System Integration Patterns: Connecting Your Business Software

Your ERP doesn't talk to your CRM. Your e-commerce platform doesn't sync with inventory. Your operations and accounting are different universes.

Here's how to connect everything.

Why Integration Matters

The Cost of Disconnection

• Double entry: Same data entered multiple places
• Inconsistency: Different systems, different "truths"
• Delays: Waiting for data to move manually
• Errors: Human transcription introduces mistakes
• Blind spots: No unified view of the business

The Integration Promise

• Single source of truth: Data entered once, available everywhere
• Real-time visibility: Changes propagate immediately
• Automation: Processes trigger automatically
• Accuracy: No manual transcription errors
• Efficiency: People do work, not data shuttling

Integration Patterns

1. Point-to-Point

Direct connection between two systems.

System A ←→ System B

Pros:

• Simple to implement
• Direct, fast communication

Cons:

• Doesn't scale (n systems = n² connections)
• Changes to one system affect the connection
• No central visibility

Best for: Single integration between two stable systems

2. Hub and Spoke (Integration Platform)

Central hub connects all systems.

      System A
         ↓
System B → Hub → System C
         ↓
      System D

Pros:

• Scales better (n systems = n connections)
• Central management and monitoring
• Easier to add new systems

Cons:

• Hub is a single point of failure
• More complex initial setup
• May add latency

Best for: Multiple systems that need to share data

3. Event-Driven (Pub/Sub)

Systems publish events, other systems subscribe.

System A publishes "Order Created"
↓
Event Bus
↓   ↓   ↓
B   C   D subscribe and react

Pros:

• Loosely coupled (systems don't need to know about each other)
• Scales well
• Easy to add new consumers

Cons:

• More complex to debug
• Eventual consistency (not immediate)
• Requires event infrastructure

Best for: Complex ecosystems, microservices, real-time reactions

4. Batch/ETL

Move data in scheduled batches.

Source → Extract → Transform → Load → Destination
         (nightly batch)

Pros:

• Simple to understand
• Good for large data volumes
• Doesn't impact production systems during business hours

Cons:

• Data is never fully current
• Errors may not be caught until next run
• Not suitable for real-time needs

Best for: Reporting, data warehousing, non-urgent synchronization

5. API Gateway

Central API that presents unified interface.

Consumers → API Gateway → Backend Systems

Pros:

• Clean, consistent interface
• Security and authentication in one place
• Can transform and aggregate data

Cons:

• Another system to maintain
• Can become a bottleneck
• Adds a layer of complexity

Best for: Exposing internal systems to partners or apps

Choosing the Right Pattern

Real-Time Requirements

Need data immediately? → Event-driven or Point-to-point Okay with delay? → Batch may be simpler

Number of Systems

Two systems? → Point-to-point is fine Many systems? → Hub-and-spoke or Event-driven

Data Volume

Low volume? → Almost any pattern works High volume? → Consider batch for bulk, events for incremental

Technical Capability

Simple needs? → iPaaS tools (Zapier, Make) Complex needs? → Custom integration layer

Common Integration Scenarios

E-Commerce ↔ ERP

• Orders flow from storefront to fulfillment
• Inventory syncs back to storefront
• Customer data shared

Pattern: Usually hub-and-spoke or point-to-point, near real-time

CRM ↔ Operations

• Sales creates opportunities
• Operations delivers
• Status flows back to sales

Pattern: Event-driven or scheduled sync

Operations ↔ Accounting

• Transactions flow to accounting
• Financial data back for reporting

Pattern: Often batch (end of day/week), sometimes real-time for critical items

External Partners

• Supplier inventory availability
• Shipping carrier status
• Payment processor transactions

Pattern: API integration, often event-driven

Integration Challenges

Data Mapping

Systems use different structures.

• Customer vs. Client vs. Account
• Different field names
• Different formats (dates, currencies, units)

Solution: Clear mapping documentation, transformation layer

Error Handling

What happens when integration fails?

• Retry logic
• Error queues
• Alerting
• Manual intervention process

Solution: Design for failure from the start

Ordering and Timing

Events can arrive out of order. Systems can be temporarily unavailable.

Solution: Idempotent operations, sequence tracking, reconciliation

Security

Data crosses system boundaries. Authentication, authorization, encryption all matter.

Solution: Secure API design, credentials management, audit logging

Building Integration Capability

Option 1: iPaaS (Integration Platform as a Service)

• Zapier, Make (Integromat), Workato
• Pre-built connectors
• Visual workflow builders

Best for: Standard systems, simple workflows, limited IT resources

Option 2: Custom Middleware

• Built specifically for your needs
• Handles complex logic
• Full control

Best for: Complex requirements, non-standard systems, high volume

Option 3: Hybrid

• iPaaS for standard integrations
• Custom for complex or high-volume

Often the practical choice.

Integration Success Factors

✅ Clear data ownership (which system is the source of truth?) ✅ Documented mappings ✅ Error handling and alerting ✅ Monitoring and visibility ✅ Testing with realistic data ✅ Runbooks for common issues

Need to connect your systems? Let's design an integration strategy