We build an application with Python and Django and use it to transform the data that an API sends, so our application is acting as a client and consuming the data.

Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.

In general, web data extraction is used by people and businesses who want to make use of the vast amount of publicly available web data to make smarter decisions.

If you’ve ever copied and pasted information from a website, you’ve performed the same function as any web scraper, only on a microscopic, manual scale. Unlike the mundane, mind-numbing process of manually extracting data, web scraping uses intelligent automation to retrieve hundreds, millions, or even billions of data points from the internet’s seemingly endless frontier.

Web data extraction – also widely known as data scraping – has a huge range of applications. A data scraping tool can help you automate the process of extracting information from other websites, quickly and accurately. It can also make sure the data you’ve extracted is neatly organized, making it easier to analyze and use for other projects.

In the world of e-commerce, web data scraping is widely used for competitor price monitoring. It’s the only practical way for brands to check the pricing of their competitors’ products and services, allowing them to fine-tune their own price strategies and stay ahead of the game. It’s also used as a tool for manufacturers to ensure retailers are compliant with pricing guidelines for their products. Market research organizations and analysts depend on web data extraction to gauge consumer sentiment by keeping track of online product reviews, news articles, and feedback.

There’s a vast array of applications for data extraction in the financial world. Data scraping tools are used to extract insight from news stories, using this information to guide investment strategies. Similarly, researchers and analysts depend on data extraction to assess the financial health of companies. Insurance and financial services companies can mine a rich seam of alternative data scraped from the web to design new products and policies for their customers.