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How can you minimize data transfers when working with Spark?
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Answer / Manish Verma
To minimize data transfers in Spark, follow these best practices:n1. Partitioning: Properly partition your RDDs to reduce the amount of data that needs to be shuffled between tasks.n2. Caching and Persistence: Cache frequently accessed datasets to keep them in memory, reducing the need for re-reading data from storage.n3. Broadcast Variables: Use broadcast variables to share large datasets across executors without replicating them.n4. Coalescing: Use coalescing to merge small files into larger ones before processing, reducing the number of reads and writes.
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