Big Data Processing with Apache Spark
As seen in the preceding chapter, Apache Spark has rapidly become one of the most widely used distributed data processing engines for big data workloads. In this chapter, we will cover the fundamentals of using Spark for large-scale data processing.
We’ll start by discussing how to set up a local Spark environment for development and testing. You’ll learn how to launch an interactive PySpark shell and use Spark’s built-in DataFrames API to explore and process sample datasets. Through coding examples, you’ll gain practical experience with essential PySpark data transformations such as filtering, aggregations, and joins.
Next, we’ll explore Spark SQL, which allows you to query structured data in Spark via SQL. You’ll learn how Spark SQL integrates with other Spark components and how to use it to analyze DataFrames. We’ll also cover best practices for optimizing Spark workloads. While we won&...
