Ebay is one of the leading websites in the Ecommerce vertical. 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 scrape 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.
1. 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.
2. 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. Programming and 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.
3. Cleansing and structuring
The data extracted in the initial phase would be present in unstructured format in the dump file and would contain noise – unwanted html tags and text that gets scraped along with the required data. The data must be run through a cleansing setup to remove these. Structuring is done to make the data machine-readable which makes it suitable for further analysis by analytics systems.
At PromptCloud, we have been extracting zettabytes of data from the web for our clients belonging to various industry verticals. With our years of expertise in web crawling and scraping technologies, we can handle all the complicated aspects of the web scraping process. You, as a business owner can focus on the core aspects of your business while getting the required data from your preferred sources, the way you need it.