architecture-paradigm-pipeline

# 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. ## 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 represen...