IN PERSON! Apache Kafka® x Apache Flink® x Apache Iceberg® Meetup - Mar 2026

Hello everyone! Join us for an Apache Kafka® x Apache Flink® x Apache Iceberg® meetup on Mar 18th from 5:30PM, hosted by intelia in Melbourne!

The address, agenda, and speaker information can be found below. See you there!

Venue:
intelia
L3, 31 Queen St, Melbourne 3000

IMPORTANT:
Please note that for security purposes, all attendees are required to sign in at the lobby to get into the building.

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Agenda:

• 5:30pm: Doors open
• 5:30pm - 6:00pm: Pizza, Drinks, and Networking
• 6:00pm - 6:30pm: Olena Kutsenko, Staff Developer Advocate, Confluent
• 6:30pm - 7:00pm: Paras Sitoula, Technical Lead, Tabcorp
• 7:00pm - 7:30pm: Additional Networking

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Speaker:
Olena Kutsenko, Staff Developer Advocate, Confluent

Talk:
Keeping data private in real-time pipelines

Abstract:
We all love real-time data — clicks, payments, rides, messages — but most of it comes with a catch: it contains personal information we're not supposed to leak, such as names, emails, locations, or even small clues that can identify someone. The challenge: how do we keep streaming data useful and safe at the same time? In this talk, we'll explore practical ways to protect privacy in streaming systems using Apache Kafka, Apache Flink, and Apache Iceberg. We'll cover: - simple tricks like masking and tokenizing PII; - why "anonymous" data often isn't anonymous (the re-identification problem); - techniques like bucketing, k-anonymity, and adding noise; - how to balance privacy with data utility (too much hiding makes data useless). Along the way, we'll look at real-world stories: from public data leaks to surprising deanonymization attacks, and show live demos of pipelines that anonymize data before it's written to storage. If you've ever wondered how to build privacy-aware pipelines, this talk will give you practical patterns you can use right away.

Bio:
Olena is a Staff Developer Advocate at Confluent and a recognized expert in data streaming and analytics. With two decades of experience in software engineering, she has built mission-critical applications, led high-performing teams, and driven large-scale technology adoption at industry leaders like Nokia, HERE Technologies, AWS, and Aiven.
A passionate advocate for real-time data processing and AI-driven applications, Olena empowers developers and organizations to use the power of streaming data. She is an AWS Community Builder, a dedicated mentor, and a volunteer instructor at a nonprofit tech school, helping to shape the next generation of engineers.
As an international speaker and thought leader, Olena regularly presents at top global conferences, sharing deep technical insights and hands-on expertise. Whether through her talks, workshops, or content, she is committed to making complex technologies accessible and inspiring innovation in the developer community.

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Speaker:
Paras Sitoula, Technical Lead, Tabcorp

Talk:
Building an AI Agent on Kafka — Architectural Realities Beyond the Demo

Abstract:
Large Language Models make it deceptively easy to build an “AI agent”: consume an event, call a model, produce a decision. But when this pattern is introduced into a high-throughput streaming system built on Apache Kafka, the architectural assumptions begin to shift.

Kafka is optimized for predictable scaling, low latency, and deterministic event processing. LLM-based agents, by contrast, introduce seconds-level latency, per-call cost, rate limits, and non-deterministic outputs. What appears straightforward in a demo can quickly create consumer lag, cost escalation, replay inconsistencies, and new operational failure modes in production.

In this session, we will:

• Build a simple event-driven AI agent on Kafka
• Examine the architectural tension between streaming systems and LLM workloads
• Explore the impact of latency, cost-per-event, backpressure, and rate limiting
• Discuss how non-determinism affects replay and auditability
• Present practical design patterns to isolate and control AI workloads in streaming environments

Rather than focusing on prompt engineering, this talk takes a systems-design perspective. It is aimed at engineers and architects who are experimenting with AI in real-time platforms and want to understand the production trade-offs before deploying at scale.

Bio
Paras is a seasoned software/date engineer with significant experience in developing and optimizing realtime scalable systems. At Tabcorp, Paras leads a high-performing team, developing and optimizing real-time transaction monitoring systems utilizing technologies like Apache Kafka and Neo4j. With a robust background in both real-time and batch data processing, Paras has extensive experience across various platforms and tools, including AWS, Databricks, and Spark, which he leverages to deliver high-performance data solutions.

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If you would like to speak or host our next event please let us know! community@confluent.io