Integration Patterns: How To Choose for Your Architecture

Overview

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

Integration patterns fall into two groups: data patterns that govern how information moves between systems, and enterprise patterns that describe how applications talk to each other.
Most production setups run two or three patterns side by side, such as real-time broadcast for new signups, overnight ELT for analytics, and scheduled two-way syncs.
The guide gives five questions to pick a pattern: direction, latency, scale, volume, and cadence.
Simple point-to-point links work for a few connections but turn brittle as integrations multiply, which is why a central broker or event-driven design takes over at larger scale.
ETL transforms data before loading it, while ELT loads raw data and transforms it inside the warehouse, a fit for cloud platforms such as Snowflake, BigQuery, and Databricks.
Deciding patterns up front costs far less than reworking the plumbing once an organization grows from a handful of endpoints to a dozen or more.

Stats & Key Facts

#About a dozen integration patterns are covered across two groups, data patterns and enterprise patterns.
#Five selection questions guide the choice: direction, latency, scale, volume, and cadence.
#Six data patterns are detailed: ETL/ELT, migration, broadcast, bidirectional sync, change data capture, and aggregation.
#Six enterprise patterns are detailed: point-to-point, hub-and-spoke, event-driven, publish-subscribe, API-led connectivity, and saga.
#Pipeline volume ranges from a few thousand records a day to millions of records daily, a key factor in pattern choice.
#Two or three patterns typically run at the same time in a single production architecture.

Integration Patterns: How To Choose for Your Architecture

Data Patterns Versus Enterprise Patterns: The Two Buckets

The guide sorts every approach into two families that answer different questions.

›Data integration patterns govern how information moves between databases, warehouses, and SaaS apps.
›Enterprise integration patterns describe how applications communicate and stay coordinated.
›Knowing which family a problem belongs to narrows the field before any tool gets picked.

Data patterns focus on records and pipelines, the plumbing that gets a customer table from one place to another. Enterprise patterns focus on messaging and coordination between running applications. A real architecture usually needs both, which is one reason teams end up combining several patterns.

Six Data Patterns: ETL, ELT, Migration, Broadcast, Sync, CDC, and Aggregation

These patterns move records between systems with different timing and transformation rules.

›ETL transforms data before loading, useful when the destination database has strict rules or needs sensitive fields scrubbed first.
›ELT loads raw data and transforms it inside the warehouse, a fit for cloud platforms such as Snowflake, BigQuery, and Databricks.
›Migration is a one-time bulk move, used when retiring a legacy system or consolidating platforms.
›Broadcast pushes one record to many targets in near real time, so a new signup reaches the CRM, billing, and analytics at once.
›Bidirectional sync keeps two or more platforms, such as HubSpot and Salesforce, updated in both directions.
›Change data capture streams only the changes from a database, more efficient than scanning everything on a schedule, with tools like Debezium.

Six Enterprise Patterns: From Point-to-Point to Saga

These patterns describe the wiring and messaging between applications.

›Point-to-point connects System A directly to System B, simple but brittle once links multiply.
›Hub-and-spoke routes everything through a central broker that translates between systems, which removes tangle but creates a single point of failure.
›Event-driven designs let systems react to published events without polling, trading easy tracing for lower latency.
›Publish-subscribe sends messages to a topic that independent subscribers consume, built on technologies like Kafka, SNS, and Google Pub/Sub.
›API-led connectivity organizes APIs into system, process, and experience layers, a discipline that suits large organizations more than small teams.
›The saga pattern coordinates a transaction spanning several services using compensating actions instead of simple rollbacks.

Why Point-to-Point Breaks Down as You Grow

Direct links are the natural starting point, and also the first thing to fail at scale.

Connecting two systems directly is the easiest place to begin, and it works fine for a small number of integrations. The trouble is that connections multiply faster than the systems do, and a single API change starts breaking several integrations at once. This is the point where a central broker, in the hub-and-spoke model, or an event-driven approach earns its keep by decoupling the senders from the receivers.

Five Questions That Point You to the Right Pattern

Rather than memorizing every pattern, the guide says to answer five questions about the job.

›Direction: does the data flow one way, or do both sides need to update each other?
›Latency: do changes need to propagate in seconds, or is a nightly refresh fine?
›Scale: are you wiring a handful of endpoints or a dozen-plus connections?
›Volume: are you moving thousands of records a day or millions?
›Cadence: is this a one-time cutover or an ongoing pipeline?

Why Real Systems Run Several Patterns at Once

The guide's main lesson is that single-pattern solutions rarely survive growth.

A typical production setup runs two or three patterns together: real-time broadcast for new signups, an overnight ELT job feeding the analytics warehouse, and scheduled two-way syncs keeping CRM records aligned. Each pattern handles a job the others do poorly, so mixing them is the norm rather than a compromise. The practical advice is to match patterns to direction, timing, and scale at the start, because rebuilding the integration layer after the fact is far more expensive than choosing well early.

Frequently Asked Questions

What is the difference between ETL and ELT?

ETL transforms data before loading it into the destination, which suits databases with strict rules or sensitive fields to scrub. ELT loads raw data first and transforms it inside the warehouse, a fit for cloud platforms such as Snowflake, BigQuery, and Databricks.

How do I choose an integration pattern?

The guide recommends answering five questions: the direction of data flow, the acceptable latency, the number of endpoints, the daily record volume, and whether the job is one-time or ongoing. The answers point you toward the patterns that fit.

Why does point-to-point integration stop working as a business grows?

Direct links work for a few connections, but the number of connections multiplies as systems are added, and a single API change starts breaking several integrations at once. At that scale a central broker or an event-driven design becomes the better choice.

Do I have to pick only one integration pattern?

No. The guide stresses that production architectures almost always run two or three patterns at the same time, since each pattern handles a job the others do poorly. Combining them is standard practice.

What is change data capture used for?

Change data capture streams only the changes made to a database rather than rescanning everything on a schedule, keeping downstream systems like analytics warehouses current more efficiently. Tools such as Debezium support this approach.

Picking integration patterns deliberately, guided by direction, latency, scale, volume, and cadence, keeps a business flexible as it grows. The honest takeaway is that one pattern is rarely enough, so plan for a mix from the start.

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