Part 3: Spark Operations

In this part, we will cover Spark DataFrames and their operations, emphasizing their role in structured data processing and analytics. This will include DataFrame creation, manipulation, and various operations such as filtering, aggregations, joins, and groupings, demonstrated through illustrative examples. Then, we will discuss advanced operations and optimization techniques, including broadcast variables, accumulators, and custom partitioning. This part also talks about performance optimization strategies, highlighting the significance of adaptive query execution and offering practical tips for enhancing Spark job performance. Furthermore, we will explore SQL queries in Spark, focusing on its SQL-like querying capabilities and interoperability with the DataFrame API. Examples will illustrate complex data manipulations and analytics through SQL queries in Spark.

This part has the following chapters:

• Chapter 4, Spark DataFrames and their Operations...