<ul><li>This guide shows how to create an NBA Data Lake using Amazon S3, AWS Glue, and Amazon Athena.</li><li>A Python script automates the setup process of creating the data lake.</li><li>Creating a data lake provides a centralized repository of structured and unstructured data at any scale.</li><li>The services used for this project are Amazon S3, AWS Glue and Amazon Athena.</li><li>Amazon S3 is used as the backbone of the data lake, storing both raw and processed NBA data.</li><li>AWS Glue helps manage metadata and schema for the data stored in S3.</li><li>Amazon Athena is used to analyze data stored in S3 using standard SQL.</li><li>CreateBucket, PutObject, ListBucket are the S3 permissions required.</li><li>You can learn about cloud architecture design, data storage best practices, and metadata management using this project.</li><li>Some future enhancements to this project include automated data ingestion, data transformation, advanced analytics, and real-time updates.</li></ul>

Building an NBA Data Lake with AWS: A Comprehensive Guide

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