How does Spark achieve fault tolerance in its distributed data processing?
- Checkpointing
- Data Replication
- Error Handling
- Redundant Processing
Spark achieves fault tolerance through checkpointing. Periodically, Spark saves the state of the distributed computation to a reliable distributed file system, allowing it to recover lost data and continue processing in the event of a node failure.
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