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Answers to questions related to Spark, Hive, and Data Frames
Optimization techniques in Spark and Hive include partitioning, bucketing, and caching
Partitioning divides data into smaller, more manageable chunks while bucketing groups data based on a specific column
Adding a column to a Data Frame can be done using the 'withColumn' method
Caching stores data in memory for faster access while persistence stores data on disk
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