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Spark uses various optimization techniques like predicate pushdown, column pruning, and shuffle optimization.
Predicate pushdown: Filters are pushed down to the data source to reduce the amount of data to be processed.
Column pruning: Only necessary columns are selected to minimize data transfer and processing.
Shuffle optimization: Minimizing data shuffling during joins and aggregations to improve performance.
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