Overview

Defines a data-pipeline paradigm where data moves through a fixed sequence of filters connected by pipes. It emphasizes reuse of individual processing stages, predictable routing, and strong failure isolation for ETL, streaming analytics, and CI/CD workflows. Key to the approach are stateless filters, explicit data schemas, and observability to ensure reliable, scalable pipelines.

How This Skill Works

Each stage is a filter that performs a single transformation with a defined input/output schema. Filters are connected via pipes—streams, message queues, or in-memory channels—that support back-pressure and buffering. Filters are kept stateless where possible, with any needed state persisted at the boundaries; deployment enables horizontal scaling and independent upgrades.

When to Use It

• When data must flow through a fixed sequence of discrete transformations (ETL, streaming analytics, or CI/CD pipelines).
• When you want to reuse individual processing stages independently or scale bottleneck stages separately.
• When failure isolation between pipeline stages is critical.
• When you need clear stage boundaries and observable metrics for end-to-end monitoring.
• Not suitable: data flow is non-sequential or has complex branching/merging; avoid using this paradigm in that case.

Quick Start

1. Step 1: DefineFilters with a single, well-defined transformation and explicit input/output schemas.
2. Step 2: Connect filters with Pipes (streams/queues) that support back-pressure.
3. Step 3: Instrument, write contract/integration tests, and deploy stages horizontally.

Best Practices

• Define each filter to perform a single, well-defined transformation with clear input/output schemas.
• Connect filters with pipes that support back-pressure and buffering.
• Keep filters stateless; externalize required state or manage at boundaries.
• Instrument every stage with latency, throughput, and error-rate metrics; build observability dashboards.
• Plan deployments to scale stages horizontally, upgrade independently, and maintain ADRs, contract tests, and end-to-end tests.

Example Use Cases

• An ETL job where data moves through extract → transform → load stages with fixed sequencing.
• A streaming analytics pipeline where event data passes through reusable filter stages that can scale independently.
• A CI/CD-like pipeline breaking build, test, and deploy into separate filters connected by streams or queues.
• A data-validation stage that enforces schemas and handles back-pressure to prevent downstream failures.
• A data processing workflow with centralized ADR, contract tests, and dashboards for end-to-end replay and observability.

Frequently Asked Questions