Scraping E-Commerce Data Feeds Used in Real-Time Pricing
The client had a Research & Analytics business so he needed a well-customized data extractor to scrape real-time e-commerce data feeds.
The client had a Research & Analytics business so he needed a well-customized data extractor to scrape real-time e-commerce data feeds.
Our achievements in the field of business digital transformation.
Data Analytics & Research Business for E-Commerce and Retail
The client was looking to get persistent, quality, & clear e-commerce data to allow doing analytics & research.
They needed easy access to complete product list data from some particular categories, alongside product specifications and pricing. Formerly, the customer had his in-house team that manually gathered data from various web sources, although the results were insufficient compared to higher efforts.
Among the severe manual efforts, establishing data when getting e-commerce data for the database was challenging. They needed clear data in the necessary format, so it could be easily uploaded to the internal database for running a comparison engine and also perform different monitoring activities.
The client had given us a list of sources to have extracted data points and data scraping frequency so that it was set for daily jobs.
Team 3i Data Scraping has set data crawlers to fetch the required e-commerce data from all particular resource websites. All the use cases come under website scraping offerings, as source websites had different structures and designs.
The customer wanted extracted data in a CSV format and uploaded to S3 servers. The initial setup was comprehensive within a few days, and the crawlers started delivering the required data immediately.
Around 300K records got distributed to this customer during the early crawling.
Setting up a Crawler: Primarily, a crawler was set that could extract product prices and required data fields for pre-defined categories in an automatic style daily.
Data Template: As per the schema provided by a customer, the template was created using data structuring, which would happen.
Data Delivery: The final data was provided in the XML format using Data API, depending on daily, without physical involvement from both sides.
Every record within a dataset had information, i.e., product’s name, pricing, accessibility, short and long descriptions, dimensions, category, image URLs, SKU, brands, resources, and source URLs from where it was procured.
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