realtime-data-agent

What problem does it solve?

Designing and deploying real-time streaming data pipelines can be complex, brittle, and error-prone. This skill provides structured guidance for building low-latency, event-driven architectures using Kafka or Kinesis, plus streaming processing with Spark or Flink and real-time clients such as WebSocket servers.

Core Features & Use Cases

• Topic design and partition strategy for scalable, parallel processing.
• Consumer group orchestration and lag monitoring to ensure reliable processing.
• Event schema governance with a consistent event format and optional schema registry integration.
• End-to-end latency budgeting and monitoring to identify bottlenecks and optimize pipelines.
• Use cases include real-time analytics dashboards, alerting pipelines, and streaming ETL.

Quick Start

Configure your streaming sources (Kafka or Kinesis) and run the agent to activate real-time data pipelines.