Book Image

Big Data Processing with Apache Spark

By : John Bura
Book Image

Big Data Processing with Apache Spark

By: John Bura

Overview of this book

Processing big data in real time is challenging due to scalability, information consistency, and fault-tolerance. Big Data Processing with Apache Spark teaches you how to use Spark to make your overall analytical workflow faster and more efficient. You'll explore all core concepts and tools within the Spark ecosystem, such as Spark Streaming, the Spark Streaming API, machine learning extension, and structured streaming. You'll begin by learning data processing fundamentals using Resilient Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs. After grasping these fundamentals, you'll move on to using Spark Streaming APIs to consume data in real time from TCP sockets, and integrate Amazon Web Services (AWS) for stream consumption. By the end of this course, you’ll not only have understood how to use machine learning extensions and structured streams but you’ll also be able to apply Spark in your own upcoming big data projects. The code bundle for this course is available at https://github.com/TrainingByPackt/Big-Data-Processing-with-Apache-Spark
Table of Contents (4 chapters)
Chapter 2
Introduction to Spark Streaming
Content Locked
Section 2
Introduction to Streaming Architectures
Consuming live streams of data is a challenging endeavor, one of the reasons being the volume of the incoming data. The variability in the flow of information may lead to situations where very fast producers may overwhelm consumers. Let's learn to find the right balance between reads and writes through some concepts.