DynaQuest PC Scraper extracts structured product information and pricing from the DynaQuest PC online store. It helps businesses and analysts turn raw computer hardware listings into actionable, searchable datasets for smarter decisions.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project collects detailed computer hardware data from DynaQuest PC and converts it into a clean, structured format. It solves the challenge of manually tracking fast-changing product catalogs and prices. It is designed for developers, analysts, and e-commerce teams who need reliable hardware market data.
- Collects product listings directly from category and product pages
- Standardizes pricing and specification fields
- Supports large-scale data collection for analysis
- Produces ready-to-use structured outputs
| Feature | Description |
|---|---|
| Product Crawling | Extracts detailed product listings from the store. |
| Pricing Capture | Collects current prices for accurate tracking. |
| Structured Output | Delivers clean, machine-readable data. |
| Scalable Runs | Handles small checks or large catalog scans. |
| Market Insights | Enables research, monitoring, and comparison. |
| Field Name | Field Description |
|---|---|
| product_name | Name of the computer hardware product. |
| price | Current listed price of the product. |
| category | Product category or hardware type. |
| brand | Manufacturer or brand name. |
| availability | Stock or availability status. |
| product_url | Direct link to the product page. |
| image_url | Main product image URL. |
[
{
"product_name": "Gaming Graphics Card RTX 4060",
"price": "₱18,995",
"category": "Graphics Cards",
"brand": "NVIDIA",
"availability": "In Stock",
"product_url": "https://dynaquestpc.com/products/rtx-4060",
"image_url": "https://dynaquestpc.com/images/rtx4060.jpg"
}
]
DynaQuest PC Scraper/
├── src/
│ ├── main.py
│ ├── crawlers/
│ │ └── product_crawler.py
│ ├── parsers/
│ │ └── product_parser.py
│ ├── utils/
│ │ └── helpers.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── input.sample.json
│ └── output.sample.json
├── requirements.txt
└── README.md
- E-commerce teams use it to track competitor pricing, so they can optimize product positioning.
- Market analysts use it to study hardware trends, so they can identify demand shifts.
- Retailers use it to monitor availability changes, so they can manage inventory strategies.
- Developers use it to feed dashboards, so they can automate reporting workflows.
Does this scraper collect all product categories? Yes, it can extract products across multiple hardware categories available on the site.
Is the output suitable for spreadsheets and dashboards? The structured format is compatible with spreadsheets, databases, and analytics tools.
Can it handle frequent price changes? Yes, it is designed for repeated runs to capture updated pricing data.
Is technical knowledge required to use it? Basic familiarity with running scripts is helpful, but the structure is straightforward.
Primary Metric: Processes hundreds of product pages per hour on average.
Reliability Metric: Maintains a high success rate across repeated runs with consistent results.
Efficiency Metric: Optimized parsing minimizes unnecessary resource usage.
Quality Metric: Extracted datasets maintain strong completeness and field accuracy across listings.
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