Episode Overview

Podcast: The Digital Executive by Coruzant Technologies
Episode: David Sztykman on Streaming Data, AI, and the Future of Real-Time Observability | Ep 1178
Guest: David Sztykman, Chief Architect & VP of Product, Hydrolix
Air Date: January 7, 2026
Duration: ~10 minutes

This episode dives into the evolution of streaming data, the increasing scale and complexity of real-time observability, and the vital role of AI in anomaly detection and security operations. David Sztykman leverages his decades of experience to outline recurring technical challenges, principles of AI-driven operational intelligence, and predictions for the next wave of data platform advancements.

Key Discussion Points & Insights

1. Persistent and Evolving Technical Challenges

Timestamp: 01:48 – 02:34

• Constant Challenge—Scalability:
  Sztykman reflects on the growth in streaming system scale over the past 20 years. Today’s demands dwarf earlier events, such as the first Super Bowl streamed online.
  • “20 years ago, the scale of the event that we had was really tiny compared to what we have now… scaling is I think the hardest part of anything that we.” — David Sztykman (01:48)
• Growing Complexity:
  Increasing users and data volumes make sustaining performant, responsive systems especially difficult.

2. The Imperative of Streaming Data Architectures

Timestamp: 03:11 – 04:43

• Real-Time Insights are Critical:
  Modern enterprises require immediate visibility into operations—delayed analytics are no longer acceptable.
  • “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.” — David (03:18)
• Dynamic, Unpredictable Load:
  Systems must gracefully handle unexpected spikes (like DDoS attacks or flash events), not just steady-state volumes.
  • “When you have something that's not planned, something that's massive, how do you scale, how do you manage that influx of new data that's coming in? And how do you do that dynamically is really hard.” — David (04:21)

3. AI & Anomaly Detection: From Reactive to Proactive

Timestamp: 05:22 – 07:23

• Preventing ‘Breaking the Bank’:
  Applying large language models (LLMs) or AI to every data point is computationally (and financially) unsustainable.
• Hybrid Approach for Efficiency:
  90% of data should be processed using cost-effective rules and heuristics, reserving expensive AI for the remaining anomalous 10%.
  • “When you have something that's unexpected, that's where you leverage an LLM and that’s where you leverage AI.” — David (05:54)
• Smart Routing to AI:
  • “If you build something smart enough that manages most of the data... but whenever something doesn't fit, that's when you leverage LLM. That really gives you the benefits of both worlds…” — David (06:38)
• Strength of LLMs:
  LLMs excel at interpreting the unexpected, given their diverse training sets.
  • “That's where 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.” — David (07:23)

4. The Future of Observability: AI-Driven Agents & Democratized Data

Timestamp: 07:51 – 09:31

• Rise of Autonomous Agents:
  AI agents will drive automated detection and remediation—actionably querying data and executing responses.
  • “Agents are the future of automatic detection and repudiation... you're going to have a huge data platform that’s going to allow you to query that data and have agent querying information about it. And then... generate action directly into different vendors.” — David (08:01)
  • Example: Automated firewall rule adjustments triggered by AI.
• Agent-to-Agent Communication:
  Agents will increasingly interact, automating multi-system responses to security and operational events.
• Natural Language as Data Interface:
  AI is simplifying data access for non-experts; natural language queries enable anyone to gain insights without specialized training.
  • “AI assistant allows you to ask question in plain 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.” — David (09:15)

Notable Quotes & Memorable Moments

• “Scaling is I think the hardest part of anything that we.”
  — David Sztykman (01:55)

• “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.”
  — David Sztykman (03:18)

• “You can't really send it to an LLM or to any agent like that all the time because that’s going to cost a lot of money for that LLM to run, query and analyze that data all the time... when you have something that's unexpected, that's where you leverage an LLM and that’s where you leverage AI.”
  — David Sztykman (05:33–05:54)

• “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 plain English and you can generate a query and get a response out of that is really brilliant.”
  — David Sztykman (09:09)

Key Timestamps

• 01:48 — The challenge of scaling streaming architectures
• 03:18 — Real-time data needs: user experience and incident response
• 05:33 — Cost and strategy for AI-driven monitoring
• 06:38 — Hybrid design: rules for the known, LLMs for the unknown
• 08:01 — The future: autonomous agent-driven detection and resolution
• 09:09 — AI enabling natural language access to data

Flow & Tone

David Sztykman covers complex technical ground with practical, conversational clarity. He emphasizes the sometimes-overlooked operational costs of AI, the critical importance of fast responses, and exciting AI-powered directions for the field—particularly the democratization of analytics via natural language interfaces. The host’s questions keep the discussion focused and accessible, delivering actionable insights for technologists and executives alike.

For New Listeners

This episode is a succinct, insightful primer on the evolution of streaming data platforms, real-time observability, and the imminent transformation AI is bringing to security and data operations. You’ll come away with a clear understanding of the perennial technical pains, emerging AI-powered solutions, and how future platforms will empower a broader array of users—well beyond data scientists.