Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By : Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei
Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By: Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei

Overview of this book

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: • Mastering Apache Spark 2.x by Romeo Kienzler • Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla • Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook
Table of Contents (23 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Streaming sources


We will not be able to cover all the stream types with practical examples in this section, but where this chapter is too small to include code, we will at least provide a description. In this chapter, we will cover the TCP and file streams and the Flume, Kafka, and Twitter streams. Apache Spark tends only to support this limited set out of the box, but this is not a problem since 3rd party developers provide connectors to other sources as well. We will start with a practical TCP-based example. This chapter examines stream processing architecture.

Note

For instance, what happens in cases where the stream data delivery rate exceeds the potential data processing rate? Systems such as Kafka provide the possibility of solving this issue by caching data until it is requested with the additional ability to use multiple data topics and consumers (publish-subscribe model).

TCP stream

There is a possibility of using the Spark Streaming Context method called socketTextStream to stream...