Real-Time Data Integration Patterns for Enterprise Systems
Architectural patterns for real-time data integration in enterprise environments. Covers event-driven architecture, change data capture, streaming platforms, and the trade-offs between consistency and latency.
When Batch Is Not Fast Enough
Traditional enterprise integration relies on batch processing: scheduled jobs that extract, transform, and load data between systems at hourly, daily, or weekly intervals. For many use cases this is perfectly adequate. But an increasing number of enterprise scenarios demand real-time or near-real-time data integration: fraud detection that cannot wait for a nightly batch, inventory visibility that must reflect the latest transaction, or operational dashboards that show current state rather than yesterday's snapshot.
Real-time integration architectures use fundamentally different patterns: event-driven messaging where systems publish events as they occur, change data capture (CDC) that streams database changes to downstream consumers, and streaming platforms that process data in continuous flows rather than discrete batches. Each pattern has different characteristics in terms of latency, ordering guarantees, exactly-once delivery, and operational complexity.
The critical decision is not whether to use real-time integration but where. Not every data flow justifies the additional complexity of event-driven architecture. At redskios, we help enterprises identify which integration points genuinely benefit from real-time capability and design hybrid architectures that use streaming where it matters and batch where it is sufficient — pragmatic engineering rather than architectural purism.