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Join us at our first event of the year at The Information Lab on the historic Watling Street in the City of London 🙌 We will be kicking off 2026 by delving into the topic of real-time data with our speakers - Sam, Nicoleta & Anton. We are running this event in collaboration with Confluent.

6pm: Doors Open

6:30pm: Talks Start

🗣️The Speakers🗣️

Load-In to Lights-Out: Data Engineering the World's Biggest Tours and Live Events Sam Malcolm, Head of Architecture & Engineering at Centrus (Sam's Linkedin) Sam’s session dives into lessons from large-scale live event data systems—handling over 10 billion data points per second for global tours like Beyoncé, Coldplay, and Glastonbury. He connects the extreme demands of real-time analytics and high-performance networking to modern cloud data practices, showing how the same principles of speed, resilience, and precision apply when designing reliable, scalable data platforms today.

Should I Stream or Should I Join: From Regular to Delta Joins in Apache Flink Nicoleta Lazar, Senior Data Engineer at Fresha & Anton Borisov, Principal Engineer at Fresha (Niloceta's LinkedIn , Anton's LinkedIn) Joins in the streaming world are where the fun stops and the tradeoffs start. State that grows forever, latency that spikes unpredictably, watermarks that never quite behave, every Flink developer has war stories about this. In this session, Anton Borisov and Nicoleta Lazar break down the join landscape in Apache Flink:

→ Regular joins and the state explosion problem → Interval joins: when they work, when they don't → Temporal joins and the versioned table dance → Lookup joins: the escape hatch and its hidden costs → Delta joins: the new kid and how Fluss enables them, and why it matters

Talks finish by 8pm and there will be a break between the talks. Afterwards, we may head to a pub to continue chatting.

You can sign up by subscribing to this event

🚨IMPORTANT: Please bring a valid form of ID.

See you all on the 22nd January 🤩

Happy Networking 🍻

Checkout Meetup Groups run by Confluent:

• https://www.meetup.com/london-real-time-data-meetup/
• https://www.meetup.com/data-streaming-europe/

By attending this event, you agree to abide by our rules of conduct:

• Respect others' opinions.
• Keep it appropriate - no harassment of any sort.
• If you see something or have a complaint, please reach out to one of the organisers or email [email protected].

IMPORTANT: PLEASE RSVP @ https://www.meetup.com/data-engineers-london/events/312450363/

Details 6pm: Doors Open

6:30pm: Talks Start

🗣️The Speakers🗣️

Load-In to Lights-Out: Data Engineering the World's Biggest Tours and Live Events Sam Malcolm, Head of Architecture & Engineering at Centrus (Sam's Linkedin) Sam’s session dives into lessons from large-scale live event data systems—handling over 10 billion data points per second for global tours like Beyoncé, Coldplay, and Glastonbury. He connects the extreme demands of real-time analytics and high-performance networking to modern cloud data practices, showing how the same principles of speed, resilience, and precision apply when designing reliable, scalable data platforms today.

Should I Stream or Should I Join: From Regular to Delta Joins in Apache Flink Nicoleta Lazar, Senior Data Engineer at Fresha & Anton Borisov, Principal Engineer at Fresha (Niloceta's LinkedIn , Anton's LinkedIn) Joins in the streaming world are where the fun stops and the tradeoffs start. State that grows forever, latency that spikes unpredictably, watermarks that never quite behave, every Flink developer has war stories about this. In this session, Anton Borisov and Nicoleta Lazar break down the join landscape in Apache Flink:

→ Regular joins and the state explosion problem → Interval joins: when they work, when they don't → Temporal joins and the versioned table dance → Lookup joins: the escape hatch and its hidden costs → Delta joins: the new kid and how Fluss enables them, and why it matters

Talks finish by 8pm and there will be a break between the talks. Afterwards, we may head to a pub to continue chatting.

*** If you are interested in speaking at or hosting a meetup, please reach out to [email protected]

IMPORTANT: PLEASE RSVP @ https://www.meetup.com/data-engineers-london/events/312450363/

Details 6pm: Doors Open

6:30pm: Talks Start

🗣️The Speakers🗣️

Load-In to Lights-Out: Data Engineering the World's Biggest Tours and Live Events Sam Malcolm, Head of Architecture & Engineering at Centrus (Sam's Linkedin) Sam’s session dives into lessons from large-scale live event data systems—handling over 10 billion data points per second for global tours like Beyoncé, Coldplay, and Glastonbury. He connects the extreme demands of real-time analytics and high-performance networking to modern cloud data practices, showing how the same principles of speed, resilience, and precision apply when designing reliable, scalable data platforms today.

Should I Stream or Should I Join: From Regular to Delta Joins in Apache Flink Nicoleta Lazar, Senior Data Engineer at Fresha & Anton Borisov, Principal Engineer at Fresha (Niloceta's LinkedIn , Anton's LinkedIn) Joins in the streaming world are where the fun stops and the tradeoffs start. State that grows forever, latency that spikes unpredictably, watermarks that never quite behave, every Flink developer has war stories about this. In this session, Anton Borisov and Nicoleta Lazar break down the join landscape in Apache Flink:

→ Regular joins and the state explosion problem → Interval joins: when they work, when they don't → Temporal joins and the versioned table dance → Lookup joins: the escape hatch and its hidden costs → Delta joins: the new kid and how Fluss enables them, and why it matters

Talks finish by 8pm and there will be a break between the talks. Afterwards, we may head to a pub to continue chatting.

*** If you are interested in speaking at or hosting a meetup, please reach out to [email protected]

While upgrading Flink to its latest versions to enable more AI-related capabilities, one can easily run into tricky savepoint incompatibilities that render existing state snapshots unusable for recovery. This is especially problematic in the case of pipelines with large state. In such cases, doing a backfill can take too long and using the State Processor API leads to downtime or breaking the exactly-once delivery guarantee.

In this talk, I’ll share a state migration pattern that I applied to one of our Flink jobs using regular streaming mode. It involves creating a new stateful operator that conforms to the new requirements, allowing for compatible savepoint creation. Leveraging side outputs and custom key traversal the existing state is forwarded to the new operator. In the meantime, regular processing is uninterrupted.

We’ll explore the core problem and understand the pitfalls and trade-offs of existing solutions such as the State Processor API. Then, a deep-dive into the migration pattern will follow: ensuring correct state handoff between operator versions, setting up triggers to migrate all keys and other technicalities. Lastly, a few words about cleaning up seamlessly. With this session I will add a nice pattern to your toolbox that you can easily apply next time you run into state migration challenges.

Talk by Celeste Hogan, Developer Advocate at Snowflake.