# DataShadowArchive

**Repository Path**: AiGenApps/data-shadow-archive

## Basic Information

- **Project Name**: DataShadowArchive
- **Description**: Data Shadow 是一个强大的数据对比工具，专注于多源数据对比分析。支持多种数据源(数据库、Excel、CSV、JSON等)，提供友好的可视化界面展示对比结果。适用于数据迁移验证、系统数据同步校验、数据质量核查等场景。（归档项目，新项目见：https://gitee.com/AiGenApps/data-shadow）
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: https://gitee.com/AiGenApps/data-shadow
- **GVP Project**: No

## Statistics

- **Stars**: 17
- **Forks**: 4
- **Created**: 2025-01-07
- **Last Updated**: 2026-01-06

## Categories & Tags

**Categories**: dbmanager

**Tags**: Python, 数据对比, Oracle, MySQL, Excel

## README

# Data Shadow - Data Comparison Tool

## Project Overview
Data Shadow is a powerful data comparison tool focused on multi-source data analysis. It supports multiple data sources (databases, Excel, CSV, JSON, etc.), provides a friendly visual interface to display comparison results. Suitable for data migration verification, system data synchronization verification, data quality inspection and other scenarios.

## Screenshots
![Software Interface Screenshot](screenshots/home_page.png)
![Software FAQ Screenshot](screenshots/faq.png)

## Product Description
### Features
1. Multi-source Data Support
   - Relational databases (MySQL/Oracle), Excel, CSV, JSON files
   - Smart field mapping and alias configuration  
   - Support primary key/composite key configuration
   - Save and load mapping schemes

2. Efficient Comparison and Display
   - Fast comparison based on primary keys, supports large data volumes
   - Data differences highlighting
   - Toggle between full/difference data
   - Custom column name display (code/name)
   
3. Result Export
   - Support Excel, CSV, JSON formats
   - Custom export templates

### Development Plan
- [x] Oracle database support optimization
- [ ] MySQL database support optimization
- [ ] Batch comparison functionality
- [ ] Custom comparison rules
- [ ] Performance optimization and UI beautification

## Usage Guide
### Quick Start
1. Environment Setup
   ```bash
   git clone https://gitee.com/yourusername/data-shadow.git
   cd data-shadow
   pip install -r requirements.txt
   ```

2. Database Support (Optional)
   ```bash
   pip install PyMySQL oracledb
   ```

### Basic Operations
1. Configure data sources and mappings
2. Set comparison rules
3. Execute comparison and view/export results

## Development
### Tech Stack
- Python 3.7+
- tkinter (GUI)
- pandas (Data Processing)
- PyMySQL/oracledb (Database Connection)
- openpyxl (Excel Processing)

### Build from Source
1. Clone the repository
   ```bash
   git clone https://gitee.com/AiGenApps/data-shadow.git
   cd data-shadow
   ```

2. Install dependencies
   ```bash
   pip install -r requirements.txt
   pip install pyinstaller
   ```

3. Run the build script for your operating system

   Windows:
   ```bash
   build_exe.bat
   ```

   macOS:
   ```bash
   chmod +x build_macos.sh
   ./build_macos.sh
   ```

   Linux:
   ```bash
   chmod +x build_linux.sh
   ./build_linux.sh
   ```

4. After building, the executable will be in the `dist` directory
   - Windows: `dist/DataShadow.exe`
   - macOS: `dist/DataShadow`
   - Linux: `dist/DataShadow`

Note:
- Ensure Python 3.7+ is installed
- macOS requires Command Line Tools
- Linux may need additional system dependencies (e.g., tkinter)
  ```bash
  # Ubuntu/Debian
  sudo apt-get install python3-tk
  # CentOS/RHEL
  sudo yum install python3-tkinter
  ```

### Contributing
Issues and Pull Requests are welcome to help improve the project.

## Contact
- Author: ns-cn
- Email: ns-cn@qq.com
- Project URL: https://gitee.com/AiGenApps/data-shadow
- Issues: https://gitee.com/AiGenApps/data-shadow/issues