A comprehensive open-source Python library and web application for accessing and filtering data using the BrightData API across multiple datasets. This system provides intuitive, type-safe database queries with built-in support for Amazon Products, Amazon-Walmart Comparison, Shopee Products, and other datasets, plus a complete snapshot management system for handling long-running database operations.
- Amazon Products - Comprehensive product data with pricing, reviews, and availability
- Amazon-Walmart Comparison - Cross-platform competitive analysis
- Shopee Products - Southeast Asian e-commerce data
- TikTok Products - Social commerce insights
- Target Products - US retail data
- Visual Query Builder - Intuitive web interface for creating complex filters
- Type-Aware Filtering - Automatic validation and type checking
- Nested Logic Groups - Support for complex AND/OR logic combinations
- Smart Deduplication - Prevents duplicate API calls with order-independent matching
- Real-time Preview - See your query structure before submission
- Long-running Query Support - Handle queries that take hours to complete
- Status Monitoring - Real-time tracking of query progress
- Download Management - Safe, cost-aware data downloads
- Local Storage - Persistent records of all submissions
- Metadata Management - Custom titles and descriptions for snapshots
- Multi-page Streamlit App - Clean, organized interface
- Query Builder - Visual filter construction
- Snapshot Viewer - Data analysis and visualization
- Settings Management - API key and configuration
- Responsive Design - Works on desktop and mobile
# Clone the repository
git clone https://github.com/yourusername/brightdata-manager.git
cd brightdata-manager
# Install dependencies
pip install -r requirements.txt
pip install -r requirements_ui.txt# Copy the example configuration
cp secrets.example.yaml secrets.yaml
# Edit secrets.yaml with your BrightData API key
# You can get an API key from https://brightdata.com/# Launch the web interface
python launch_viewer.py
# Or run directly with Streamlit
streamlit run app.pyfrom util import BrightDataFilter
# Initialize with dataset name (recommended)
amazon_products = BrightDataFilter("amazon_products")
# Create a simple filter
F = amazon_products.filter
query = (F.rating >= 4.5) & (F.reviews_count > 100)
# Submit the query
snapshot_id = amazon_products.search_data(
filter_obj=query,
records_limit=1000,
description="High-rated products with many reviews"
)
print(f"Query submitted! Snapshot ID: {snapshot_id}")graph TB
subgraph "User Interface"
UI[Streamlit Web App]
CLI[Command Line Interface]
JUPYTER[Jupyter Notebooks]
end
subgraph "Core System"
BDF[BrightDataFilter]
FC[Filter Criteria]
DR[Dataset Registry]
CM[Config Manager]
end
subgraph "Data Storage"
SR[Snapshot Records]
DL[Downloads]
SEC[Secrets]
end
subgraph "External APIs"
BDA[BrightData API]
end
UI --> BDF
CLI --> BDF
JUPYTER --> BDF
BDF --> FC
BDF --> DR
BDF --> CM
BDF --> SR
BDF --> DL
CM --> SEC
BDF --> BDA
- Architecture Overview - System design and components
- Technical Specifications - Detailed technical documentation
- API Reference - Complete API documentation
- Examples - Usage examples and tutorials
- Getting Started - Step-by-step setup guide
- Query Builder Guide - How to use the visual query builder
- Snapshot Management - Working with snapshots
- Configuration - System configuration options
- Product Analysis - Find trending products and market opportunities
- Competitive Intelligence - Compare prices and availability across platforms
- Market Research - Analyze customer reviews and ratings
- Inventory Planning - Identify stockout opportunities
- Machine Learning - Train models on product and review data
- Statistical Analysis - Perform market research and trend analysis
- Data Visualization - Create charts and dashboards
- Research Projects - Academic and commercial research
- Market Analysis - Understand market trends and opportunities
- Competitor Analysis - Track competitor pricing and products
- Customer Insights - Analyze customer behavior and preferences
- Strategic Planning - Make data-driven business decisions
from util import BrightDataFilter
# Initialize filter
amazon_products = BrightDataFilter("amazon_products")
F = amazon_products.filter
# Complex nested query
query = (
(F.rating >= 4.0) &
(F.reviews_count > 50) &
(F.price.between(10, 100)) &
(F.category.in_list(["Electronics", "Books"]))
)
# Submit with custom metadata
snapshot_id = amazon_products.search_data(
filter_obj=query,
records_limit=5000,
description="High-quality electronics and books under $100",
title="Premium Products Analysis"
)# Process multiple queries
queries = [
{"filter": F.rating >= 4.5, "limit": 1000, "desc": "Top rated products"},
{"filter": F.price < 50, "limit": 2000, "desc": "Budget products"},
{"filter": F.reviews_count > 1000, "limit": 500, "desc": "Popular products"}
]
results = []
for query in queries:
snapshot_id = amazon_products.search_data(
filter_obj=query["filter"],
records_limit=query["limit"],
description=query["desc"]
)
results.append(snapshot_id)# Run all tests
python -m pytest tests/
# Run with coverage
python -m pytest tests/ --cov=util
# Run specific test file
python -m pytest tests/test_brightdata.py -vWe welcome contributions! Please see CONTRIBUTING.md for details.
- Fork the repository
- Clone your fork:
git clone https://github.com/yourusername/brightdata-manager.git - Install dependencies:
pip install -r requirements.txt - Install UI dependencies:
pip install -r requirements_ui.txt - Copy
secrets.example.yamltosecrets.yamland add your API key - Run tests:
python -m pytest tests/
- Follow PEP 8
- Use type hints
- Add docstrings to functions and classes
- Write tests for new features
This project is licensed under the MIT License - see the LICENSE file for details.
- BrightData for providing the comprehensive API
- Streamlit for the amazing web framework
- The open source community for inspiration and contributions
- GitHub Issues - For bug reports and feature requests
- GitHub Discussions - For questions and general discussion
- Documentation - Check the docs/ directory for detailed guides
- Additional Datasets - Support for more e-commerce platforms
- Advanced Analytics - Built-in statistical analysis tools
- API Rate Limiting - Smart rate limiting and retry logic
- Data Export - Export to various formats (Excel, Parquet, etc.)
- Scheduled Queries - Automated query execution
- Collaboration Features - Share queries and results with team members
Made with ❤️ by the BrightData Manager team