Book Image

Scala and Spark for Big Data Analytics

By : Md. Rezaul Karim, Sridhar Alla
Book Image

Scala and Spark for Big Data Analytics

By: Md. Rezaul Karim, Sridhar Alla

Overview of this book

Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.
Table of Contents (19 chapters)

Here comes Apache Spark

Apache Spark is a unified distributed computing engine across different workloads and platforms. Spark can connect to different platforms and process different data workloads using a variety of paradigms such as Spark streaming, Spark ML, Spark SQL, and Spark GraphX.

Apache Spark is a fast in-memory data processing engine with elegant and expressive development APIs to allow data workers to efficiently execute streaming machine learning or SQL workloads that require fast interactive access to data sets. Apache Spark consists of Spark core and a set of libraries. The core is the distributed execution engine and the Java, Scala, and Python APIs offer a platform for distributed application development. Additional libraries built on top of the core allow workloads for streaming, SQL, Graph processing, and machine learning. Spark ML, for instance, is designed...