Building an ETL Pipeline in Python | ETL Pipeline for Web Scraping | Naukri Learning
Web Scraping can be a complicated and slow process. But it can be a useful one and can capitalize on parallel processing either with multiple processor threads or distributing scraping requests. Extract, Transform & Load (ETL) pipelines also help to achieve the same.
ETL includes a set of processes that include gathering data from various sources, transforming it, and then storing it into a new single data warehouse. This data warehouse can be accessed by data analysts and data scientists to perform data science tasks, such as data visualization, statistical analysis, ML modeling and front-end app development.
In this tutorial, you will learn how to build a web scraping ETL pipeline in Python using multithreading. The process includes scraping multiple pages, performing transformations on the fetched data and loading the same to a SQL database.
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