docs: add data pipeline testing spike findings

[mikelane]

Summary

• Researched data engineering stacks (Airflow 320M downloads, Prefect 32M, Dagster 15M) and testing pain points
• Assessed dioxide API fitness for data pipeline use cases (no API changes needed)
• Evaluated framework integrations: recommends Airflow guide + lightweight module, skip Dagster/Prefect
• Produced priority-ranked content and DX improvement recommendations with T-shirt size estimates
• Includes a complete ETL pipeline code sketch (ports, prod adapters, test fakes, service, tests)

Key Findings

The existing dioxide API (@adapter.for_(), @service, Profile, fresh_container) already handles data pipeline dependency injection well. The gap is documentation and examples that speak to data engineers, not API changes.

Top recommendations:

1. P0: Data pipeline testing guide in docs/guides/
2. P1: Runnable data pipeline example in examples/data_pipeline/
3. P2: Use data pipeline as case study for mock-vs-fake comparison (Write Mock vs Fake comparison page with code examples #394)
4. P3: dev.to article targeting data engineers

Test plan

• Document is well-structured with actionable next steps
• Code sketch uses correct dioxide API patterns
• No slop (verified: no em-dashes, semicolons, or filler words)
• Next steps are concrete enough to file as individual issues

Fixes #435

Generated with Claude Code

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 91.85%. Comparing base (9f37e0b) to head (ad30780).
⚠️ Report is 11 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #475   +/-   ##
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  Coverage   91.85%   91.85%           
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  Files          21       21           
  Lines        1903     1903           
  Branches      372      372           
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  Hits         1748     1748           
  Misses         91       91           
  Partials       64       64           

Flag	Coverage Δ
python-3.13	91.85% <ø> (ø)

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