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Answer: Use DataFrames for all transformations and optimize performance by caching frequently accessed data and using appropriate join strategies.
The correct approach is to use DataFrames for all transformations, as they provide a higher-level API and better performance optimizations. Caching frequently accessed data and using appropriate join strategies can further optimize the performance of the transformations.
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You are working on a data transformation project using Apache Spark in Azure Databricks. The project involves joining two large datasets, 'Sales' and 'Products', on the 'ProductID' field and then performing a series of transformations, including filtering, aggregation, and sorting. Which Spark API would you use to achieve this, and how would you optimize the performance of the transformations?
A
Use RDDs for all transformations and optimize performance by increasing the number of partitions.
B
Use DataFrames for all transformations and optimize performance by caching frequently accessed data and using appropriate join strategies.
C
Use Datasets for all transformations and optimize performance by reducing the number of partitions.
D
Use RDDs for all transformations and optimize performance by reducing the number of partitions.
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