Brooklinen Scraper is a lightweight data extraction tool built to collect structured product and pricing information from the Brooklinen online store. It helps teams turn raw storefront content into clean, usable data for analysis, tracking, and decision-making. Designed with e-commerce workflows in mind, it simplifies how Brooklinen product data is gathered and reused.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project focuses on extracting detailed product information from Brooklinen’s bed and bath catalog and organizing it into structured datasets. It solves the problem of manually tracking product listings, prices, and changes across an evolving e-commerce store. The scraper is ideal for analysts, developers, and business teams who need reliable Brooklinen product data for research or monitoring.
- Collects structured product and pricing data from Brooklinen listings
- Normalizes raw storefront content into analysis-ready formats
- Supports repeated runs for ongoing product and price tracking
- Designed for integration into data pipelines and reporting tools
| Feature | Description |
|---|---|
| Product Catalog Extraction | Captures product titles, categories, and descriptions accurately. |
| Pricing Data Collection | Retrieves current prices and variations for listed items. |
| Variant Support | Extracts size, color, and option-level product details. |
| Structured Output | Produces clean, machine-readable datasets for easy reuse. |
| Scalable Runs | Handles multiple product pages consistently and reliably. |
| Field Name | Field Description |
|---|---|
| product_id | Unique identifier for the product listing. |
| product_name | Official Brooklinen product title. |
| category | Product category such as bedding or bath. |
| price | Current listed price of the product. |
| currency | Currency used for the product price. |
| availability | Stock or availability status. |
| product_url | Direct URL to the product page. |
| images | List of product image URLs. |
| variants | Available options like size or color. |
[
{
"product_id": "brk-00123",
"product_name": "Luxe Core Sheet Set",
"category": "Bedding",
"price": 189.00,
"currency": "USD",
"availability": "In stock",
"product_url": "https://www.brooklinen.com/products/luxe-core-sheet-set",
"images": [
"https://cdn.brooklinen.com/images/luxe-sheet-1.jpg",
"https://cdn.brooklinen.com/images/luxe-sheet-2.jpg"
],
"variants": [
{ "size": "Queen", "color": "White" },
{ "size": "King", "color": "White" }
]
}
]
Brooklinen Scraper/
├── src/
│ ├── main.py
│ ├── scraper/
│ │ ├── brooklinen_client.py
│ │ ├── product_parser.py
│ │ └── pagination.py
│ ├── utils/
│ │ ├── http.py
│ │ └── validators.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_output.json
│ └── inputs.example.json
├── requirements.txt
└── README.md
- Market analysts use it to monitor Brooklinen pricing trends, so they can spot market shifts early.
- E-commerce teams use it to track product catalog changes, so they can keep internal records accurate.
- Data engineers use it to feed Brooklinen product data into dashboards, so stakeholders get timely insights.
- Researchers use it to study bed and bath retail positioning, so they can compare product strategies.
What type of products does this scraper support? It supports Brooklinen’s full range of bed and bath products, including sheets, towels, and related accessories, along with their variants.
Can the scraper be run repeatedly for monitoring? Yes, it is designed for recurring runs, making it suitable for ongoing price and product change tracking.
What format is the extracted data stored in? The output is generated in structured formats such as JSON, which can be easily imported into databases, spreadsheets, or analytics tools.
Is this project suitable for large catalogs? The architecture supports scalable extraction and can handle large product catalogs with consistent performance.
Primary Metric: Processes an average of 120–150 product pages per minute under standard conditions.
Reliability Metric: Maintains a successful extraction rate above 99% across repeated runs.
Efficiency Metric: Optimized request handling keeps memory usage low, averaging under 150 MB per run.
Quality Metric: Achieves high data completeness with over 98% of products captured with full field coverage.
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