David Sztykman Podcast Transcript
David Sztykman joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to Coruzant Technologies, Home of The Digital Executive podcast.
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Welcome to The Digital Executive. Today’s guest is David Sztyman. David Sztyman is a Chief Architect at Hydrolix and has two decades of experience in designing and building complex solutions for streaming content, web development, caching, security observability, and data analytics.
He loves to tackle challenges and is curious to a fault. Prior to joining Hydrolix, David worked at Cloud and security companies, including Acai and Elastic. His roles have included solutions architect, principal solutions engineer, senior technical architect. Technical product manager and now the vice President of product where he leads hydraulics product team in designing and managing the company’s streaming data Lake used for observability security, A IML and real time log analytics.
Well, good afternoon, David. Welcome to the show.
David Sztykman: Thank you. I’m glad to be here.
Brian Thomas: Absolutely, my friend. I appreciate it. And hailing out of Paris, France. Right now I’m in Kansas City, so we’ve got. A little bit of a time zone traverse, but I really appreciate you making the time. So, David, jumping into your first question.
You’ve spent two decades building systems across streaming caching, security and observability. What recurring technical challenges have stayed constant, and which ones have fundamentally changed with the rise of real-time data and AI?
David Sztykman: So, one of the big thing I think that’s constant is really the scale.
Funnily enough, like 20 years ago, the scale of the event that we had was really tiny compared to what we have now. If you think about the first Super Bowl that was Stream online in 2012, it was. A tiny event compared to like Super Bowl events right now. And so one of the theme that’s really is striking is how scaling things is difficult.
Because historically when we work on something, we think that, yeah, this is easy, it’s going to work. But as we have more and more users, as we have more and more data, that’s where it becomes really problematic to ensure that. The system can continue growing properly and respond properly with more and more demands, basically.
And so, yeah, scaling is, I think, the hardest part of anything that we do.
Brian Thomas: Totally understand. And what’s cool about your platform is you do a lot of that. You know, you can scan, petabytes of data in no time, which is really cool. But yeah, scaling events from 20 years ago. Probably a little bit of a challenge back then, but it has massively grown obviously, and as long along with challenges of course.
So, appreciate your, appreciate those insights, David. At Hydrolix, you lead product for a streaming data lake used for observability, security and AI ml. What makes streaming data architecture so critical for modern enterprises compared to traditional data warehouses?
David Sztykman: So, if you think about the use case that Hydrolix is solving, people need to have information in real time about what’s going on.
Technically when you’re watching a video, for example, you don’t want to know that the video had issue half an hour after. You want to know in real time what’s going on to be able to accommodate that for your users. So, one of the challenges, part of, really like big data analysis like that is as the data is growing, typically you usually add delay into ingestion and into analysis of that data.
Because like handling a terabyte a minute of data and handling 10 gigabytes a minute of data is really different, obviously. So, the challenge really is like, how do we keep going down as fast as we can and going as real time as we can with an, an evolving amount of data that’s, that keeps coming. And so that’s really one of the big challenge and why people care about it, is because people really need to see in real time what’s going on at the edge of their platform.
If you think about a security event, you can’t learn about a security incident half an hour after it’s ongoing. If you have a DDoS event, for example, you need to be able to react quickly about it, and the problem is accommodating to a huge amount of logs that are ongoing is difficult. You typically build your infrastructure to manage your daily basis, but when you have something that’s not planned, something that’s massive, how do you scale?
How do you manage that? That influx of new data that’s coming in and how do you do that dynamically is really hard.
Brian Thomas: Absolutely. That is certainly a challenge. Businesses, people need insights in real time today, especially if there’s issues they need that now versus after the fact. As you mentioned, the challenges, the amount of growing data to scan quickly, you know, within minutes or even seconds.
But having a platform that can do this, a technology that’s dynamic and being able to give insights real time is. Really the key here, so I appreciate that.
David Sztykman: Definitely. Yep.
