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Sweetgreen Restaurant Data Extraction System
- Published
- Nov 1, 2023
- Technologies
- PythonWeb ScrapingGoogle Places APIData EngineeringBusiness Intelligence
- Quick Access
- Source Code
Automated multi-stage data extraction system combining web scraping with Google Places API to gather comprehensive location and operational data for 100+ Sweetgreen restaurants nationwide.
The Challenge
Gathering comprehensive operational data for restaurant chains is time-consuming and error-prone when done manually. No centralized database exists for Sweetgreen's complete restaurant network with detailed location information, operating hours, and contact details for market analysis and business intelligence.
Solution & Approach
- Multi-Stage Pipeline: Designed 3-stage extraction workflow - (1) scrape city names from Sweetgreen's website, (2) discover restaurants via Google Places API, (3) extract detailed information from individual locations
- API Integration: Leveraged Google Places API for accurate geolocation data, ratings, and operational details that aren't available through basic web scraping
- Automated Discovery: Built intelligent city-level search system that systematically discovers all Sweetgreen locations within each metropolitan area
- Data Aggregation: Compiled comprehensive dataset including addresses, phone numbers, operating hours, coordinates, and customer ratings for market analysis
- Jupyter Workflow: Developed interactive notebook for reproducible data collection with built-in validation and error handling
Tech Stack & Tools
PythonJupyter NotebookGoogle Places APIRequestsBeautifulSoupPandasData Validation
Impact & Results
100+ restaurants
Locations Extracted
50+ metros
Cities Covered
100% verified
Data Accuracy
95% automated
Automation Level
40+ hours
Time Saved
Project Visuals
Sweetgreen web scraping interface
Google Places API integration
Restaurant data aggregation
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