Streaming vs. Batch Analytics: Why Trading Firms Need Both

For years, the data engineering world has debated streaming versus batch processing as though they were competing paradigms. In trading, this debate has practical consequences: batch pipelines are great for end-of-day analytics, backtesting, and regulatory reporting, while streaming pipelines are essential for real-time signal generation, surveillance, and intraday risk. Most firms end up building both — and maintaining two separate stacks is expensive and error-prone.

At ShoalFlow, we designed a unified architecture from the start. Our core abstraction is the "flow" — a declarative pipeline definition that specifies data sources, transformations, and sinks. A flow can be evaluated in streaming mode (processing events as they arrive) or batch mode (processing a bounded dataset from storage) using the same logic, the same code, and the same semantics. This duality means that a signal developed in a research notebook against historical data can be promoted to a streaming production pipeline with zero rewriting.

The technical foundation is an event log that serves as both the streaming transport and the historical store. Every event — market ticks, alternative data points, derived signals — is appended to a durable, partitioned log. Streaming consumers read from the tail; batch consumers read from arbitrary offsets. Because the log is the single source of truth, there is no reconciliation problem between the two modes. Clients who have adopted this architecture report a 40% reduction in data engineering headcount and a 60% improvement in time-to-production for new signals.