Building reliable distributed systems at a worldwide scale requires trade-offs between consistency and availability. Amazon's cloud infrastructure, including services like S3, SimpleDB, and EC2, must meet strict requirements for security, scalability, availability, performance, and cost-effectiveness while serving millions of users globally. These systems are massive distributed systems that operate on a worldwide scale, requiring replication techniques to ensure consistent performance and high availability. However, replication introduces challenges, as customers may encounter consequences of using replication techniques. One key challenge is data consistency, particularly in distributed systems that use eventual consistency models. When designing large-scale systems at Amazon, the trade-offs between high availability and data consistency are carefully considered. The CAP theorem, introduced by Eric Brewer, states that in distributed systems, only two of the three properties—consistency, availability, and partition tolerance—can be achieved simultaneously. This means that systems must choose between consistency and availability, depending on their requirements. Eventual consistency is a model where, if no new updates are made to an object, all accesses will eventually return the last updated value. It is a form of weak consistency and is used in many systems, including the Domain Name System (DNS). There are various types of consistency models, such as causal consistency, read-your-writes consistency, session consistency, and monotonic consistency, each with different guarantees and trade-offs. Amazon's Dynamo is a key-value storage system that allows applications to make trade-offs between consistency, durability, availability, and performance. The choice of consistency model depends on the application's requirements, with some applications being able to handle slightly stale data. The goal of this article is to raise awareness about the complexity of engineering systems that operate at a global scale and the importance of careful tuning to ensure durability, availability, and performance.Building reliable distributed systems at a worldwide scale requires trade-offs between consistency and availability. Amazon's cloud infrastructure, including services like S3, SimpleDB, and EC2, must meet strict requirements for security, scalability, availability, performance, and cost-effectiveness while serving millions of users globally. These systems are massive distributed systems that operate on a worldwide scale, requiring replication techniques to ensure consistent performance and high availability. However, replication introduces challenges, as customers may encounter consequences of using replication techniques. One key challenge is data consistency, particularly in distributed systems that use eventual consistency models. When designing large-scale systems at Amazon, the trade-offs between high availability and data consistency are carefully considered. The CAP theorem, introduced by Eric Brewer, states that in distributed systems, only two of the three properties—consistency, availability, and partition tolerance—can be achieved simultaneously. This means that systems must choose between consistency and availability, depending on their requirements. Eventual consistency is a model where, if no new updates are made to an object, all accesses will eventually return the last updated value. It is a form of weak consistency and is used in many systems, including the Domain Name System (DNS). There are various types of consistency models, such as causal consistency, read-your-writes consistency, session consistency, and monotonic consistency, each with different guarantees and trade-offs. Amazon's Dynamo is a key-value storage system that allows applications to make trade-offs between consistency, durability, availability, and performance. The choice of consistency model depends on the application's requirements, with some applications being able to handle slightly stale data. The goal of this article is to raise awareness about the complexity of engineering systems that operate at a global scale and the importance of careful tuning to ensure durability, availability, and performance.