How does partitioning of data contribute to load balancing and fault tolerance in a distributed DBMS? Provide examples.

Dividing data into a Distributed Database Management System (DBMS) is essential for improving load balancing and fault tolerance.

This technique involves breaking down a data set into partitions and distributing them across multiple servers or nodes within the distributed system.

It offers benefits in terms of performance and reliability.

Load Balancing: Data partitioning ensures a distribution of query and transaction workloads across the nodes of a distributed DBMS.

This prevents any node from becoming overloaded with data access requests.

Consequently, the system can effectively use its resources, ensuring that all nodes contribute to processing queries and improving response times.

Fault Tolerance: Hardware failures or network issues can occur in a distributed DBMS.

Data partitioning enhances fault tolerance by replicating or storing data across nodes.

When a node fails, the system can seamlessly redirect queries to nodes with copies of the data, reducing downtime and minimizing data loss.