Building a Data Lake on AWS: From Data Migration to AI-Driven Insights

Abstract

As organizations generate and process increasing amounts of data, building data lakes on cloud platforms like AWS has become crucial to managing large datasets efficiently. This paper outlines the key steps in constructing a scalable data lake on AWS, starting from data migration to leveraging AI for insights. It explores how AWS services like S3, Glue, and SageMaker work together to facilitate data storage, transformation, and machine learning. In addition, it highlights the importance of orchestrating data pipelines with automation tools like AWS Lambda and Apache Airflow to ensure smooth, scalable, and efficient workflows. This paper explores the end-to-end process of migrating data to AWS, constructing scalable data lakes, and leveraging AI capabilities to drive actionable insights. Through practical examples, diagrams, and pseudocode, this paper provides a comprehensive guide to implementing data lakes with AWS services such as S3, Glue, and SageMaker, highlighting key considerations around data migration, storage, processing, and analytics. The role of automation tools like AWS Lambda and Airflow in orchestrating these pipelines is also discussed. Keywords AWS, Data Lake, AI-driven Insights, Data Migration, Amazon S3, AWS Glue, Amazon SageMaker, Cloud Analytics, Data Pipeline, ETL, Machine Learning