Implement Public Communication & Logs Consolidation Strategy

[PipFoweraker]

Overview

Implement the public communication strategy and logs consolidation system to provide transparency around data pipeline operations and dataset releases.

Background

We have a comprehensive strategy documented in docs/PUBLIC_COMMUNICATION_STRATEGY.md that needs implementation.

Goals

1. Transparency: Share data updates publicly
2. Traceability: Document all data transformations
3. Accessibility: Make logs understandable to non-technical users
4. Automation: Generate updates automatically from pipeline runs

Implementation Tasks

Phase 1: Foundation

• Create docs/PUBLIC_DEVBLOG.md - Public-facing development blog
• Create RELEASES.md - Dataset release announcements
• Create logs/consolidated/ - Human-readable log summaries directory
• Implement scripts/logging/consolidate_logs.py - Log consolidation script
• Implement scripts/logging/update_devblog.py - Devblog update script
• Test manual log consolidation

Phase 2: Automation Workflows

• Create .github/workflows/consolidate-logs.yml - Daily log consolidation
• Create .github/workflows/announce-dataset-release.yml - Release announcements
• Test automated daily consolidation
• Test automated release announcements

Phase 3: Enhancement

• Implement scripts/logging/generate_weekly_summary.py - Weekly summaries
• Implement scripts/publishing/generate_release_announcement.py - Release notes generator
• Create .github/workflows/weekly-public-summary.yml - Weekly summary workflow
• Add GitHub Discussions integration (optional)
• Create release asset packaging

Phase 4: Refinement

• Gather feedback on public communications
• Refine log consolidation logic
• Improve summary readability
• Document public communication process

Scripts to Implement

1. scripts/logging/consolidate_logs.py

Purpose: Consolidate all logs from a date into human-readable summary

Key Functions:

• parse_extraction_log(path) -> metrics dict
• parse_validation_log(path) -> validation results
• parse_pipeline_logs(dir) -> pipeline stats
• generate_summary(metrics) -> markdown text
• save_summary(text, output_path)

2. scripts/logging/update_devblog.py

Purpose: Append daily summary to public devblog

Behavior:

• Prepends new entry to top of devblog
• Maintains chronological order (newest first)
• Limits total entries (keep last 30 days, archive older)

3. scripts/publishing/generate_release_announcement.py

Purpose: Generate release announcement from manifest changes

Output: Formatted markdown for RELEASES.md and GitHub releases

4. scripts/logging/generate_weekly_summary.py

Purpose: Aggregate daily summaries into weekly report

Output: Weekly summary with trends, highlights, and metrics

New Files to Create

docs/PUBLIC_DEVBLOG.md          # Public development blog
RELEASES.md                     # Dataset release notes
logs/consolidated/              # Consolidated log summaries
  └── YYYY-MM-DD_summary.md

scripts/logging/
  ├── consolidate_logs.py       # Daily log consolidation
  ├── update_devblog.py         # Update devblog with summary
  └── generate_weekly_summary.py # Weekly summaries

scripts/publishing/
  └── generate_release_announcement.py  # Release notes

.github/workflows/
  ├── consolidate-logs.yml      # Daily automation
  ├── announce-dataset-release.yml  # Release automation
  └── weekly-public-summary.yml # Weekly summaries

Success Metrics

• 100% of pipeline runs have public summary
• All dataset releases announced within 24 hours
• Weekly summaries published consistently
• Zero sensitive data leaked in logs
• Summaries accurately reflect operations

Related Documentation

• PUBLIC_COMMUNICATION_STRATEGY.md - Complete strategy
• RUNBOOK.md - Operations procedures
• LINEAGE.md - Data provenance

Priority

Medium - Important for transparency but not blocking integration work

Estimated Effort

• Phase 1: 4-6 hours
• Phase 2: 3-4 hours
• Phase 3: 3-4 hours
• Phase 4: 2-3 hours

Total: 12-17 hours