From Scraping to S3: how I automated a data pipeline with AWS | by Kevin Meneses González | December 2024

Imagine you work for a streaming company like Netflix or Disney+ and are responsible for evaluating whether acquiring the rights to Marvel films is a profitable investment. To make this decision, you need to analyze box office data, revenue trends, and audience demand. This article explains how to create a data pipeline on AWS to solve this problem by extracting data from various sources, cleaning it, transforming it, and storing it in an S3 bucket.

ETL (Extract, Transform, Load) is a process that extracts data from different sources, transforms it to meet specific needs, and loads it into a storage system. This approach simplifies the analysis of large volumes of data.

Using AWS to create an ETL offers several advantages:

• Scalability: Services like AWS Lambda handle load fluctuations without server management.
• Seamless integration: AWS S3, EventBridge, and Lambda work together effortlessly.
• Profitability: Pay-as-you-go pricing minimizes unnecessary spending.

As Albert Einstein once said

“The measure of intelligence is…”