📊 Data Integration Design Patterns?

🧩 Common Data Integration Design Patterns

1. ETL (Extract, Transform, Load)

• Use Case: Batch processing of large datasets
  • Flow: Data is extracted from sources, transformed for consistency, and loaded into a data warehouse
  • Pros: High control over transformation logic
  • Cons: Not suitable for real-time needs

2. ELT (Extract, Load, Transform)

• Use Case: Cloud-native data platforms
  • Flow: Data is loaded first, then transformed within the target system
  • Pros: Leverages target system’s compute power
  • Cons: May require advanced transformation capabilities

3. CDC (Change Data Capture)

• Use Case: Real-time replication
  • Flow: Captures changes in source data and replicates them to the target
  • Pros: Low latency updates
  • Cons: Complex setup and monitoring

4. Data Virtualization

• Use Case: Unified view without physical movement
  • Flow: Queries are executed across multiple sources in real time
  • Pros: No data duplication
  • Cons: Performance depends on source systems

5. Streaming Integration

• Use Case: IoT, real-time analytics
  • Flow: Data flows continuously via platforms like Kafka or Spark
  • Pros: Real-time insights
  • Cons: Requires robust infrastructure

🧠 Choosing the Right Pattern

Pattern	Best For	Latency	Complexity	Scalability
ETL	Historical analysis	High	Medium	High
ELT	Cloud-native analytics	Medium	Medium	High
CDC	Real-time replication	Low	High	Medium
Data Virtualization	On-demand unified views	Low	Medium	Low
Streaming Integration	Real-time event processing	Very Low	High	High