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Apache Spark Quick Start Guide
Apache Spark Quick Start Guide

Apache Spark Quick Start Guide: Quickly learn the art of writing efficient big data applications with Apache Spark

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Profile Icon Shrey Mehrotra Profile Icon Grade
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Paperback Jan 2019 154 pages 1st Edition
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Arrow left icon
Profile Icon Shrey Mehrotra Profile Icon Grade
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€18.99 per month
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
Paperback Jan 2019 154 pages 1st Edition
eBook
€13.98 €19.99
Paperback
€24.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€13.98 €19.99
Paperback
€24.99
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Renews at €18.99p/m

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Apache Spark Quick Start Guide

Apache Spark Installation

In Chapter 1, Introduction to Apache Spark, we learned about what Spark is, its architecture, and the different components that are provided by Spark. In this chapter, we will configure Spark in different modes and look into the different APIs that we can use to access Spark clusters or to submit a Spark application. This chapter will cover the following topics:

  • Creating a single node (EC2 Linux instance) on AWS cloud
  • Installing Java on an instance
  • Installing Python on an instance
  • Installing Scala on an instance
  • Installing Spark on an instance
  • How to access different Spark component clients on an instance

AWS elastic compute cloud (EC2)

Amazon Web Service (AWS) is a popular cloud platform that provides various offerings for infrastructure as a service (IAAS), platform as a service (PAAS), and software as a service (SAAS). AWS provides scalable EC2 instances as nodes (machines) with configurable resources (RAM and cores). It also provides Simple Storage Service (S3) as scalable, low-cost storage.

Creating a free account on AWS

AWS provides you with a free account so that you can explore different services. For more details about these free services, please visit https://aws.amazon.com/free . You will see the following screen:

Follow these steps to get your free EC2 node for Spark installation:

  1. Sign up for an AWS account...

Configuring Spark

Apache Spark can be configured in the following modes:

  • Standalone mode
  • Distributed or cluster mode

Prerequisites

The following are the prerequisites that are required for configuring Spark in any of the modes:

  • Linux OS: Spark is most compatible with any flavor of the Linux operating system. You can use any desktop, virtual machine, server, or a cloud-scale machine to install Spark. Although you can install it on your Windows machine, we are using the Ubuntu AWS machine for configuring Spark.
  • Scala/Python/Java: Spark supports APIs in multiple languages including Scala, Python, and Java. All Spark actions and transformations APIs are available in these different languages.
...

Summary

This chapter has helped you install Java, Scala, and Spark on a Linux machine that has been procured from an AWS EC2 instance. You can now use the same setup to execute the queries/examples that are provided in other chapters of this book.

In the next chapter, you will learn about the basic unit of Spark, which is RDD. RDD refers to an immutable Resilient Distributed Dataset, on which we can apply further actions and transformations.

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Key benefits

  • Learn about the core concepts and the latest developments in Apache Spark
  • Master writing efficient big data applications with Spark’s built-in modules for SQL, Streaming, Machine Learning and Graph analysis
  • Get introduced to a variety of optimizations based on the actual experience

Description

Apache Spark is a ?exible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases. It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis. Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications.

Who is this book for?

If you are a big data enthusiast and love processing huge amount of data, this book is for you. If you are data engineer and looking for the best optimization techniques for your Spark applications, then you will find this book helpful. This book also helps data scientists who want to implement their machine learning algorithms in Spark. You need to have a basic understanding of any one of the programming languages such as Scala, Python or Java.

What you will learn

  • Learn core concepts such as RDDs, DataFrames, transformations, and more
  • Set up a Spark development environment
  • Choose the right APIs for your applications
  • Understand Spark's architecture and the execution ?ow of a Spark application
  • Explore built-in modules for SQL, streaming, ML, and graph analysis
  • Optimize your Spark job for better performance

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 31, 2019
Length: 154 pages
Edition : 1st
Language : English
ISBN-13 : 9781789349108
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Product Details

Publication date : Jan 31, 2019
Length: 154 pages
Edition : 1st
Language : English
ISBN-13 : 9781789349108
Vendor :
Apache
Category :
Languages :
Concepts :

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Table of Contents

9 Chapters
Introduction to Apache Spark Chevron down icon Chevron up icon
Apache Spark Installation Chevron down icon Chevron up icon
Spark RDD Chevron down icon Chevron up icon
Spark DataFrame and Dataset Chevron down icon Chevron up icon
Spark Architecture and Application Execution Flow Chevron down icon Chevron up icon
Spark SQL Chevron down icon Chevron up icon
Spark Streaming, Machine Learning, and Graph Analysis Chevron down icon Chevron up icon
Spark Optimizations Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Ulysses May 02, 2019
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I basically got what I wanted, which was a quick overview, with an intro to key concepts. Also some of the details addressed questions I had. I scouted a bunch of other books first, and it was the table-of-contents that sold me on this one.The problem is that this feels like a first draft that nobody bothered to read through. I doubt that it's an exaggeration to say that the average page has a dozen glaring syntax problems that could easily have been fixed. The figures are often poorly labelled, or even poorly conceived. There are extra or missing words. It might be that I actually learned a little more than I would have otherwise, because at a few places I had to decipher some garbled text to work out what the authors must have meant, whereas cleaned-up text might've gone in one ear and out the other.The concepts are accessible. The scope seems thorough, for a quick introduction. It's not so bad that you can't breeze your way through it, but it feels like a hasty job slapped into print without any supervision.
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