Does this skill provide code for specific programming languages?

Yes, it includes detailed implementation patterns and code examples for TypeScript (Node.js), Python, Go, and Java/Scala.

What delivery guarantees does the skill help implement?

It provides configuration guidance for various delivery semantics, including At-Most-Once, At-Least-Once, and complex Exactly-Once transactional processing.

Which message brokers are supported by this skill?

The skill covers major brokers including Apache Kafka, Redpanda, Apache Pulsar, and RabbitMQ, providing specific decision frameworks for choosing the right one for your needs.

Can I use this for Change Data Capture (CDC)?

Absolutely. The skill includes dedicated patterns for CDC using tools like Debezium to synchronize database changes into your event streams in real-time.

Does it support stream processing frameworks?

Yes, it includes architectural comparisons and setup guides for Apache Flink, Apache Spark Streaming, Kafka Streams, and ksqlDB.

Streaming Data & Event-Driven Systems

Name: Streaming Data & Event-Driven Systems
Author: ancoleman

byancoleman

•

158

데이터베이스 관리

Builds production-ready event streaming systems and real-time data pipelines using modern brokers like Kafka, Redpanda, and Pulsar.

소개

This skill empowers developers to architect and implement robust streaming data solutions, ranging from event-driven microservices to real-time analytics pipelines. It provides domain-specific guidance on choosing the right message brokers and stream processors (such as Flink and Spark), while offering implementation patterns for producer/consumer logic, Change Data Capture (CDC), and event sourcing across TypeScript, Python, Go, and Java. Whether managing delivery guarantees or implementing complex event processing, this skill ensures best practices for performance, durability, and scalability in high-throughput environments.

주요 기능

End-to-end stream processing patterns with Flink, Spark, and ksqlDB
Production-grade error handling, DLQs, and exactly-once processing strategies
158 GitHub stars
Advanced architectural patterns including Event Sourcing and CDC
Multi-broker support for Apache Kafka, Redpanda, and Apache Pulsar
Language-specific implementations for Node.js, Python, Go, and Java

사용 사례

Implementing event-driven microservices communication and state management
Building real-time analytics, monitoring, and alerting dashboards
Creating high-throughput data integration and automated ETL/ELT pipelines

소개

주요 기능

End-to-end stream processing patterns with Flink, Spark, and ksqlDB
Production-grade error handling, DLQs, and exactly-once processing strategies
158 GitHub stars
Advanced architectural patterns including Event Sourcing and CDC
Multi-broker support for Apache Kafka, Redpanda, and Apache Pulsar
Language-specific implementations for Node.js, Python, Go, and Java

사용 사례

Implementing event-driven microservices communication and state management
Building real-time analytics, monitoring, and alerting dashboards
Creating high-throughput data integration and automated ETL/ELT pipelines