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A distributed database is a collection of multiple, interconnected databases distributed across a network, but managed as a single database system.

Data fragmentation refers to the process of dividing a database into smaller pieces that can be managed and accessed more efficiently.

Data replication is the process of storing copies of data on multiple servers to ensure data availability and improve read performance.

Data sharding is a type of data partitioning where data is split across multiple databases or tables to improve performance and management.

ACID stands for Atomicity, Consistency, Isolation, and Durability, which are properties that ensure reliable transaction processing in database systems.

The CAP Theorem states that a distributed database system can only guarantee two out of the following three properties at the same time: Consistency, Availability, and Partition Tolerance.

The Two-phase Commit Protocol is a type of consensus protocol used in distributed systems to ensure all or none of the databases commit a transaction in a coordinated manner.

Horizontal scaling, also known as scale-out, involves adding more nodes to a system to handle increased load rather than increasing the capacity of existing nodes.

BASE stands for Basically Available, Soft state, and Eventually consistent, which are properties of systems that prioritize availability over consistency in distributed databases.

Vector clocks are a mechanism for achieving version control in distributed systems by keeping track of the relative ordering of events rather than absolute time.

Distributed transactions involve a series of database operations spread across multiple databases within a distributed system, which must be completed successfully as a unit.