We will start this book with the basics of Spark  SQL and its role in Spark applications. After the initial familiarization with Spark SQL, we will focus on using Spark SQL to execute tasks that are common to all big data projects, such as working with various types of data sources, exploratory data analysis, and data munging. We will also see how Spark SQL and SparkR can be leveraged to accomplish typical data science tasks at scale.
With the DataFrame/Dataset API and the Catalyst optimizer at the heart of Spark SQL, it is no surprise that it plays a key role in all applications based on the Spark technology stack. These applications include large-scale machine learning pipelines, large-scale graph applications, and emerging Spark-based deep learning applications. Additionally, we will present Spark SQL-based Structured Streaming applications that are deployed in complex production environments as continuous applications.
We will also review performance tuning in Spark SQL applications, including cost-based optimization (CBO) introduced in Spark 2.2. Finally, we will present application architectures that leverage Spark modules and Spark SQL in real-world applications. More specifically, we will cover key architectural components and patterns in large-scale Spark applications that architects and designers will find useful as building blocks for their own specific use cases.
