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🔄 Architectural Patterns for Data and Communication Flow

This guide covers common architectural patterns used to structure how data and communication flow within distributed systems. These patterns solve integration, scalability, and maintainability challenges across services and components.

🔁 Communication Patterns

These patterns define how components interact in a system.

🔹 1. Request-Response

The client sends a request and waits for the server to respond. This is the most traditional and synchronous pattern.

Use Cases: REST APIs, RPC calls, internal service communication
Pros: Simple, intuitive
Cons: Tightly coupled, blocking latency

// Example HTTP call
GET /user/123 → 200 OK

🔹 2. Pub/Sub (Publish-Subscribe)

Publishers emit messages to a broker, and subscribers receive relevant messages asynchronously.

Use Cases: Event-driven microservices, notifications, log pipelines
Pros: Decoupled, scalable
Cons: Delivery guarantees vary, difficult to trace

// Publisher sends event to "user.signup"
publish("user.signup", { id: 123 })

// Subscribers listen to the topic
on("user.signup", handleWelcomeEmail)

🔹 3. API Gateway

A single entry point to route, authenticate, and shape traffic to internal services.

Use Cases: Microservice-based architectures, external APIs
Pros: Centralized management, security, analytics
Cons: Single point of failure if not designed properly

🔹 4. Peer-to-Peer

Components communicate directly with each other without intermediaries.

Use Cases: File sharing (BitTorrent), mesh networks
Pros: Decentralized, resilient
Cons: Harder to coordinate and secure

🔹 5. Orchestration

A central orchestrator controls the sequence and interaction between services.

Use Cases: Workflow engines, CI/CD pipelines, data pipelines
Pros: Visibility, control, step-by-step logic
Cons: Tight coupling to orchestrator logic

🧠 State & Data Flow Patterns

These patterns focus on how data flows and is stored or transformed across the system.

🔹 6. Event Sourcing

Store all state changes as a sequence of events, rather than current state.

Use Cases: Audit trails, undo/redo functionality, temporal data
Pros: Replayable, auditable
Cons: Rebuilding state can be expensive, event versioning needed

// Events stored: UserCreated, EmailUpdated
replay(events) → state

🔹 7. ETL (Extract, Transform, Load)

Extract data from sources, transform it, and load it into a data warehouse.

Use Cases: Data warehousing, reporting, BI tools
Pros: Clean, structured data for analysis
Cons: Not real-time, batch-oriented

// Pseudo-ETL
data = extract(csv)
clean = transform(data)
load(clean, warehouse)

🔹 8. Streaming Processing

Process and analyze data in real-time as it's produced.

Use Cases: Fraud detection, real-time metrics, IoT
Pros: Low latency insights
Cons: More complex infrastructure

// Kafka consumer
consume("sensor.data") → process(stream)

🔹 9. Batching

Accumulate data until a size or time threshold is reached, then process it in bulk.

Use Cases: Email sending, scheduled imports, billing
Pros: Resource efficient, reduces overhead
Cons: Latency between events and processing

🧭 Comparison Summary

Pattern	Type	Use Case	Pros	Cons
Request-Response	Communication	APIs, sync services	Simple	Tightly coupled
Pub/Sub	Communication	Messaging, events	Decoupled, scalable	Hard to trace, retry logic
API Gateway	Communication	Microservices API exposure	Secure, centralized control	Possible bottleneck
Peer-to-Peer	Communication	Mesh systems, P2P file sharing	Decentralized, resilient	Harder to manage
Orchestration	Communication	Workflows, CI/CD	Coordinated, visible steps	Orchestrator bottleneck
Event Sourcing	State Management	Financial, audit trails	Replay, auditability	Complex rebuild, versioning
ETL	Data Integration	BI, analytics	Structured clean data	Latency, batch only
Streaming	Data Flow	Real-time apps	Fast, responsive	Infra complexity
Batching	Data Flow	Aggregated jobs	Efficient resource use	Delayed processing

📚 Further Reading

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