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Optimization techniques in Spark involve tuning parameters, partitioning data, and using caching.
Tune parameters like memory allocation, parallelism, and shuffle partitions for better performance
Partition data to distribute workload evenly across nodes
Use caching to store intermediate results in memory for faster access
Examples: repartition(), cache(), persist() functions in Spark
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posted on 2 Nov 2021
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posted on 18 May 2018
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