5 Data Integration Patterns Every Business Should Know
Master the fundamental data integration patterns: point-to-point, hub-and-spoke, publish-subscribe, ETL, and event-driven architectures.
Point-to-point vs hub-and-spoke
Point-to-point integrations are quick to ship but scale poorly: every new system adds N−1 connections. A hub (often your warehouse or iPaaS) centralizes mappings and reduces operational sprawl.
Use point-to-point only for low-volume, stable links; plan a hub when three or more systems exchange the same entities.
Events, ETL, and hybrid flows
Publish-subscribe (e.g. Kafka, SNS) fits real-time reactions—inventory updates, fraud signals—while batch ETL remains ideal for historical analytics and large backfills.
n8n shines at orchestrating hybrid paths: listen to a webhook or queue, transform, then load Snowflake and notify Slack on failure.
Choosing a pattern
Match latency, ordering, and replay requirements. If you must never drop messages, invest in durable queues and dead-letter handling up front.
Document data contracts (schemas, SLAs) so analytics and ops teams trust the same definitions across workflows.
Written by Devma Labs
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