A focused data extraction tool designed to collect structured product listings data from ODH-style catalogs and marketplaces. It helps transform scattered product information into clean, usable datasets for analysis, monitoring, and integration workflows.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for odhproductslistings you've just found your team — Let’s Chat. 👆👆
OdhProductslistings Scraper extracts structured product listings data such as titles, prices, availability, and metadata from ODH-based product sources. It solves the problem of manually collecting and normalizing large volumes of product data. This project is built for developers, analysts, and businesses that rely on accurate product datasets.
- Collects structured product listing information at scale
- Normalizes inconsistent product fields into a unified format
- Designed for automation-ready data pipelines
- Suitable for analytics, monitoring, and integrations
| Feature | Description |
|---|---|
| Structured Output | Produces clean, normalized product listing records. |
| Scalable Crawling | Handles large product catalogs efficiently. |
| Flexible Inputs | Supports configurable listing sources and parameters. |
| Data Validation | Ensures consistent field formatting and completeness. |
| Field Name | Field Description |
|---|---|
| product_id | Unique identifier of the product listing. |
| product_name | Name or title of the product. |
| price | Listed price of the product. |
| currency | Currency used for pricing. |
| availability | Stock or availability status. |
| category | Product category or classification. |
| product_url | Direct URL to the product page. |
| last_updated | Timestamp of the last data update. |
OdhProductslistings/
├── src/
│ ├── main.py
│ ├── scraper/
│ │ ├── listings_parser.py
│ │ └── request_handler.py
│ ├── utils/
│ │ ├── validators.py
│ │ └── helpers.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- E-commerce teams use it to collect product listings, so they can track pricing and availability changes.
- Data analysts use it to build product datasets, enabling market and trend analysis.
- Developers use it to feed structured product data into internal systems and APIs.
- Researchers use it to study catalog composition and product distribution patterns.
Q: What type of products can this scraper handle? It is designed for general product listings and works across multiple categories as long as the data follows a listing-based structure.
Q: Is this suitable for large catalogs? Yes, the scraper is structured to handle high-volume product listings efficiently.
Q: Can the output be integrated into other systems? Absolutely. The structured format is ideal for databases, analytics pipelines, and APIs.
Primary Metric: Processes hundreds of product listings per minute under normal conditions.
Reliability Metric: Maintains a high success rate with stable request handling and retries.
Efficiency Metric: Optimized parsing minimizes memory usage during large runs.
Quality Metric: Delivers consistent, complete product records with validated 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
|