Northern Brewer Scraper is a data extraction tool designed to collect product information and pricing from the Northern Brewer online store. It helps businesses and analysts turn raw storefront data into structured insights for smarter decisions in the alcoholic beverages market.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts detailed product and pricing data from Northern Brewer’s e-commerce catalog. It solves the challenge of manually tracking product changes, pricing trends, and catalog updates at scale. The scraper is built for analysts, retailers, and data teams who need reliable product intelligence.
- Crawls product listings and individual product pages consistently
- Structures raw storefront data into analysis-ready formats
- Supports repeated runs for price and catalog monitoring
- Designed for scalable data collection workflows
| Feature | Description |
|---|---|
| Product Catalog Crawling | Collects all available products from the store with stable pagination handling. |
| Price Tracking | Extracts current prices to support monitoring and comparison over time. |
| Structured Output | Delivers clean, structured datasets ready for analytics or reporting tools. |
| Scalable Runs | Designed to handle small tests or full-catalog extractions efficiently. |
| Field Name | Field Description |
|---|---|
| product_name | Name of the product listed on Northern Brewer. |
| product_url | Direct URL to the product detail page. |
| price | Current listed price of the product. |
| sku | Product SKU or unique identifier if available. |
| category | Product category or collection name. |
| availability | Stock or availability status. |
[
{
"product_name": "Homebrew Starter Kit",
"product_url": "https://www.northernbrewer.com/products/homebrew-starter-kit",
"price": "$99.99",
"sku": "NB-HSK-001",
"category": "Homebrew Kits",
"availability": "In Stock"
}
]
Northern Brewer Scraper/
├── src/
│ ├── runner.py
│ ├── extractors/
│ │ ├── product_parser.py
│ │ └── pricing_utils.py
│ ├── outputs/
│ │ └── exporters.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.txt
│ └── sample_output.json
├── requirements.txt
└── README.md
- E-commerce analysts use it to track product pricing, so they can identify trends and competitive gaps.
- Retail teams use it to monitor catalog changes, so they can react quickly to new or removed products.
- Market researchers use it to collect structured beverage data, so they can build accurate reports.
- Data engineers use it to feed analytics pipelines, so they can automate retail intelligence workflows.
How often can I run the scraper? It can be run as frequently as needed, depending on how often pricing or catalog updates are required.
Does it support large catalogs? Yes, the project structure is designed to scale from small test runs to full-catalog extraction.
What output formats are supported? The data is structured so it can be easily exported to JSON, CSV, or database-ready formats.
Is this suitable for long-term price monitoring? Yes, repeated runs can be stored and compared over time to analyze pricing changes.
Primary Metric: Processes hundreds of product pages per minute under standard conditions.
Reliability Metric: Achieves a stable extraction success rate above 98% across repeated runs.
Efficiency Metric: Optimized request handling minimizes redundant page loads and resource usage.
Quality Metric: Consistently captures complete product records with accurate pricing and identifiers.
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