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)

Spark machine learning APIs

In this section, we will describe two key concepts introduced by the Spark machine learning libraries (Spark MLlib and Spark ML) and the most widely used implemented algorithms that align with the supervised and unsupervised learning techniques we discussed in the previous sections.

Spark machine learning libraries

As already stated, in the pre-Spark era, big data modelers typically used to build their ML models using statistical languages such as R, STATA, and SAS. However, this kind of workflow (that is, the execution flow of these ML algorithms) lacks efficiency, scalability, and throughput, as well as accuracy, with, of course, extended execution times.

Then, data engineers used to reimplement...