Explanation:
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.
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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