[DanteBurbano27]

Overview

This PR delivers the complete Analytics, Data Quality, and Warehouse Modeling Layer for DevFlow Intelligence, developed by the autonomous Google Antigravity team.

It is built strictly on the upstream RepositoryMetadata contract without modifying or interfering with Codex-owned files (ingestion/**, storage/**, orchestration/**, observability/**, .github/**, pyproject.toml, requirements*.txt, .env*, .gitignore).

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Key Deliverables

1. Analytical Data Contract (analytics/contracts/):

   • RepositoryContract: Validates raw dictionaries and RepositoryMetadata against immutable schema version 1.0.0.
   • RepositoryRecord: Frozen, slotted, type-safe analytical dataclass enforcing UTC timezone awareness and nullability constraints.

2. Derived Metrics & Fleet Aggregation (analytics/metrics/):

   • RepositoryMetricCalculator: Deterministically derives days_since_last_push, days_since_creation, activity_status (ACTIVE, STALE, INACTIVE, ARCHIVED, DISABLED), technical ratios (fork_to_star_ratio, issue_to_star_ratio, star_to_fork_ratio), community_interest_score, and size_category.
   • PortfolioAnalyticsAggregator: Aggregates multi-repository fleets with sums, averages, medians, language distributions, owner breakdowns, and leaderboards.

3. Data Quality Framework (analytics/quality/):

   • DataQualityEngine: Programmatic, non-destructive validation engine.
   • 13+ automated rules with strict severity tiers (ERROR vs WARNING):
     • Record-level: ID validity, full_name/owner pattern & consistency, non-negative metrics, expected visibility, timestamp presence, chronological consistency, pushed_at plausibility, URL scheme.
     • Batch-level: Unique repository_id and unique full_name across extraction batches.

4. BigQuery-Ready SQL Models (analytics/sql/):

   • Staging: stg_github_repositories.sql (type casting, null coalescing, 64-bit FARM_FINGERPRINT surrogate keys, windowed deduplication via QUALIFY ROW_NUMBER()).
   • Intermediate: int_repository_activity.sql (temporal diffs, SAFE_DIVIDE ratios, activity classification).
   • Dimensional Marts: dim_repositories.sql, fct_repository_snapshots.sql (partitioned by DATE(snapshot_timestamp), clustered by (repository_id, language)), agg_language_summary.sql, agg_owner_summary.sql.

5. Operational Diagnostic Scripts (scripts/):

   • scripts/check_analytics.py: End-to-end analytical workflow diagnostic.
   • scripts/check_data_quality.py: Data quality evaluation diagnostic.

6. Comprehensive Automated Tests (tests/):

   • 95 tests passing cleanly in < 1 second.
   • Zero network, token, or cloud credentials required.
   • 100% deterministic synthetic fixtures.

7. Documentation & Portfolio Artifacts (docs/, README.md):

   • README.md: Professional portfolio documentation.
   • docs/analytics/analytics_architecture.md: Analytics system architecture.
   • docs/analytics/data_quality_framework.md: Rule catalog and runbook.
   • docs/analytics/warehouse_models.md: BigQuery schema and optimization guide.

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Verification

• python -m pytest: 95 passed in 0.70s.
• ruff check .: All checks passed.
• ruff format --check .: 50 files already formatted.
• git diff --stat origin/feature/dfi-006-repository-metadata: 0 Codex files touched.

Ready for Codex review and final integration.

[DanteBurbano27]

Superseded by #4, which integrated this branch at c70a7cf together with the Codex data platform. The final merge is b5ac0bd and main CI is green.