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)

Summary

In this chapter, you saw how difficult the testing and debugging your Spark applications are. These can even be more critical in a distributed environment. We also discussed some advanced ways to tackle them altogether. In summary, you learned the way of testing in a distributed environment. Then you learned a better way of testing your Spark application. Finally, we discussed some advanced ways of debugging Spark applications.

We believe that this book will help you to gain some good understanding of Spark. Nevertheless, due to page limitation, we could not cover many APIs and their underlying functionalities. If you face any issues, please don't forget to report this to Spark user mailing list at [email protected]. Before doing so, make sure that you have subscribed to it.

This is more or less the end of our little journey with advanced topics on Spark. Now...