When to Choose Hybrid vs Pure Batch or Stream

The Decision Framework: Choosing between hybrid, pure batch, or pure stream isn't about picking the most sophisticated architecture. It's about matching your system design to your actual requirements and constraints. Choose Hybrid When: You have business critical use cases demanding both low latency and high correctness from the same underlying data. For example, an ads platform needs 500 millisecond p99 budget pacing and fraud detection, but also requires 100 percent accurate billing and regulatory reporting over months of history. Additionally, your data volume is large enough, typically tens of terabytes per day or more, that recomputing everything as a stream becomes either prohibitively complex or expensive. At this scale, dedicated batch jobs running during off-peak hours on cheaper compute are more economical. Finally, you have strong backfill and audit requirements. Financial systems, healthcare records, and compliance driven industries need the ability to reprocess historical data with bug fixes and apply retroactive corrections with full audit trails. Stick with Pure Batch When: Your latency tolerance is 1 hour or more across all use cases. Purely analytical workloads like daily business intelligence dashboards, weekly cohort analysis, or monthly financial reports don't benefit from real-time complexity. Your queries require full table scans or complex multi way joins across historical data. Batch systems excel at these patterns with cost per query often 10x to 100x lower than maintaining equivalent streaming state. Stick with Pure Stream When: You're building real-time control loops with limited historical depth. Simple online scoring, basic monitoring and alerting, or session based features that only need minutes or hours of context don't require batch recomputation. Your correctness requirements are relaxed. Social media feeds, recommendation systems, and many analytics dashboards can tolerate approximate counts or occasional inconsistencies in exchange for subsecond freshness. The Cost Equation: Hybrid systems have concrete cost implications. You pay for streaming infrastructure provisioned for peak real-time load, batch compute for periodic reprocessing, storage for both raw events and materialized views, and the operational overhead of merge logic and reconciliation monitoring. For a system processing 50 terabytes daily, pure batch might cost $20,000 monthly in compute and storage. Pure stream adds real-time infrastructure for perhaps $45,000. Hybrid requires both plus serving layer, reconciliation tools, and increased operational burden, reaching $75,000 or more. The Organizational Factor: Hybrid architectures require organizational maturity. You need teams capable of maintaining separate batch and streaming code paths without letting them diverge. You need robust schema governance to prevent breaking changes. You need Service Level Objectives (SLOs) that explicitly define cutover times and acceptable reconciliation lag. If your team is small or your product is early stage, start simple. A pure batch system that runs every 15 minutes often beats a poorly implemented hybrid system that's always inconsistent.