data_pipeline_fragility.md

(draft — where pipelines break)

Data Pipeline Fragility#

Scientific data pipelines often combine multiple FW/SW layers.
Fragility emerges when assumptions compound across stages.

1. Multi‑Stage Inference#

Each stage adds:

• assumptions
• noise
• transformations
• potential drift

Fragility increases with pipeline depth.

2. Hidden Dependencies#

Pipelines often depend on:

• undocumented parameters
• implicit defaults
• environmental conditions
• hardware quirks

These create Q‑regime and neg‑regime behavior.

3. Nonlinear Interactions#

Small upstream errors can:

• amplify
• cascade
• destabilize downstream stages

This is common in inversion algorithms and AI tools.

4. Data Quality Sensitivity#

Pipelines break when:

• SNR drops
• baselines drift
• samples deviate from training data
• calibration is outdated

5. Version Mismatch#

Different versions of:

• firmware
• libraries
• models
• drivers

…can produce incompatible or contradictory outputs.

6. Containment Strategies#

• document pipeline stages
• log versions
• define valid input domains
• test against known references
• monitor drift over time

This file helps contributors understand where FW/SW pipelines become fragile and how to document them clearly.