In advanced Hadoop deployments, how is batch processing optimized for performance?
- Increasing block size
- Leveraging in-memory processing
- Reducing replication factor
- Using smaller Hadoop clusters
In advanced Hadoop deployments, batch processing is often optimized for performance by leveraging in-memory processing. This involves storing intermediate data in memory rather than writing it to disk, reducing the time needed for data access and improving overall processing speed. In-memory processing is a key strategy for enhancing the performance of batch processing jobs in Hadoop.
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