assure them that you were able to put it in an offer on their house and that was like the first step and now that's like, you know something. You just go on rocket mortgage and put your information in and you know they spit out a result. It says you can go buy a house. We did a refly last year with with rocket. That just you know the whole process was so much more efficient and automated compared to where it was even like when we bought our house originally four years ago. I mean it's it's amazing how much you see improvement in technology in these areas. Just every few years you see some things that didn't used to be real time or are now and didn't used to be automated. You know that are now able to be much more efficient. I'm still waiting for the very automated, simple refinance situation. I haven't had the pleasure yet. It was I will say it was. It wasn't entirely painfully, but it was a lot easier than I had been in the past. I refined my house in two thousand and twenty. That was interesting, you know, meeting someone in a parking lot. You still can't go at a building. We did some something similar. Met Him in a park that the turney walked out to the car in the parking lot. Yep, on paperwork handed back over. Maybe a combination of like RPA plus, you know, some of this blockchain stuff. Maybe you will to help facilitate that. You know, who know, maybe maybe I will. Doesn't matter. All the day it was wrong on my reefly. I had to go chase all that down. Anyway. Process, the inner writing was pretty seamless last year for us. The only facetoface was the final, you know, settlement. That had to be notarized. Right. Everything up until that, you know, and then they just came to our house and we went out front and and just, you know, did all of I think we took a card table and set it up so we could do the closing our front porch. That's that's what we tried to do. But now they wanted US come to an office and do it a parking lot. And it was ninety three degrees in Atlanta office. But we're actually going to just do it in the parking lot. Right, we're going to look back in a few years ago. Why did we do that? That's okay, that's for another time. Well, John Mark, I've held your I've held your calendar probably long enough. I want to thank you so much for coming on the podcast and sharing some ideas and sharing about your company and, you know, hopefully, you know, inspiring some of our listeners to, you know, look into what you guys do it Hazel cast and think differently about the way they're approaching challenges. Yeah, hopefully people will check us out. Anytime you're dealing with needing to make applications faster, more real time or want to inject more intelligence into, you know, whatever you're doing across industries, come check out as a cost, or if you just want to play around with cool technologies that you know support no sequel and streaming and and one run time. You know, it's a great platform to to use and it's open source, so you can just gets started today, or you can also use our manute service and just, you know, fire up a cluster and a few minutes. Very cool. Well, guys, thank you so much again. It's been awesome and I know our listeners will love us. Thanks, thanks for having us. Thanks so much. Now application modernization is sponsored by Red Hat, the world's leading provider of enterprise open source solutions, including high...

...performing Linux, cloud, container and couber Netti's technologies. Thanks for listening to application modernization, a podcast for high growth software companies. Don't forget to subscribe to the show on your favorite podcast player so you never miss an episode, and, if you use apple podcasts, do us a favor and leave a click rating by tapping the stars. Join US on the next episode to learn more about modernizing your infrastructure and applications for growth. Until next time,.

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