Event Sourcing in Distributed Systems: Architecture, Consistency, and Fault-Tolerant Projections

If you've worked with a relational database for any length of time, you know the drill: state lives in a row, and every UPDATE overwrites whatever was there before. It's simple, it's fast to reason about, and it throws away history by design. Update a bank balance from $1,000 to $900, and the fact that a withdrawal happened and why is gone unless some separate audit log happened to catch it.

Event sourcing flips that model on its head. Instead of storing current state, you store the sequence of events that produced it, and you derive state on demand by replaying the log. History isn't a side effect you bolt on afterward; it is the write path.