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streaming-data

Build event streaming pipelines with Kafka, Pulsar, Redpanda, and RabbitMQ.

503|73|Updated Nov 13, 2025
Search Tags:#microservices#data pipeline#real-time#streaming#kafka#spark#flink
One-click install
npx skills add https://github.com/ancoleman/ai-design-components --skill streaming-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: streaming-data
Source: https://github.com/ancoleman/ai-design-components/tree/main/skills/streaming-data
Command: npx skills add https://github.com/ancoleman/ai-design-components --skill streaming-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables the creation of robust event streaming systems and real-time data pipelines, facilitating seamless communication and data flow between applications.

Core Features & Use Cases

  • Message Broker Integration: Supports Kafka, Pulsar, Redpanda, and RabbitMQ for durable event storage and distribution.
  • Stream Processing: Enables real-time data transformation and analysis using Flink, Spark, Kafka Streams, and ksqlDB.
  • Use Case: Build a microservices architecture where events like 'order created' are published to Kafka, processed by a Flink application for real-time analytics, and then consumed by downstream services for fulfillment.

Quick Start

Use the streaming-data skill to set up a basic Kafka producer and consumer in Python.

Frequently Asked Questions about streaming-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a real-time data pipeline with Kafka and Flink for microservices?▼

To build a real-time data pipeline with Kafka and Flink, you publish events like 'order created' to Kafka, process them using a Flink application for real-time analytics, and then consume them downstream for fulfillment.

What is the best way to process streaming data using Kafka Streams and ksqlDB?▼

Processing streaming data using Kafka Streams and ksqlDB enables real-time data transformation and analysis. This approach supports durable event storage and distribution while allowing continuous query execution over live data streams.

Can I use Pulsar or Redpanda instead of Kafka for event streaming pipelines?▼

Yes, you can use Pulsar or Redpanda for event streaming pipelines. The system supports multiple message brokers including Kafka, Pulsar, Redpanda, and RabbitMQ for durable event storage and distribution.

How do I set up a basic Kafka producer and consumer in Python?▼

Setting up a basic Kafka producer and consumer in Python involves creating scripts that publish events to Kafka topics and subsequently subscribe to those topics to process incoming messages in real-time.

Does event sourcing and CDC work with RabbitMQ and Spark stream processing?▼

Yes, event sourcing and CDC are supported alongside stream processing via Spark. RabbitMQ handles the message broker integration for durable event storage, while Spark manages the real-time data transformation and analysis.

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