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Practical Real-time Data Processing and Analytics
Practical Real-time Data Processing and Analytics

Practical Real-time Data Processing and Analytics: Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Profile Icon Shilpi Saxena Profile Icon Saurabh Gupta
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$54.99
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Paperback Sep 2017 360 pages 1st Edition
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Arrow left icon
Profile Icon Shilpi Saxena Profile Icon Saurabh Gupta
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$54.99
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1 (1 Ratings)
Paperback Sep 2017 360 pages 1st Edition
eBook
$9.99 $43.99
Paperback
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Free Trial
Renews at $19.99p/m
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$9.99 $43.99
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Practical Real-time Data Processing and Analytics

Real Time Applications – The Basic Ingredients

This chapter gets you acquainted with the basic building blocks of near real-time (NRT) systems. We introduce you to a high-level logical, physical, and technical view of such applications, and will touch upon various technology choices for each building block of the system:

  • NRT system and its building blocks
  • Data Collection
  • Stream processing
  • Analytical layer – serve it to the end user
  • NRT – high-level system views
  • NRT – technology views

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...

NRT – high-level system view

The previous section of this chapter is dedicated to providing you with an understanding of the basic building blocks of an NRT application and its logical overview. The next step is to understand the functional and systems view of the NRT architectural framework. The following figure clearly outlines the various architectural blocks and cross cutting concerns:

So, if I get to describe the system as a horizontal scale from left to right, the process starts with data ingestion and transformation in near real-time using low-latency components. The transformed data is passed on to the next logical unit that actually performs highly optimized and parallel operations on the data; this unit is actually the near real-time processing engine. Once the data has been aggregated and correlated and actionable insights have been derived, it is passed on...

NRT – technology view

In this section, we introduce you to various technological choices for NRT components and their pros and cons in certain situations. As the book progresses, we will revisit this section in more detail to help you understand why certain tools and stacks are better suited to solving certain use cases.

Before moving on, it's very important to understand the key aspects against which all the tools and technologies are generally evaluated. The aspects mentioned here are generic to software, we move on to the specifics of NRT tools later:

  • Performance: This is basically gauging the performance of the software component on a given set of hardware at a given load.
  • Capacity: This is a very crucial aspect because it decides the breaking point for any application.
  • Management: How easy or cumbersome is the management of the component? Would I need specialized...

Summary

In this chapter we got you acquainted and introduced to various components of the NRT architectural framework and technology choices for it. You gained understanding of the challenges of real-time and the key aspects to be considered and the USPs of each technology available in stack. The intent here was to get you familiarized with the available choices in terms of tools and tech stack conceptually, so that you can pick and choose what works best for your use-case solutions, depending upon your functional and non-functional requirements.

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Key benefits

  • • Learn about the various challenges in real-time data processing and use the right tools to overcome them
  • • This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems
  • • A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time

Description

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.

Who is this book for?

If 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

  • • Get an introduction to the established real-time stack
  • • Understand the key integration of all the components
  • • Get a thorough understanding of the basic building blocks for real-time solution designing
  • • Garnish the search and visualization aspects for your real-time solution
  • • Get conceptually and practically acquainted with real-time analytics
  • • Be well equipped to apply the knowledge and create your own solutions
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Table of Contents

13 Chapters
Introducing Real-Time Analytics Chevron down icon Chevron up icon
Real Time Applications – The Basic Ingredients Chevron down icon Chevron up icon
Understanding and Tailing Data Streams Chevron down icon Chevron up icon
Setting up the Infrastructure for Storm Chevron down icon Chevron up icon
Configuring Apache Spark and Flink Chevron down icon Chevron up icon
Integrating Storm with a Data Source Chevron down icon Chevron up icon
From Storm to Sink Chevron down icon Chevron up icon
Storm Trident Chevron down icon Chevron up icon
Working with Spark Chevron down icon Chevron up icon
Working with Spark Operations Chevron down icon Chevron up icon
Spark Streaming Chevron down icon Chevron up icon
Working with Apache Flink Chevron down icon Chevron up icon
Case Study Chevron down icon Chevron up icon

Customer reviews

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Annyman Jun 12, 2018
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One of the worst books written on Real Time analytics. It has only architecture diagrams and long codes. The codes are not readable and the flow in the book is absolutely bad.
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