nonda Scraper is a production-ready tool designed to collect structured product information and pricing data from the nonda online store. It helps businesses and analysts turn raw storefront pages into actionable e-commerce intelligence. Built for reliability and scale, it supports continuous monitoring and data-driven decisions.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts product listings, prices, and related metadata from nonda’s e-commerce platform and converts them into clean, structured datasets. It solves the challenge of manually tracking product changes and pricing trends across a growing catalog. The scraper is ideal for analysts, marketers, and product teams who need consistent and accurate data.
- Collects structured product and pricing data at scale
- Designed for stores powered by modern e-commerce frameworks
- Outputs data ready for analytics, dashboards, or internal tools
- Supports repeated runs for historical comparison and trend analysis
| Feature | Description |
|---|---|
| Product Data Extraction | Captures names, prices, availability, and product URLs. |
| Pricing Monitoring | Enables tracking of price changes over time. |
| Structured Output | Delivers clean, analysis-ready JSON datasets. |
| Scalable Crawling | Handles small catalogs and large inventories efficiently. |
| Automation Friendly | Easily integrates into data pipelines and workflows. |
| Field Name | Field Description |
|---|---|
| product_name | The displayed name of the product. |
| product_url | Direct link to the product page. |
| price | Current listed price of the product. |
| currency | Currency in which the price is listed. |
| availability | Stock or availability status. |
| sku | Product identifier or SKU if available. |
| category | Product category or collection name. |
| last_updated | Timestamp of when the data was captured. |
[
{
"product_name": "Wireless Car Charger",
"product_url": "https://nonda.co/products/wireless-car-charger",
"price": 39.99,
"currency": "USD",
"availability": "In Stock",
"sku": "NND-WCC-01",
"category": "Car Accessories",
"last_updated": "2025-03-18T10:42:11Z"
}
]
nonda Scraper/
├── src/
│ ├── main.py
│ ├── crawler/
│ │ ├── product_crawler.py
│ │ └── pagination.py
│ ├── parsers/
│ │ └── product_parser.py
│ ├── utils/
│ │ ├── helpers.py
│ │ └── logger.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- E-commerce analysts use it to monitor pricing trends, so they can optimize competitive positioning.
- Marketing teams use it to track product availability, so campaigns stay aligned with stock levels.
- Product managers use it to analyze catalog changes, so they can plan launches and updates effectively.
- Data teams use it to feed dashboards, so stakeholders get up-to-date insights automatically.
Is this scraper suitable for large product catalogs? Yes. It is designed to scale efficiently and can handle hundreds or thousands of product pages with consistent performance.
Can the output be used directly in analytics tools? The structured JSON output is analysis-ready and can be imported into BI tools, databases, or spreadsheets.
Does it support repeated runs for historical tracking? Yes. Running the scraper on a schedule allows you to build historical datasets for price and availability analysis.
What level of technical skill is required to use it? Basic familiarity with Python and command-line tools is sufficient for setup and operation.
Primary Metric: Average processing rate of ~120 product pages per minute under standard conditions.
Reliability Metric: Maintains a success rate above 98% across repeated runs.
Efficiency Metric: Optimized crawling minimizes redundant requests and reduces runtime costs.
Quality Metric: Delivers consistently complete datasets with accurate pricing and metadata fields.
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