There are several ways to handle a sudden increase in data volume on a Hadoop cluster:   Scale Up: Add more resources (such as nodes) to the existing cluster to handle the increased data volume.   Scale Out: Add more clusters to handle the increased data volume.   Partitioning: Divide the data into smaller chunks and distribute them across multiple nodes.   Compression: Compress the data to reduce its size and decrease the amount of storage required.  Data Archiving: Move infrequently used data to a separate storage system to free up space on the main cluster.   Data Deletion: Remove unnecessary or redundant data from the cluster.   It also depends on the data's access pattern, if it is write-heavy then we can go for more storage or if it is read-heavy then we can go for more processing power. Overall, the approach to handling a sudden increase in data volume will depend on the specific use case and the resources available.