In Distributed Systems, the State is replicated and not shared, which threatens Consistency. With a lot of variables like network, malicious nodes, failures, and growing load, it needs much care and protocols to maintain consistency. Strong consistency can be maintained with sequential processing but in real-time, it's not possible. 'Linearizability' achieves consistency and portrays the distributed system as a single node, allowing total and real-time ordering. This model with basic ACID properties would work wonders. Robust mechanisms in Checkpointing and view change are necessary to maintain consistency across nodes, allowing them to share data agree upon a value, and take it forward throughout the system.

Choose between ACID and BASE properties based on the tolerance for delays: 1.ACID for strong consistency. 2.BASE for eventual consistency and high availability. Manage transactions with protocols like: Two-Phase Commit (2PC) for atomicity, though it can block. Three-Phase Commit (3PC) for non-blocking and better fault tolerance. Use data replication strategies such as: Leader-Follower Replication where a leader handles writes and followers replicate data. Services like Google Spanner (used in the F1 database) can help achieve strong consistency with global clocks. Implement data versioning, as seen in PostgreSQL, to ensure consistency while enabling concurrent operations through techniques like MVCC.

Replication Protocols: Use protocols like Paxos or Raft to ensure data is consistently replicated across nodes. Database Transactions: Use ACID transactions to ensure that updates are consistent and reliable. Eventual Consistency: Allow data to become consistent over time, where immediate consistency isn't required. Distributed Locks: Use locks to prevent multiple nodes from making conflicting changes to the same data.

This depends on the application and user needs. There are 2 forms of consistency. 1. Strong: All nodes in your system need to agree on a piece of data before confirming it. This is super useful when processing financial transactions or booking systems, where a double-booking can ruin a customer's experience. 2. Eventual: Think of your group chat where messages don’t always appear in real time but eventually show up. It’s perfect for scenarios where speed is key, and a slight delay in data accuracy won’t break the system. You need to have a balance between the 2 for specific parts of your app and also implement Quorom based R/W and Consistent hashing for effective data distribution across nodes.