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Level 7 / Project 07 - Stale Data Detector

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— This project — — Flashcards — —

Focus

  • freshness checks and stale alerts

Why this project exists

This project gives you level-appropriate practice in a realistic operations context. Goal: run the baseline, alter behavior, break one assumption, recover safely, and explain the fix.

Run (copy/paste)

Use <repo-root> as the folder containing this repository's README.md.

cd <repo-root>/projects/level-7/07-stale-data-detector
python project.py --input data/sample_input.txt --output data/output_summary.json
pytest -q

Expected terminal output

... output_summary.json written ...
2 passed

Expected artifacts

  • data/output_summary.json
  • Passing tests
  • Updated notes.md

Alter it (required)

  1. Add a "degraded" severity level between warning and stale (e.g. at 75% of stale threshold).
  2. Add a most_stale() method that returns the single stalest source.
  3. Re-run script and tests — verify the new severity and method appear in output.

Break it (required)

  1. Provide a last_updated timestamp in the future (ahead of now) and observe negative age.
  2. Remove the source key from one of the source records.
  3. Capture the KeyError or nonsensical "fresh" classification for future timestamps.

Fix it (required)

  1. Clamp negative ages to zero (a future timestamp means "just refreshed").
  2. Validate that each source record has the required source and last_updated keys.
  3. Add tests for future timestamps and missing required keys.

Explain it (teach-back)

  1. Why do different data sources need different freshness thresholds?
  2. What happened when a timestamp was in the future?
  3. How did clamping to zero prevent misleading results?
  4. Where would stale-data detection be used in a real data platform?

Mastery check

You can move on when you can: - run baseline without docs, - explain one core function line-by-line, - break and recover in one session, - keep tests passing after your change.

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