System Integration topics for business projects
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System integration for SMEs: ERP connection, middleware and API architecture
System integration is often the biggest IT infrastructure problem in mature mid-sized companies: ERP, CRM, web shop, production systems, logistics tools and external partners communicate via manual exports, fragile point-to-point connections or not at all. This page clarifies architecture decisions and integration strategies before commissioning.
The costs of poor integration are hidden: manual data transfers, errors from duplicates, delayed order processing, support effort from inconsistent data states. A clean integration solution is not an IT luxury but directly measurable in process costs and error rates.
Integration architecture decisions – direct API coupling, event streaming (Kafka, RabbitMQ), ESB/iPaaS platforms (MuleSoft, Azure Integration Services, Zapier) or custom middleware – depend on system count, data volume, real-time requirements and available IT resources.
Integration architecture: point-to-point, hub-and-spoke and event-driven
Point-to-point integration (direct coupling of two systems) is fast and pragmatic for few integrations; beyond three to four systems, an unmanageable integration mesh emerges with no clear data ownership. Complexity scales quadratically with system count.
Hub-and-spoke via an integration platform (iPaaS, ESB) centralises transformation logic, monitoring and error management. A middleware layer increases initial complexity but substantially reduces total cost as the system landscape grows.
Event-driven integration (Apache Kafka, RabbitMQ, Azure Event Hub) makes sense for high message volume, asynchronous processes and decoupling requirements between producer and consumer. For synchronous real-time queries (e.g. stock lookup in a shop), classic REST API integration is often sufficient.
API design, data transformation and operations
Well-specified APIs (OpenAPI/Swagger) are the foundation of stable integration: versioning, error codes, pagination and rate limiting are standard features, not special requests. APIs without these properties are hard to integrate and even harder to debug.
Data transformation – mapping different data models between systems – is often the most demanding part of an integration. Explicit mapping tables, canonical data models and validation layers prevent silent data errors that surface weeks after go-live.
Integration monitoring: dead-letter queues for failed messages, alerting on latency degradation, regular end-to-end consistency checks and clear reprocessing procedures for failed transactions are operational prerequisites, not nice-to-haves.
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