Analyzing YouTube Content With AWS: Data Pipeline, AI And Visualization

Abstract

YouTube generates a large volume of semi-structured data that provides valuable insights into audience engagement, publishing behavior, and emerging content trends. Efficient analysis of this data has become increasingly important due to the rapid growth of digital media consumption. This study presents a cloud-based data engineering pipeline developed using Amazon Web Services (AWS) to process and analyze large-scale YouTube datasets. The pipeline begins with data ingestion either directly from Amazon S3 or via the YouTube Data API. Automated preprocessing is performed using AWS Lambda functions, including data cleaning, transformation, and feature extraction. To improve storage efficiency and query performance, the processed data is converted to the Apache Parquet format and stored in partitioned structures on Amazon S3. Amazon Athena enables serverless querying of optimized datasets, allowing scalable analysis without the need for infrastructure management. AWS Glue is utilized for ETL automation, metadata cataloging, and schema management, while AWS Step Functions coordinate workflow execution and enhance reliability through error-handling mechanisms. The proposed architecture is designed to be scalable and extensible, enabling future integration with visualization platforms and machine learning workflows. Overall, the system provides a practical and cost-effective approach for transforming raw YouTube data into meaningful insights that support data-driven decision-making for content creators, analysts, and digital 
media organizations.