Batch data processing has served enterprises well for decades, and for many use cases—monthly financial close, historical trend reporting—it remains entirely appropriate. But a growing set of use cases genuinely require data to be acted upon within seconds of being generated: fraud detection that must flag a transaction before it settles, inventory systems that must reflect a sale the instant it happens, and operational alerting that loses most of its value if it arrives an hour late.
Streaming is an architecture commitment, not a feature toggle
Moving from batch to streaming is not simply running the same pipeline more frequently; it requires rethinking data models for continuous rather than periodic processing, handling out-of-order and late-arriving events gracefully, and building monitoring that can detect a stalled stream within minutes rather than discovering it during the next batch reconciliation. Organizations that underestimate this shift often end up with a fragile streaming pipeline that behaves like a batch job running every few minutes, without the actual latency benefits.
Not every use case needs streaming, and a disciplined migration starts by identifying the specific decisions genuinely blocked by batch latency, then building streaming capability around those first rather than converting the entire data estate at once.
JIG helps organizations design streaming data architectures for the use cases that truly need them, without over-engineering the rest of the platform.