Build a News ETL Data Pipeline Using Python and SQLite

Extract, transform, and load (ETL) is a process in data warehousing and data integration where data is extracted from different source systems, transformed into a more suitable format, and then loaded into a target database or data warehouse. The ETL process is a fundamental step in data integration and plays a vital role in ensuring that data is accurate, consistent, and ready for analysis.

SQLite is a lightweight, serverless, and self-contained relational database management system (RDBMS). It’s known for its simplicity and ease of use. It’s used as an embedded system in smart TVs and IoT devices. It’s also used to power web browsers like Google Chrome and Mozilla Firefox to manage and store data, such as bookmarks, history, etc.

In this hands-on project, we’ll delve into the world of data engineering by building an ETL pipeline for news data. The primary goal is to extract news data from News API, which is in a semi-structured format (JSON), transform it into a structured format, and load it into an SQLite database. Furthermore, we’ll explore the automation of this pipeline using Apache Airflow.

The final implementation of the project will transform data from an unstructured format to a structured one, as illustrated below.