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)

Interoperability with streaming platforms (Apache Kafka)

Spark Streaming has very good integration with Apache Kafka, which is the most popular messaging platform currently. Kafka integration has several approaches, and the mechanism has evolved over time to improve the performance and reliability.

There are three main approaches for integrating Spark Streaming with Kafka:

  • Receiver-based approach
  • Direct stream approach
  • Structured streaming

Receiver-based approach

The receiver-based approach was the first integration between Spark and Kafka. In this approach, the driver starts receivers on the executors that pull data using high-level APIs, from Kafka brokers. Since receivers are pulling events from Kafka brokers, receivers...