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

The NRT system and its building blocks


The first and foremost question that strikes us here is "when do we call an application an NRT application?" The simple and straightforward answer to this is a software application that is able to consume, process, and generate results very close to real-time; that is, the lapse between the time the event occurred to the time results arrived is very small, an order of a few nanoseconds to at most a couple of seconds.

It's very important to understand the key aspects where the traditional monolithic application systems are falling short to serve the need of the hour:

  • Backend DB: Single point monolithic data access.
  • Ingestion flow: The pipelines are complex and tend to induce latency in end to end flow.
  • Failure & Recovery: The Systems are failure prone, but the recovery approach is difficult and complex.
  • Synchronization and state capture: It's very difficult to capture and maintain the state of facts and transactions in the system. Getting diversely distributed...