Welcome to sparksneeze’s documentation!

sparksneeze is a Python library tool that can process common Data Warehouse transformations to supported targets.

It is still in early development, it is not recommended using it in any kind of production environment.

Contents:

• Introduction
  • The Data Warehouse Challenge
  • The Problem with Typical Solutions
  • The Hidden Costs
  • A Better Approach
  • Scope and focus
  • Roadmap
• Installation
  • Requirements
  • Using uv (Recommended)
  • Using pip
  • Development Installation
• Usage Guide
  • Library Usage
  • Command Line Usage
• Strategies
  • Introduction
  • Automatic Metadata Enrichment
  • Strategies
• Strategy Comparison and Selection Guide
  • Quick Strategy Selector
  • Detailed Strategy Comparison
  • Use Case Decision Tree
  • Real-World Scenarios
  • Strategy Selection Guidelines
  • Common Anti-Patterns
  • Migration Strategies
• Schema Evolution
  • Introduction
  • How Schema Evolution Works
  • Auto-Expand Behavior
  • Auto-Shrink Behavior
  • Hash Calculation During Schema Evolution
  • Strategy-Specific Evolution Behavior
  • Data Loss Scenarios
  • Best Practices
  • Example: Complete Schema Evolution Workflow
  • Troubleshooting Schema Evolution
• Metadata Module
  • Overview
  • Metadata Fields
  • Configuration
  • Usage Examples
  • Strategy-Specific Behavior
  • Querying Data with Metadata
  • Best Practices
• API Reference
  • Core Module
  • CLI Module
• Command Line Interface
  • Basic Usage
  • Required Arguments
  • Strategy Options
  • Logging Options
  • Global Options
  • Examples

Quick Start

Install sparksneeze:

uv add sparksneeze

Use as a library or as cli.

Indices and tables

• Index

• Module Index

• Search Page