Apache Spark is a powerful open source processing engine originally developed by Matei Zaharia as a part of his PhD thesis while at UC Berkeley. The first version of Spark was released in 2012. Since then, in 2013, Zaharia co-founded and has become the CTO at Databricks; he also holds a professor position at Stanford, coming from MIT. At the same time, the Spark codebase was donated to the Apache Software Foundation and has become its flagship project.
Apache Spark is fast, easy to use framework, that allows you to solve a wide variety of complex data problems whether semi-structured, structured, streaming, and/or machine learning / data sciences. It also has become one of the largest open source communities in big data with more than 1,000 contributors from 250+ organizations and with 300,000+ Spark Meetup community members in more than 570+ locations worldwide.
In this chapter, we will provide a primer to understanding Apache Spark. We will explain the concepts behind Spark Jobs and APIs, introduce the Spark 2.0 architecture, and explore the features of Spark 2.0.
The topics covered are:
What is Apache Spark?
Spark Jobs and APIs
Review of Resilient Distributed Datasets (RDDs), DataFrames, and Datasets
Review of Catalyst Optimizer and Project Tungsten
Review of the Spark 2.0 architecture