dbt Data Tool

This tool analyzes dbt project artifacts to provide visibility into project size, quality, and structure. It reads the manifest.json, dbt_project.yml, and optional run_results.json to compute metrics such as model_count, test_coverage, and lineage depth, enabling teams to gauge readiness and plan optimizations.
Conceptually, the tool builds a dependency graph from dbt nodes (models, seeds, sources), identifies cycles, and measures depth via the longest path. It cross-maps tests to models to report coverage and flags gaps where tests are missing or outdated. When run results are supplied, it aggregates status across environments and time windows to show recent reliability.
Who Benefits: analytics engineers, data platform teams, and data scientists who maintain dbt pipelines, perform onboarding, or prepare CI/CD checks. Unique differentiator: it combines static artifact analysis with optional run-data to reveal actionable insights like orphan models, dependencies that cause rebuild bottlenecks, and potential run-time issues across environments.
Core features include artifact parsing, graph construction, cycle detection, lineage depth calculation, test-to-model mapping, run-result aggregation, and exportable summaries suitable for dashboards or reports. Use cases include project intake, release readiness, auditing model counts, and validating pipeline health before deployment.
Outputs are designed for automation: a structured metrics object, a human-readable summary, and exportable formats (JSON/CSV). The tool emphasizes deterministic results, reproducibility across environments, and clear signals for optimization tasks.