Apache Spark is a distributed cluster-computing framework designed for fast and general-purpose computation. It is an open-source technology originally developed by Berkeley's AMPLab at the University of California. It provides an easy-to-use interface for programming interactive queries and stream processing of data. What makes it a popular big data analytics tool is its implicit data parallelism, where it automates operation on data in parallel across processors in the computing cluster. Users only need to focus on how they like to manipulate the data without worrying about how data is distributed among all computing nodes, or which part of the data a node is responsible for.

Bear in mind that this book is mainly about machine learning. Hence, we will only brief on the fundamentals of Spark, including its components, installation, deployment, data structure, and core programming.