Enterprise Integration Patterns (EIP)

Systems rarely live in isolation. Integration is the "glue" that holds the enterprise together.

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The 4 Integration Styles

How do applications exchange data?

1. File Transfer: System A writes a CSV, System B reads it.
   • Use Case: Batch processing, legacy mainframes.
   • Drawback: Not real-time, data consistency lag.
2. Shared Database: System A and B read the same SQL tables.
   • Use Case: Simple monolithic systems.
   • Drawback: Strong coupling. If you change the schema, you break both apps. Avoid in distributed systems.
3. Remote Procedure Invocation (RPC/REST): System A calls System B's API.
   • Use Case: Real-time user interactions.
   • Drawback: Tight coupling to availability (if B is down, A fails).
4. Messaging: System A sends a message to a broker; B consumes it.
   • Use Case: Decoupled, asynchronous workflows.
   • Drawback: Added infrastructure complexity, eventual consistency.

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Topologies

Point-to-Point

Every system connects directly to every other system.

• Result: N(N-1)/2 connections.
• Verdict: "Spaghetti Architecture." High maintenance nightmare.

Real-World Problems:

• Adding new system requires changes to all existing systems
• No centralized monitoring or governance
• Tight coupling makes independent deployment impossible
• Testing becomes exponentially complex

Hub-and-Spoke (Broker)

A central message broker (RabbitMQ, Kafka, SQS) manages traffic.

• Result: Decoupled systems. The sender doesn't need to know who the receiver is or if they are online.
• Verdict: The standard for modern microservices.

Benefits:

• Easy to add new consumers/producers
• Centralized monitoring and management
• Natural load balancing and scaling
• Built-in retry and persistence

Bus (ESB)

An intelligent pipe that handles routing and transformation.

• Verdict: Proceed with caution. In the mid-2000s, ESBs became bloated with business logic. The modern preference is "Smart Endpoints, Dumb Pipes."

Modern Alternatives:

• API Gateways for routing and transformation
• Sidecar patterns for cross-cutting concerns
• Service meshes for service-to-service communication

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Real-World Integration Scenarios

Scenario 1: E-Commerce Order Processing

Integration Challenges Solved:

• Order processing continues even if payment service is temporarily down
• New services (like fraud detection) can be added without changing order service
• Each service can scale independently based on load
• Natural audit trail through event log

Scenario 2: Legacy System Integration

Key Components:

• Anti-Corruption Layers translate between modern APIs and legacy protocols
• API Gateway provides unified entry point
• Data Transformation handles format differences (JSON ↔ XML)
• Protocol Bridging converts REST to SOAP/file-based interfaces

Benefits:

• Legacy systems can be modernized incrementally
• Modern services protected from legacy complexity
• Clear boundaries prevent tight coupling
• Easier testing and maintenance

Scenario 3: Multi-Cloud Data Integration

Integration Patterns Used:

• Change Data Capture (CDC) for real-time data replication
• Message Broker for cross-cloud data streaming
• Data Transformation in flight for different cloud requirements
• Multi-tenant Processing for different data consumers

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Essential Async Patterns

1. Dead Letter Queue (DLQ)

What happens when a message can't be processed (malformed JSON, logic bug)?

• Don't: Block the queue and stop processing valid messages (Head-of-Line blocking).
• Do: Move the failed message to a side "Dead Letter" queue for human inspection or automated retry later.

2. Idempotent Receiver

In distributed messaging, you will receive duplicate messages (network retries, at-least-once delivery).

• The Rule: Processing the same message twice must not corrupt the state.
• The Fix: Track processed Message IDs in a Redis cache or a dedicated DB table. If seen before, acknowledge and ignore.

3. Scatter-Gather

You need to ask multiple vendors for a quote (e.g., "Get Flight Price").

1. Scatter: Broadcast the request to all vendors.
2. Gather: Wait for replies.
3. Aggregate: Return the best price once a timeout is reached or all replies are in.

Integration Best Practices

1. Start with the Business Process

• Map the actual business workflow first
• Identify system boundaries and data flows
• Consider error scenarios and recovery paths
• Design for business continuity

2. Choose the Right Integration Style

3. Handle Failure Gracefully

• Implement circuit breakers for external dependencies
• Use dead letter queues for failed messages
• Design for idempotency to handle retries
• Monitor and alert on integration failures

4. Security Considerations

• Encrypt data in transit and at rest
• Implement proper authentication/authorization
• Use API keys and secrets management
• Log and audit all cross-system communications

5. Monitoring and Observability

• Trace requests across system boundaries
• Monitor message queue depths and processing times
• Set up alerts for integration failures
• Measure business metrics, not just technical ones

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