Architects robust and maintainable data integration pipelines using ETL, CDC, and event streaming patterns.
Integration Patterns provides expert architectural guidance for connecting disparate data sources and microservices within a modern data ecosystem. Grounded in Gregor Hohpe’s Enterprise Integration Patterns and industry standards like TOGAF, this skill helps developers choose the optimal approach—whether batch ETL, real-time Change Data Capture (CDC), or asynchronous event streams—while ensuring system resilience through idempotency, schema evolution strategies, and advanced error handling like dead-letter queues and circuit breakers.
Key Features
01Resilient schema evolution and transformation layer strategies
02Pipeline monitoring, alerting, and SLA violation tracking
03Pattern selection guidance for ETL, ELT, CDC, and Event Streaming
04Advanced error handling via dead-letter queues and circuit breakers
05Design patterns for idempotency and data deduplication
065 GitHub stars
Use Cases
01Connecting microservices via loosely coupled asynchronous event streams
02Designing reliable batch processing pipelines for high-latency external APIs
03Implementing real-time data synchronization using Change Data Capture (CDC)
