Ramzi Alashabi

Most data platforms still think in batches. Daily jobs, hourly micro-batches, and carefully tuned schedules dominate, even in organizations already running Kafka. The result is familiar: complex backfills, fragile dependencies, hard-to-debug pipelines, and endless debates about whether “real-time” is worth the cost and complexity. This talk argues that the real problem isn’t batch versus streaming, it’s the mindset behind them. Batch and streaming are not fundamentally different architectures. They are the same model operating at different speeds. When teams design pipelines around intervals instead of events, they lock themselves into unnecessary complexity and make future change expensive. By contrast, designing workloads as streams first allows processing speed to become a configuration choice rather than an architectural constraint. In this session, we explore how to transition batch workloads by shifting to stream-first, event-based thinking without committing to always-on, low-latency systems on day one. We’ll show how to model data as a sequence of events, reason about state and correctness over time, and decouple business logic from scheduling. From there, the same…

As we move into 2026, the financial services industry has reached a tipping point. The "batch-processing" era is officially a legacy constraint, and the ability to act on data in motion has become the primary differentiator for market leaders. We will explore how the emergence of the Data Streaming Engineer and the rise of Real-Time AI are reshaping everything from fraud prevention to customer experience. This session moves beyond the "why" of digital transformation and dives into the "how" of building a real-time "central nervous system" for your organization. The FSI Luncheon is open to all Current London 2026 attendees. Lunch will be provided. No registration required.