datacoolie-studio 0.1.2

Local web studio for inspecting DataCoolie metadata, lineage, and ETL logs.

DataCoolie Studio

DataCoolie Studio is a local web app for exploring DataCoolie projects. Use it to manage sources, edit metadata, inspect lineage, and monitor extract, transform, and load (ETL) runs.

What you can do

• Organize sources by Project and Environment
• Read and edit metadata in JSON, YAML, or XLSX format
• Inspect lineage from metadata, SQL queries, and Python code
• Monitor Dataflow, Job, and System logs
• Connect to Local, S3, MinIO, ADLS, OneLake, GCS, and Databricks storage
• Store cloud credentials in the operating system credential store

Studio keeps source files as the source of truth. Metadata saves validate the document and create a backup before replacing the original file. Lineage combines evidence for display without creating a merged metadata file.

Install and run

DataCoolie Studio requires Python 3.11 or later.

pip install datacoolie-studio
datacoolie-studio

The launcher starts Studio at http://127.0.0.1:8765, creates its local workspace on first run, and opens your browser.

Install only the cloud integrations you need:

pip install "datacoolie-studio[s3]"
pip install "datacoolie-studio[minio]"
pip install "datacoolie-studio[adls]"
pip install "datacoolie-studio[onelake]"
pip install "datacoolie-studio[gcs]"

Databricks SDK support is included in the base installation. Use pip install "datacoolie-studio[cloud]" to install every other cloud integration.

Create your first workspace

1. Create a Project
2. Add an Environment such as dev, test, or prod
3. Add a metadata file or scan a DataCoolie project
4. Add ETL logs for Monitoring
5. Add Python code artifacts when metadata references Python functions
6. Open Metadata, Assets, Lineage, or Monitoring

Metadata is required. Logs and code artifacts are optional.

Configure Studio

Studio stores local state under ~\.datacoolie\datacoolie-studio\:

db\studio.db
backups\
cache\
logs\

Common launcher options:

datacoolie-studio --port 8765
datacoolie-studio --host 127.0.0.1
datacoolie-studio --db .\.scratch\studio.db
datacoolie-studio --database-url "postgresql+psycopg://user:password@host:5432/datacoolie_studio"
datacoolie-studio --no-open

You can also configure storage with environment variables:

Variable	Purpose
DATACOOLIE_STUDIO_DB	SQLite workspace database path
DATACOOLIE_STUDIO_DATABASE_URL	SQLAlchemy database URL; overrides the SQLite path
DATACOOLIE_STUDIO_RESULT_CACHE_URL	Result-cache SQLite URL

Studio binds to 127.0.0.1 by default. Choose a shared database and review network access before hosting it for multiple users.

Develop from source

Run the backend directly from src. This assumes the active Python environment already contains the dependencies declared in pyproject.toml.

$env:PYTHONPATH = "$PWD\src"
python -m uvicorn datacoolie_studio.main:app `
  --reload `
  --host 127.0.0.1 `
  --port 8765

Run the frontend in another terminal:

cd frontend
npm install
npm run dev

Open http://127.0.0.1:5173. Vite sends API requests to the backend at http://127.0.0.1:8765.

Build the frontend into the Python package:

cd frontend
npm run build

Run repository checks:

.\scripts\verify.ps1
.\scripts\verify.ps1 -Mode Full

The default check covers architecture, packaged static assets, security, API contracts, frontend tests, and the production build. Full mode also runs the complete backend test suite.