Amazon S3 is a monumental system, capable of handling hundreds of millions of transactions per second and storing over 500 trillion objects. The engineering behind this scale is impressive; when S3 launched in 2006, it used eventual consistency, but the team managed a significant upgrade to strong consistency without compromising availability or costs. They achieved this through a replicated journal and a unique cache coherency protocol that tolerates failures while maintaining performance. The article highlights the shift to Rust for performance-critical components, reflecting a growing engineering focus on optimizing speed and reducing latency. S3 boasts an extraordinary durability claim of 11 nines (99.999999999%), backed by continuous auditing and automatic repair systems, which assume that server failures are a constant reality. Formal methods play a critical role in ensuring code correctness, particularly in areas like consistency and cross-region replication, where automated proofs verify that updates don’t regress the system's reliability. Correlated failures pose a significant risk, prompting S3’s architecture to design against them by replicating data across multiple availability zones and employing quorum-based algorithms. Interestingly, S3 operates with around 200 microservices, considerably fewer than systems like Uber, suggesting that effective service design can vary widely. The article also introduces S3 Vectors, a new data structure aimed at efficient searching in high-dimensional spaces, which employs precomputed neighborhoods to enhance query performance.