chuck-data 0.1.0

Command line AI for customer data

Chuck Data

A text-based user interface (TUI) for managing Databricks resources including Unity Catalog, SQL warehouses, models, and volumes. Chuck Data provides an interactive shell environment for data engineering tasks with AI-powered assistance.

Features

• Interactive TUI for managing Databricks resources
• AI-powered data engineering assistance
• Authentication with Databricks using personal access tokens
• List and select available LLM models from Databricks Model Serving
• Browse Unity Catalog resources (catalogs, schemas, tables)
• Manage SQL warehouses
• Profile database tables with automated PII detection (via LLMs)
• Support for Amperity operations
• Command-based interface with both modern commands and slash commands

Installation

pip install chuck-data

Usage

Chuck Data provides an interactive text-based user interface. Run the application using:

chuck-data [options]

Command Line Options

• --version - Show program version and exit
• --no-color - Disable color output
• --help - Show help message and exit

The application will launch an interactive TUI where you can use various commands to manage your Databricks resources.

Available Commands

Chuck Data supports a command-based interface with slash commands that can be used within the interactive TUI. Type /help within the application to see all available commands.

Authentication & Workspace

• /login, /amperity-login - Log in to Amperity
• /databricks-login, /set-token - Set Databricks API token
• /logout - Log out from Amperity or other authentication services
• /workspace - Select a workspace configuration

Catalog & Schema Management

• /catalogs - List catalogs in Unity Catalog
• /schemas - List available schemas in Unity Catalog
• /tables - List available tables in Unity Catalog
• /catalog - Get information about a specific catalog
• /schema - Get information about a specific schema
• /table - Get specific table from Unity Catalog
• /select-catalog - Select a catalog for future operations
• /select-schema - Select a schema for future operations

Model & Endpoint Management

• /models - List available models from Databricks API
• /list-models - List available models with filtering and detailed information
• /model - Set the active model

SQL Warehouse Management

• /warehouses - List available SQL warehouses
• /warehouse - Set SQL warehouse ID
• /select-warehouse - Set the active SQL warehouse for database operations
• /create-warehouse - Create a new SQL warehouse in the Databricks workspace
• /run-sql - Execute SQL query against the active warehouse

Volume Management

• /list-volumes, /volumes - List volumes in a Unity Catalog schema
• /create-volume - Create a new volume in Unity Catalog
• /upload-file - Upload a file to a Unity Catalog volume

PII & Data Management

• /scan-pii - Scan a table for PII data using active model
• /tag-pii - Tag detected PII columns in a table
• /setup-stitch - Configure Stitch integration
• /add-stitch-report - Add a Stitch report to a Unity Catalog table

Job Management

• /jobs - List available jobs in Databricks workspace
• /job-status - Check status of a specific job

Utilities

• /help - Display help information about available commands
• /status - Show current connection status
• /agent - Interact with the AI agent
• /bug - Report a bug
• /exit - Exit the application

Development

Requirements

• Python 3.10 or higher
• uv - Python package installer and resolver

Install the project with development dependencies:

uv pip install -e .[dev]

Testing

Run the test suite:

uv run -m pytest

Run linters and static analysis:

uv run ruff .
uv run black --check --diff src tests
uv run pyright

For test coverage:

uv run -m pytest --cov=src

CI/CD

This project uses GitHub Actions for continuous integration:

• Automated testing on Python 3.10
• Code linting with flake8
• Format checking with Black

The CI workflow runs on every push to main and on pull requests. You can also trigger it manually from the Actions tab in GitHub.