Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. Spark’s “native” language is Scala, but you can use language APIs to run Spark code from other programming languages (for example, Java, R, and Python). In this book, I teach you how to use PySpark to solve big data problems in Spark. In this book, you will learn how to solve your big data problems in Spark by expressing your solution in PySpark. You will lean how to read your data and represent it as an RDD and DataFrame. RDD is a fundamental data abstraction of Spark. DataFrame (a distributed table of rows with named columns) in Spark allows developers to impose a structure onto a distributed collection of data, allowing higher-level abstraction.