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pipes-and-filters

Decompose data transformation pipelines into independent, testable stages.

Updated Jun 5, 2026
Search Tags:#data pipeline#decomposition#composable#backpressure#stage-based
One-click install
npx skills add https://github.com/hung-phan/system-skills --skill pipes-and-filters
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pipes-and-filters
Source: https://github.com/hung-phan/system-skills/tree/main/skills/system-review/references/architecture-patterns/pipes-and-filters
Command: npx skills add https://github.com/hung-phan/system-skills --skill pipes-and-filters

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps manage and optimize data transformation pipelines by decomposing them into independent stages, making them more scalable, testable, and maintainable.

Core Features & Use Cases

  • Decomposition into Stages: Break down monolithic pipelines into smaller, composable stages.
  • Scalability: Scale individual stages independently based on their resource requirements.
  • Testability: Test each stage in isolation without the need for the entire pipeline.
  • Use Case: For example, a data pipeline processing logs can be split into stages for parsing, filtering, and aggregating data, each running in parallel and independently scalable.

Quick Start

To use the pipes-and-filters Skill, define your pipeline stages and connect them using pipes (queues or channels) that manage backpressure and data flow.

Frequently Asked Questions about pipes-and-filters

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

FAQPage Schema
How do I decompose a monolithic data pipeline into composable stages?▼

Decomposing a data pipeline involves breaking monolithic transformations into smaller, independent stages connected by pipes. You define each stage separately and link them using queues or channels to manage data flow and backpressure for increased scalability.

How does backpressure management work in stage-based event processing systems?▼

Backpressure management in stage-based pipelines uses queues or channels to control data flow between independent stages. This prevents faster upstream stages from overwhelming slower downstream components, ensuring stable parallel processing during event processing.

Can I test individual data transformation stages in isolation?▼

Yes, you can test individual data transformation stages in isolation. Decomposing monolithic pipelines into independent stages allows you to validate each parsing, filtering, or aggregation component without running the entire data pipeline.

What is the best way to scale ETL jobs for parallel processing?▼

The best way to scale ETL jobs is decomposing them into independent, composable stages. This allows you to scale individual stages horizontally based on specific resource requirements, optimizing parallel processing across the data pipeline.

Do I need knowledge of distributed systems to manage composable data pipelines?▼

Yes, managing composable data pipelines requires understanding of distributed systems, concurrency, and backpressure management. This foundational knowledge is necessary to effectively handle parallel processing and data flow across independent pipeline stages.

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