Book Image

Practical Real-time Data Processing and Analytics

Book Image

Practical Real-time Data Processing and Analytics

Overview of this book

With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you’ll be equipped with a clear understanding of how to solve challenges on your own. We’ll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You’ll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Summary


In this chapter, we introduced the readers to Apache Spark compute engine, and we talked about the various components of Spark framework and its schedulers. We touched upon the advantages that provide edge to spark as a scalable, high performing compute engine. The users were also acquainted with the not-so sparkling features of Spark—when it's not the best fit to be used. We walked the users through some practical realtime industry wide use cases. Next we got into the layered architecture of Spark and its internal working across the cluster. In the end we touched upon the pragmatic concepts of Spark: RDD, DataFrame, and datasets.

The next chapter will touch upon the spark APIs and execution of all components through working code blocks.