Integration Patterns: How To Choose for Your Architecture
An n8n engineering guide lays out about a dozen integration patterns for moving data and events between business systems, split into data patterns and enterprise patterns. The central point is that real production systems almost always run two or three patterns at once, so choosing them deliberately up front saves a costly rebuild later. The guide offers five plain questions to narrow the choice: direction, latency, scale, volume, and cadence.
Key Takeaways
- Explore the most common integration patterns for data and enterprise systems.
Learn their uses, key tradeoffs, and ways to combine them in production.
- In this guide, we cover common integration patterns and the ways they fit into daily work.
Integration patterns every technical team should know Every integration decision has two layers.
- ETL and ELT The distinction matters less than it used to, but it hasn't disappeared.
Cloud warehouses like Snowflake, BigQuery, and Databricks handle transformation efficiently at the storage layer, so ELT has become the default for analytics workloads - load raw, transform in the warehouse.
- That record needs to land in the CRM, the billing system, and the analytics warehouse, ideally within seconds.
Broadcast is one-way and asynchronous, which means the source has no idea whether downstream systems accepted the data.
- Tools like Debezium capture changes directly from the database transaction log.
Explore the most common integration patterns for data and enterprise systems. Learn their uses, key tradeoffs, and ways to combine them in production. Integration patterns describe how data and events move between systems.
They help teams scale operations, handle increasing workloads, and adapt to new processes. In practice, many teams don't choose them but inherit from earlier decisions. A scheduled SQL script becomes the company's ETL strategy.
A webhook someone set up late at night becomes the only link between two platforms. By the time anyone notices, Slack messages and tribal knowledge hold your architecture together. This system isn't reliable or repeatable, and it isn't sustainable under growth.
For more details please read the original article at n8n Blog.
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