/architecture-paradigm-pipeline

name: architecture-paradigm-pipeline
description: Applies pipes-and-filters for sequential data transformations. Use when data flows through discrete stages like ETL, streaming analytics, or CI/CD pipelines.
alwaysApply: false
category: architectural-pattern
tags:
- architecture
- pipeline
- pipes-filters
- ETL
- streaming
- data-processing
dependencies: []
tools: []
usage_patterns:
- paradigm-implementation
- data-transformation
- workflow-automation
complexity: medium
model_hint: standard
estimated_tokens: 700

The Pipeline (Pipes and Filters) Paradigm

When to Employ This Paradigm

• When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.
• When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.
• When failure isolation between stages is a critical requirement.

When NOT To Use

• Interactive request/response systems (use

`archetypes:architecture-paradigm-client-server`)

• Stages that must share mutable state, which the pattern cannot express

Adoption Steps

1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema. 2. **Connect via Pipes**: Connect the filters using "pipes," which can be implemented as streams, message queues, or in-memory channels. validate these pipes support back-pressure and buffering. 3. **Maintain Stateless Filters**: Where possible, design filters to be stateless. Any required state should be persisted externally or managed at the boundaries of the pipeline. 4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates. 5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.

Key Deliverables

• An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.
• A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.
• Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).

Risks & Mitigations

• **Single-Stage Bottlenecks**:
• **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.
• **Schema Drift Between Stages**:
• **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.
• **Back-Pressure Failures**:
• **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.

Concrete Components

These vocabulary items name the concrete tools and abstractions that show up when the paradigm is implemented. They are not required dependencies and they are not part of the skill's ``tools:`` frontmatter (which is reserved for Claude Code tool restrictions). Use this list to disambiguate during architecture discussions.

• ``stream-processor``: the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)
• ``message-queue``: the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)
• ``data-validator``: schema-checks every record at filter input and output

Exit Criteria

• [ ] An ADR documents every filter in the pipeline, the chosen pipe technology, the

error-handling strategy (DLQ, retry count, dead-letter routing), and the data replay mechanism.

• [ ] Each filter has a contract test covering its input and output schema; schema drift between

adjacent filters is caught by a CI compatibility check.

• [ ] Observability dashboards are configured showing per-stage latency, throughput, and error

rate before the pipeline is promoted to production.

• [ ] Load testing validates that back-pressure and buffering mechanisms prevent data loss at

2x the expected peak throughput.