Design, process, and analyze large sets of complex data in real timeAbout This Book<ul>
<li>Get acquainted with transformations and database-level interactions, and ensure the reliability of messages processed using Storm</li>
<li>Implement strategies to solve the challenges of real-time data processing</li>
<li>Load datasets, build queries, and make recommendations using Spark SQL</li>
</ul>Who This Book Is ForIf you are a Big Data architect, developer, or a programmer who wants to develop applications/frameworks to implement real-time analytics using open source technologies, then this book is for you.What You Will Learn<ul>
<li>Explore big data technologies and frameworks</li>
<li>Work through practical challenges and use cases of real-time analytics versus batch analytics</li>
<li>Develop real-word use cases for processing and analyzing data in real-time using the programming paradigm of Apache Storm</li>
<li>Handle and process real-time transactional data</li>
<li>Optimize and tune Apache Storm for varied workloads and production deployments</li>
<li>Process and stream data with Amazon Kinesis and Elastic MapReduce</li>
<li>Perform interactive and exploratory data analytics using Spark SQL</li>
<li>Develop common enterprise architectures/applications for real-time and batch analytics</li>
</ul>In DetailEnterprise has been striving hard to deal with the challenges of data arriving in real time or near real time.
Although there are technologies such as Storm and Spark (and many more) that solve the challenges of real-time data, using the appropriate technology/framework for the right business use case is the key to success. This book provides you with the skills required to quickly design, implement and deploy your real-time analytics using real-world examples of big data use cases.
From the beginning of the book, we will cover the basics of varied real-time data processing frameworks and technologies. We will discuss and explain the differences between batch and real-time processing in detail, and will also explore the techniques and programming concepts using Apache Storm.
Moving on, we'll familiarize you with “Amazon Kinesis” for real-time data processing on cloud. We will further develop your understanding of real-time analytics through a comprehensive review of Apache Spark along with the high-level architecture and the building blocks of a Spark program.
You will learn how to transform your data, get an output from transformations, and persist your results using Spark RDDs, using an interface called Spark SQL to work with Spark.
At the end of this book, we will introduce Spark Streaming, the streaming library of Spark, and will walk you through the emerging Lambda Architecture (LA), which provides a hybrid platform for big data processing by combining real-time and precomputed batch data to provide a near real-time view of incoming data.Style and approachThis step-by-step is an easy-to-follow, detailed tutorial, filled with practical examples of basic and advanced features.
Each topic is explained sequentially and supported by real-world examples and executable code snippets.

Real-Time Big Data Analytics: Design, Process, and Analyze Large Sets of Complex Data in Real Time

Computers & Technology

Gupta, Sumit

Saxena, Shilpi

Networking & Cloud Computing

Packt Pub Ltd

A practical guide to help you tackle different real-time data processing and analytics problems using the best tools for each scenarioAbout This Book<ul>
<li>Learn about the various challenges in real-time data processing and use the right tools to overcome them</li>
<li>This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems</li>
<li>A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time</li>
</ul>Who This Book Is ForIf you are a Java developer who would like to be equipped with all the tools required to devise an end-to-end practical solution on real-time data streaming, then this book is for you. Basic knowledge of real-time processing would be helpful, and knowing the fundamentals of Maven, Shell, and Eclipse would be great.What You Will Learn<ul>
<li>Get an introduction to the established real-time stack</li>
<li>Understand the key integration of all the components</li>
<li>Get a thorough understanding of the basic building blocks for real-time solution designing</li>
<li>Garnish the search and visualization aspects for your real-time solution</li>
<li>Get conceptually and practically acquainted with real-time analytics</li>
<li>Be well equipped to apply the knowledge and create your own solutions</li>
</ul>In DetailWith 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.Style and Approach In this practical guide to real-time analytics, each chapter begins with a basic high-level concept of the topic, followed by a practical, hands-on implementation of each concept, where you can see the working and execution of it. The book is written in a DIY style, with plenty of practical use cases, well-explained code examples, and relevant screenshots and diagrams.

Practical Real-time Data Processing and Analytics

Gupta, Saurabh

Databases & Big Data

Packt Publishing

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