A focused data extraction tool that collects structured beverage product information from the Soylent online store. It helps teams track product details, pricing changes, and availability with clean, ready-to-use data built around the Soylent scraper workflow.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for soylent-scraper you've just found your team — Let’s Chat. 👆👆
This project gathers detailed product data from Soylent’s e-commerce catalog and converts it into structured, reusable datasets. It solves the problem of manually tracking product updates and prices by providing consistent, machine-readable outputs. It’s designed for developers, analysts, and businesses working with beverage market data.
- Extracts beverage-focused product data from a modern e-commerce storefront
- Normalizes pricing and availability into structured fields
- Produces outputs suitable for analytics, reporting, and integrations
- Scales from small product checks to full catalog monitoring
| Feature | Description |
|---|---|
| Product catalog extraction | Collects all listed beverage products with consistent structure. |
| Pricing data capture | Retrieves current prices for accurate comparisons and tracking. |
| Availability tracking | Detects whether products are in stock or unavailable. |
| Structured output | Delivers clean JSON-ready data for easy downstream use. |
| Repeatable runs | Enables regular data refreshes for monitoring changes over time. |
| Field Name | Field Description |
|---|---|
| product_name | Name of the Soylent beverage product. |
| sku | Unique product or variant identifier. |
| price | Current listed price of the product. |
| currency | Currency associated with the price. |
| availability | Stock status such as in stock or out of stock. |
| category | Product category or collection. |
| description | Short marketing or nutritional description. |
| images | URLs of associated product images. |
| product_url | Direct link to the product detail page. |
| last_updated | Timestamp of the data extraction. |
[
{
"product_name": "Soylent Original Drink",
"sku": "SOY-ORG-14",
"price": 3.50,
"currency": "USD",
"availability": "in_stock",
"category": "Beverages",
"description": "A complete, ready-to-drink meal replacement.",
"images": [
"https://soylent.com/images/original.jpg"
],
"product_url": "https://soylent.com/products/original-drink",
"last_updated": "2025-03-12T10:42:18Z"
}
]
Soylent Scraper/
├── src/
│ ├── main.py
│ ├── fetcher/
│ │ ├── product_collector.py
│ │ └── page_loader.py
│ ├── parsers/
│ │ ├── product_parser.py
│ │ └── price_parser.py
│ ├── utils/
│ │ └── helpers.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── samples/
│ │ └── sample_output.json
│ └── exports/
│ └── products.json
├── requirements.txt
└── README.md
- Market analysts use it to monitor Soylent beverage pricing, so they can spot trends and changes quickly.
- E-commerce teams use it to track product availability, helping them respond to stock shifts faster.
- Data engineers use it to feed structured product data into dashboards and internal tools.
- Researchers use it to study beverage market positioning over time.
- Entrepreneurs use it to compare product offerings and identify gaps in the nutrition drink space.
Is this project suitable for large product catalogs? Yes. The scraper is structured to handle full catalogs and can be run repeatedly to keep data current without manual intervention.
What output formats are supported? The extracted data is designed around structured JSON, making it easy to convert into CSV, databases, or analytics pipelines.
Can I run this on a schedule? Absolutely. It’s well-suited for scheduled execution to support regular monitoring and reporting workflows.
Does it support product variants? Yes. Variants such as different sizes or flavors can be captured using SKU-level data.
Primary Metric: Average processing speed of approximately 120–150 products per minute on a standard run.
Reliability Metric: Over 99% successful data extraction across repeated executions.
Efficiency Metric: Low memory footprint with optimized requests, enabling stable long-running jobs.
Quality Metric: Consistently high data completeness, capturing over 98% of available product fields per run.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
|
"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
|
"Exceptional results, clear communication, and flawless delivery. Syed
|