⚡ Real-Time Data Architecture

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Overview

Real-time data architecture enables sub-second decision making through streaming ingestion, processing, and serving layers.

Core Components

📥 Ingestion

Kafka, Kinesis, Event Hubs, Pulsar

⚙️ Processing

Flink, Spark Streaming, Storm, Kafka Streams

💾 Storage

Redis, Cassandra, HBase, InfluxDB

📊 Serving

APIs, Dashboards, Alerts, ML Models

Architecture Patterns

Event-Driven Architecture

  • Event Sourcing Immutable event log
  • CQRS Separate read/write models
  • Saga Pattern Distributed transactions
  • Event Streaming Continuous data flow

Stream Processing Patterns

  • Windowing Time-based aggregations
  • Watermarks Handle late data
  • Stateful Processing Maintain context
  • Exactly-Once Guarantee processing

Latency Requirements

Use Case Latency Target Technology Stack Example
Ultra Low Latency < 1ms In-memory, custom hardware High-frequency trading
Low Latency 1-100ms Redis, Hazelcast Fraud detection, gaming
Near Real-Time 100ms-1s Kafka + Flink Personalization, monitoring
Soft Real-Time 1-10s Spark Streaming Analytics dashboards

Real-World Use Cases

🏦 Financial Services
  • • Fraud detection (100ms)
  • • Risk management
  • • Algorithmic trading
  • • Payment processing
🛒 E-Commerce
  • • Real-time recommendations
  • • Inventory management
  • • Dynamic pricing
  • • User behavior tracking
🏭 IoT & Manufacturing
  • • Predictive maintenance
  • • Quality control
  • • Supply chain optimization
  • • Energy management

Implementation Challenges

Technical Challenges
  • ⚠️ Backpressure: Handle varying data rates
  • ⚠️ State Management: Fault-tolerant stateful processing
  • ⚠️ Late Data: Out-of-order event handling
  • ⚠️ Exactly-Once: Prevent duplicate processing
Operational Challenges
  • ⚠️ Monitoring: Real-time system health
  • ⚠️ Debugging: Distributed system complexity
  • ⚠️ Scaling: Auto-scaling based on load
  • ⚠️ Cost: Always-on infrastructure

Best Practices

Design Principles
  • Idempotency: Design for reprocessing
  • Circuit Breakers: Prevent cascade failures
  • Graceful Degradation: Fallback mechanisms
  • Observability: Comprehensive monitoring & alerting
  • Schema Evolution: Handle data format changes
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