A production-ready data extraction tool that collects product listings, prices, and metadata from Spigen Inc’s online store. It helps teams monitor catalog changes, analyze pricing trends, and build reliable datasets for e-commerce insights using the Spigen Inc Scraper.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for spigen-inc-scraper you've just found your team — Let’s Chat. 👆👆
The Spigen Inc Scraper systematically gathers structured product data from Spigen’s storefront, turning dynamic product pages into clean, usable datasets. It solves the challenge of manual product tracking and inconsistent pricing visibility. This project is built for developers, analysts, and e-commerce teams who need dependable product intelligence at scale.
- Extracts structured product and pricing data from live storefront pages
- Designed for repeatable runs and consistent data output
- Handles large product catalogs with stable performance
- Outputs data ready for analytics, reporting, or integrations
| Feature | Description |
|---|---|
| Product Listing Crawl | Collects all available products from categories and collections. |
| Pricing Extraction | Captures current prices, discounts, and currency details. |
| Product Metadata | Extracts titles, SKUs, images, variants, and availability. |
| Scalable Execution | Handles small and large catalogs reliably. |
| Structured Output | Produces clean, analysis-ready data formats. |
| Field Name | Field Description |
|---|---|
| product_id | Unique identifier of the product. |
| product_name | Official product title as listed in the store. |
| price | Current selling price of the product. |
| original_price | Base price before discounts, if available. |
| currency | Currency used for pricing. |
| availability | Stock or availability status. |
| product_url | Direct URL to the product page. |
| image_urls | List of product image links. |
| category | Product category or collection name. |
| scraped_at | Timestamp of data extraction. |
Spigen Inc Scraper/
├── src/
│ ├── main.py
│ ├── scraper/
│ │ ├── product_parser.py
│ │ └── pricing_utils.py
│ ├── config/
│ │ └── settings.json
│ └── output/
│ └── formatter.py
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- E-commerce analysts use it to monitor price changes, so they can optimize competitive pricing strategies.
- Retail researchers use it to analyze product assortments, so they can identify gaps and opportunities.
- Marketing teams use it to track product launches, so they can align campaigns with catalog updates.
- Developers use it to feed dashboards and tools, so they can automate reporting workflows.
Can this scraper handle large product catalogs? Yes, it is designed to scale efficiently across extensive product listings while maintaining stable execution.
What output formats are supported? The scraper generates structured data that can be easily converted to JSON, CSV, or database-ready formats.
Does it support repeated scheduled runs? Yes, it is suitable for recurring executions to track historical pricing and catalog changes.
Is the data structured for analytics tools? Absolutely. Fields are normalized and consistent, making them easy to ingest into BI or analytics systems.
Primary Metric: Processes an average of 120–180 product pages per minute under standard conditions.
Reliability Metric: Maintains a success rate above 98% across repeated runs.
Efficiency Metric: Optimized requests minimize redundant page loads and reduce execution time.
Quality Metric: Achieves over 99% data completeness for core product and pricing fields.
"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
|