Fault tolerance in Spark Structured Streaming is solely achieved through the replication of data across multiple nodes, without the need for checkpointing.

Checkpointing is a technique that saves the state of the streaming application at regular intervals to a reliable storage system, enabling the application to recover from failures by restarting from the last saved state, thus minimizing data loss and ensuring continuous processing.

Checkpointing can be enabled by setting the 'recovery' option in the Spark configuration, which automatically saves all processed data to disk for recovery purposes.

The primary benefit of checkpointing is to increase the processing speed of the streaming application by caching intermediate results in memory, with fault tolerance being a secondary advantage.