What is Hybrid Batch-Stream Processing?

The Core Problem: Real systems have conflicting requirements. Your fraud detection team needs alerts in under 200 milliseconds. Your finance team needs monthly revenue reports accurate to the cent over petabytes of history. Your product analytics team wants dashboards updated every 5 minutes. Pure batch processing gives you perfect accuracy but hour or day level latency. Pure stream processing gives you subsecond latency but makes it hard to guarantee correctness over complex joins or long histories. How Hybrid Solves This: You run two parallel processing paths on the same raw data. The streaming path handles events in near real time, maintaining rolling windows and incremental aggregates. The batch path periodically recomputes complete, authoritative results by scanning all historical data. A serving layer then merges both views. When someone queries for metrics, they get batch computed data for older time periods plus streaming computed data for recent minutes. To them, it appears as one unified dataset. Three Main Architectural Patterns: First, Lambda architecture uses separate batch and stream pipelines that converge in a serving layer. Batch recomputes the full dataset periodically, stream provides real-time deltas. Second, Kappa architecture takes a streaming first approach where batch processing is implemented by replaying the event log from the beginning. Third, unified engines treat both batch and stream as variants of the same computation model, often using lakehouse style storage with time travel and incremental updates.