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How can data transfer be minimized when working with Apache Spark?
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Answer / Jatin Girdhar
Data transfer can be minimized in Apache Spark by using techniques such as data partitioning, caching, and persistence. Partitioning splits the data into smaller chunks that can be processed independently, reducing the amount of data transferred between nodes. Caching stores RDDs (Resilient Distributed Datasets) in memory for faster access during subsequent tasks, while persistence stores DataFrames or Datasets on an external storage system like HDFS.
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