Practical example: Data management in AWS: A focus on CAP theorem and compression algorithms

Let us consider an example of a global e-commerce platform that runs on multiple cloud servers across the world. This platform handles thousands of transactions every second, and the data generated from these transactions needs to be stored and processed efficiently. We’ll see how the CAP theorem and compression algorithms can guide the design of the platform’s data management system.

1. Applying the CAP theorem

The CAP theorem states that a distributed data store cannot simultaneously provide more than two out of the following three guarantees: consistency, availability, and partition tolerance.

In our e-commerce platform scenario, availability and partition tolerance might be prioritized. High availability ensures that the system can continue processing transactions even if a few servers fail. Partition tolerance means the system can still function even if network...