LESSON 4 - SIA

System Integration

Connecting diverse applications, systems, and data sources to enable seamless communication and data flow

1. Business Agility

2. Data Consistency

3. Operational Efficiency

4. Cost Reduction

5. Risk Mitigation

Why Integration Matters—Business Drivers?

Business Agility

Quickly adapt to market changes by recombining systems

Data Consistency

Ensure single source of truth across the organization

Operational Efficiency

Automate manual handoffs and reduce

errors

Cost Reduction

Leverage existing systems rather than replacing them

Risk Mitigation

Minimize disruption when modernizing legacy systems

1. Data Integration

2. Application Integration

3. Process Integration

What are the three layers of Integration?

Data Integration

Moving and synchronizing

data between systems;

ensuring consistency.

Application Integration

Enabling communication between applications; exchanging messages and commands.

Process Integration

Orchestrating business workflows across multiple systems.

1. Monolithic Architecture

2. Service-Oriented Architecture (SOA)

3. Microservices Architecture

4. Serverless Architecture

What are the Architecture Styles?

Monolithic Architecture

Single, tightly coupled application; all components in one codebase.

Pros: Simple to develop initially; easier debugging; single deployment; straightforward testing

Cons: Difficult to scale; technology lock-in; risky deployments; hard to modify individual components

Service-Oriented Architecture

(SOA)

Multiple loosely coupled services; each exposes well-defined interfaces (typically SOAP/XML)

Pros: Reusability; loose coupling; technology flexibility; independent

scaling

Cons: Complexity; message overhead; orchestration challenges; governance overhead

Microservices Architecture

Small, independently deployable services organized around

business capabilities

Pros: Team autonomy; independent scaling; technology flexibility; faster releases

Cons: Distributed-systems complexity; network failures; data consistency challenges; operational overhead

Serverless Architecture

Functions or managed services execute application logic on

demand

Pros: Low infrastructure management; rapid delivery; elastic scaling;

granular cost model

Cons: Cold starts; runtime limits; provider dependency; distributed debugging; variable costs

1. Point-to-Point Integration (Tight

Coupling)

2. Hub-and-Spoke Integration (ESB Pattern)

3. Message-Oriented Integration

(Asynchronous):

4. Event-Driven Architecture

What are the Integration Patterns?

Point-to-Point Integration (Tight

Coupling)

A direct connection between one producer and one consumer

Pros: Simple to understand; fast to implement; low initial infrastructure cost

Cons: Tight coupling; duplicated transformations; difficult monitoring;

connection sprawl

Hub-and-Spoke Integration (ESB

Pattern)

A central hub connects and coordinated multiple systems

Pros: Centralized governance; reusable transformations; simplified endpoint connectivity

Cons: Bottleneck risk; central failure domain; expensive administration; possible coupling to the hub

Message-Oriented Integration (Aynchronous)

Systems communicate by sending and receiving messages through brokers or queues

Pros: Resilience; load smoothing; consumer independence; reliable delivery

Cons: Eventual processing; operational complexity; duplicate

messages; ordering concerns

Event-Driven Architecture

Components react to events representing facts that have occurred

Pros: Loose coupling; extensibility; real-time reactions; independent consumers

Cons: Eventual consistency; difficult tracing; schema evolution; duplicate processing

Synchronous (Request-Response)

The caller sends a request and waits for a response

Best For: Real-time validation, queries, and decisions requiring an immediate answer

Aynchronous (Fire-and-Forget)

The producer submits work or publishes an event without waiting for completion

Best For: Long-running work, notifications, batch processing,

and decoupled workflows

1. ETL (Extract data, Transform into procession layer, Load into target)

2. ELT (Extract and Load data first, Transform to target platform)

3. CDC (Change Data Capture)

4. Batch Processing

5. Streaming

6. Key Concerns

What are the Data Integration Strategies?

1. Versioning

2. Documentation

3. Rate Limiting

4. Error Handling

5. Security

6. Idempotency

API Design Best Practices:

1. Circuit Breaker

2. Retry

3. Bulkhead

4. Timeout

5. Fallback

What are the Resilience Patterns?

Circuit Breaker

Stop calls to an unhealthy dependency

temporarily

Retry

Reattempt transient failures with bounded exponential backoff and jitter

Bulkhead

Isolate resources so one dependency cannot exhaust all capacity

Timeout

Stop waiting after a defined limit

Fallback

Return a safe alternative, cached result, or deferred response