The CAP Theorem is an important consideration when designing a distributed system. CAP stands for Consistency, Availability, and Partition Tolerance. The Consistency property states that all users must see the same data at the same time, regardless of the node to which they are connected. The Availability property ensures that all requests receive a response, even when some nodes are offline. Lastly, the Partition Tolerance property stipulates that a system should continue functioning despite network partitions causing communication loss between nodes. The CAP Theorem asserts that a distributed system cannot simultaneously support all three properties; it must sacrifice one to support the remaining two. This implies that there are three possible types of distributed systems: consistent-partition-tolerant (CP), availability-partition-tolerant (AP), and consistent-availability (CA). In practice, network partition is assumed to occur at some point, so distributed systems must be partition-tolerant. Since Partition Tolerance cannot be sacrificed, CA systems do not exist in practice. A CP system is consistent and partition-tolerant, where data inconsistency is unacceptable. After data is updated, the changes are replicated across all nodes with minimal delay in between. It's important to note that the consistency in CAP does not refer to strong consistency but to eventual consistency, which means that data will eventually be replicated to all nodes. This gap is generally small and considered acceptable. An AP system offers high availability and is partition-tolerant. Data inconsistency is acceptable within an AP system. The primary goal of an AP system is to ensure the successful completion of all requests. This does not necessarily mean that the requests were fulfilled correctly, as some requests may have been processed with stale data. Businesses with AP systems may need to address these discrepancies after the fact.