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Found 40 Skills
Diagnose ClickHouse Kafka engine health, consumer status, thread pool capacity, and consumption issues. Use for Kafka lag, consumer errors, and thread starvation.
Expert in Apache Kafka, Event Streaming, and Real-time Data Pipelines. Specializes in Kafka Connect, KSQL, and Schema Registry.
Best practices and guidelines for Apache Kafka event streaming and distributed messaging
Applies general coding standards and best practices for Kafka development with Scala.
Complete guide for Apache Kafka stream processing including producers, consumers, Kafka Streams, connectors, schema registry, and production deployment
Expert-level Apache Kafka, event streaming, Kafka Streams, and distributed messaging
Set up Kafka-based event-driven microservices with Platformatic Watt. Use when users ask about: - "kafka", "event-driven", "messaging" - "kafka hooks", "kafka webhooks" - "kafka producer", "kafka consumer" - "dead letter queue", "DLQ" - "request response pattern" with Kafka - "migrate from kafkajs", "kafkajs migration", "replace kafkajs" Covers @platformatic/kafka, @platformatic/kafka-hooks, consumer lag monitoring, and OpenTelemetry instrumentation.
Implement Event-Driven Architecture (EDA) in Spring Boot using ApplicationEvent, @EventListener, and Kafka. Use for building loosely-coupled microservices with domain events, transactional event listeners, and distributed messaging patterns.
Provides Complete patterns for testing async Python code with pytest: pytest-asyncio configuration, AsyncMock usage, async fixtures, testing FastAPI with AsyncClient, testing Kafka async producers/consumers, event loop and cleanup patterns. Use when: Testing async functions, async use cases, FastAPI endpoints, async database operations, Kafka async clients, or any async/await code patterns.
Use this skill when building real-time data pipelines, stream processing jobs, or change data capture systems. Triggers on tasks involving Apache Kafka (producers, consumers, topics, partitions, consumer groups, Connect, Streams), Apache Flink (DataStream API, windowing, checkpointing, stateful processing), event sourcing implementations, CDC with Debezium, stream processing patterns (windowing, watermarks, exactly-once semantics), and any pipeline that processes unbounded data in motion rather than data at rest.
Manage serverless Redis, Kafka, and QStash on Upstash
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.