Large-Scale Data Analytics with Python and Spark A Hands-on Guide to Implementing Machine Learning Solutions

Spark SQL is a module in Spark for structured data processing, which improves upon RDDs. The chapter explains how imposing structure on the data helps Spark perform further optimizations. We talk about the transition from RDDs to DataFrames and Datasets, including a brief description of the Catalyst and Tungsten projects. In Python, we don’t have Datasets, and we focus on DataFrames. With a learn-by-example approach, we see how to create DataFrames, inferring the schema automatically or manually, and operate with them. We show how these operations usually feel more natural for SQL developers, but we can interact with this API following an object-oriented programming style or SQL. Like we did with RDDs, we showcase various examples to demonstrate the functioning of different operations with DataFrames. Starting from standard transformations such as select or filter, we move to more peculiar operations like Column transformations and how to perform efficient aggregations using Spark functions. As advanced content, we include implementing user-defined functions for DataFrames, as well as an introduction to pandas-on-Spark, a powerful API for those programmers more used to pandas.