Matthew Darwin

In real-time markets, stale data is expensive. By the time data moves through a traditional batch pipeline, the trade has changed, the fraud has happened, or the opportunity is gone. This session explores how organizations are rethinking modern data architectures to support real-time operational and AI workloads. Using the example of streaming FX market data, we’ll show how live data moves from Kafka streams to applications and GenAI systems in milliseconds. This enables everything from real-time decision-making to AI agents that reason on fresh operational context instead of stale warehouse snapshots. You’ll see how Confluent and SingleStore work together to power this new class of applications, combining streaming ingestion, low-latency serving, vector search, and SQL in a single real-time architecture. Along the way, we’ll demonstrate GenAI operating directly on live FX data as it streams in — answering questions and reasoning on data it has never seen before. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.