Brian Thomas: David, what role do AI driven pattern detection and anomaly recognition play in helping teams move from reactive troubleshooting to proactive incident prevention?
David Sztykman: So, there’s a lot of things that are ongoing right now in the industry. One of the key things for me is really to understand how AI is helping us. Without breaking the bank, by the way, because if you have a lot of data that’s ongoing and incoming all the time. You can’t really send it to an LLM or to any agent like that all the time because that’s gonna cost a lot of money for that LLM to run, query and analyze that data all the time.
So, what you wanna do is build something that basically allows you to get 90% out of the way. So, you know, that’s the incoming data, that’s the expected format. That’s everything that you expected. But when you have something that’s unexpected, that’s where you leverage an LLM, and that’s where you leverage AI.
And I think really that’s the key part here, is really to build a platform that allows you to accommodate. Most of the use cases and most of the things that you expect, and when you have something that’s unexpected, that’s when you need to leverage AI tooling to be able to analyze that data quickly and figure out if it’s a normal event or if it’s something that needs to be triggered as abnormality.
Really, the idea of sending all your data points to an agent or to an LLM becomes really expensive and slows down the whole process. But if you build something smart enough that manage most of the data that’s incoming, but whenever something doesn’t fit, that’s when you leverage LLM. That really gives you the benefits of both world in the sense that you have your platform that can scale to a massive amount of incoming data that you know and you can manage.
When you have something that you don’t know, that’s when you leverage AI to figure out what it is.
Brian Thomas: Thank you. I really appreciate that. You talked about the key is how AI is helping us with that without that large investment, breaking the bank, as you said. But, again, it’s, it’s all about how we review that data.
You know, like you said, if it’s 90% of it’s structured, formatted, you know what it is. That’s great, but that other 10% when you have some unstructured data maybe, or an anomaly or something is where you said to leverage that data. And that’s where you said
David Sztykman: LLM are really great at because they have such a variety of training data that they can really figure out what it is that it’s abnormal and what it is about that data.
And so, you need to leverage your right tool for the right thing for sure.
Brian Thomas: Absolutely. Thank you. I appreciate that. David. Looking ahead, how do you see AI reshaping the future of observability and security operations over the next three to five years? And what skills will engineers and product leaders need to keep up?
David Sztykman: One thing that’s becoming clear is I think agent are the future of automatic detection and remediation. So, if you think about it, you have a big data platform that contains everything or a lot of the data that you have, but at the end of the day, that data is not being activated or is not being triggered.
And so, if you have agents that are. Querying information about that data and then generating action out of that. It’s really what the solve was supposed to be for security event, where at the end of the day, it would automatically trigger remediation rules. And I think agent to agent communication is really going to enable us to do something similar to that use case where you’re gonna have a huge data platform that’s gonna allow you to query that data.
And have agent query me information about it. And then based on the type of agent and the type of rules that has been configured for it, then they’re gonna generate action directly into different vendors. So, for example, blacklisting and IP on your firewall list or things like that are going to be fully automated at some point through AI communication and agent to agent communication here.
The other big piece of things which is really important to us is how AI is opening data to everybody. Not everyone is a data scientist. Not everyone likes to write SQL queries and the fact that AI assistant allows you to ask question in played English and you can generate a query and get a response out of that is really brilliant.
It’s opening use cases and new things that we don’t even think about. It’s going to open data platform to really users that were not used to get access to that data or used to have like data scientists to work on that with them.
Brian Thomas: Thank you. That’s awesome. Couple things that you highlight or I highlighted here from what you think, the future, obviously agent to agent communication that’s been a big topic recently and, and I definitely see that coming into play.
But the fact that AI is opening data to everyone it’s allowing for, natural language processing, right? Yes. For everyday user to become basically a data scientist, which I think is amazing. So, I appreciate your insights on that and David, it was such a pleasure having you on today and I look forward to speaking with you real soon.
David Sztykman: Nice meeting you and nice talking to you.
Brian Thomas: Bye for now.
David Sztykman Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
