This blog post, part of a series on DynamoDB data modeling, delves into optimizing secondary indexes to maximize cost savings and scalability. It emphasizes the importance of carefully selecting which attributes to project into a secondary index, advocating for minimal projection to reduce write unit consumption and storage costs. The concept of "sparse" indexes is highlighted as a means to efficiently filter data, and the pitfalls of "GSI overloading" are discussed, a practice that became obsolete after DynamoDB increased the limit of Global Secondary Indexes (GSIs) per table from 5 to 20. The article criticizes the "single table design" approach when it leads to unnecessary complexity and inefficiency, urging developers to prioritize flexibility and scalability. The author concludes by advising against GSI overloading and encouraging thoughtful index design to optimize costs and performance.