This popular Ecommerce portal provides a great platform for sellers to list their products and sell them to a global customer base. This also makes eBay a great source for acquiring Ecommerce data for business intelligence, research or price monitoring. Products are available under various categories like fashion, electronics, computers, home appliances and much more. Acquiring this data manually is practically impossible considering the quantity of data available. Automated data extraction is the best option left to acquire product data from eBay.
Web scraping technologies can be used to crawl product data from eBay at scale. Web scraping is a computing technique used for fetching huge amounts of unstructured data from the web on automation. It is a fairly complicated process that involves coding and demands technical expertise. Here is a brief overview of the web scraping process.
Defining sources and data points: This is the first step in the web scraping process. To start scraping data, one must first identify the best sources for the data required. In our case, the source will be eBay. Data points are pieces of information available on the web pages that needs to be scraped. Product data includes data points like product title, product id, price, brand name, reviews, ratings, color, size and so on.
Web crawler setup: Once the data points and sources are defined, the source code of the target website can be analysed to find out what tags hold the required data points. These tags are used for programming the web crawler. Programmingand setting up the crawler is the most complicated part of the web scraping process. Once the crawler has been set up and run, the data starts getting collected in a dump file which can either be offline or on the cloud.