{"id":33319300,"url":"https://github.com/vibhark04/dilligent","last_synced_at":"2026-05-16T09:01:38.325Z","repository":{"id":324209311,"uuid":"1096383895","full_name":"vibhark04/dilligent","owner":"vibhark04","description":"End-to-end AI-assisted e-commerce data pipeline using Python, SQLite, and SQL—featuring synthetic data generation, ingestion, and analytics.","archived":false,"fork":false,"pushed_at":"2025-11-14T11:05:41.000Z","size":162,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-11-14T13:09:15.899Z","etag":null,"topics":["python","sql","sqlite"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vibhark04.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-11-14T10:48:44.000Z","updated_at":"2025-11-14T11:09:59.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/vibhark04/dilligent","commit_stats":null,"previous_names":["vibhark04/dilligent"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/vibhark04/dilligent","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vibhark04%2Fdilligent","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vibhark04%2Fdilligent/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vibhark04%2Fdilligent/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vibhark04%2Fdilligent/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vibhark04","download_url":"https://codeload.github.com/vibhark04/dilligent/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vibhark04%2Fdilligent/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":285319005,"owners_count":27151474,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-11-19T02:00:05.673Z","response_time":65,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["python","sql","sqlite"],"created_at":"2025-11-19T20:00:31.622Z","updated_at":"2025-11-19T20:01:26.195Z","avatar_url":"https://github.com/vibhark04.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Synthetic E-Commerce Data Pipeline\n\nThis project demonstrates an end-to-end agentic SDLC workflow for generating, ingesting, and analyzing synthetic e-commerce data using Cursor IDE.\n\n## Project Structure\n- `data/` – auto-generated CSV datasets.\n- `src/` – Python modules for data generation, ingestion, analytics, and reporting.\n- `db/` – SQLite database file (`ecom.db`).\n- `sql/` – Analytical SQL scripts.\n- `README.md` – documentation.\n\n## Workflow Overview\n1. **Generate synthetic data** with `src/generate_data.py` using Faker and pandas. The script produces `users.csv`, `products.csv`, `orders.csv`, `order_items.csv`, and `payments.csv` with referential integrity.\n2. **Ingest into SQLite** via `src/ingest_data.py`, which rebuilds the schema from scratch, loads CSVs, enforces foreign keys, and validates row counts.\n3. **Run analytics** with SQL files inside `sql/` executed through `src/run_queries.py`. Each SQL statement answers a specific business question (revenue per user, top products, monthly sales, payment distribution).\n4. **Summarize** the entire process using `src/report.py`, which inspects CSV stats, database row counts, and highlights analytics results to confirm the pipeline health.\n\n## Getting Started\n```bash\npython -m venv .venv\n.\\.venv\\Scripts\\activate  # Windows\npip install -r requirements.txt\n```\n\n## Commands\n- `python src/generate_data.py` – regenerate CSV datasets (idempotent).\n- `python src/ingest_data.py` – rebuild database and load data from CSV.\n- `python src/run_queries.py` – execute SQL analytics and pretty-print results.\n- `python src/report.py` – produce a workflow summary (CSV counts, DB counts, KPI snippets).\n\n## Agentic SDLC Notes\n- **Plan** – `README.md` plus `src/config.py` capture requirements and tunable parameters.\n- **Build** – modular scripts inside `src/` create data, database, and analytics artifacts.\n- **Verify** – ingestion validates row counts; SQL runner logs execution status; report script summarizes KPIs.\n- **Operate** – logging across scripts and GitHub-ready structure enable quick troubleshooting and deployment.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvibhark04%2Fdilligent","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvibhark04%2Fdilligent","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvibhark04%2Fdilligent/lists"}