The examples have been implemented using Scala, Java, R, and Python on a Linux 64-bit. You will also need, or be prepared to install, the following on your machine (preferably the latest version):

• Spark 2.3.0 (or higher)
• Hadoop 3.1 (or higher)
• Flink 1.4
• Java (JDK and JRE) 1.8+
• Scala 2.11.x (or higher)
• Python 2.7+/3.4+
• R 3.1+ and RStudio 1.0.143 (or higher)
• Eclipse Mars or Idea IntelliJ (latest)

Regarding the operating system: Linux distributions are preferable (including Debian, Ubuntu, Fedora, RHEL, and CentOS) and, to be more specific, for example, as regards Ubuntu, it is recommended having a complete 14.04 (LTS) 64-bit (or later) installation, VMWare player 12, or Virtual box. You can also run code on Windows (XP/7/8/10) or macOS X (10.4.7+).

Regarding hardware configuration: Processor Core i3, Core i5 (recommended) ~ Core i7 (to get the best result). However, multicore processing would provide faster data processing and scalability. At least 8 GB RAM (recommended) for a standalone mode. At least 32 GB RAM for a single VM and higher for cluster. Enough storage for running heavy jobs (depending on the dataset size you will be handling) preferably at least 50 GB of free disk storage (for stand alone and SQL warehouse).

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Big-Data-Analytics-with-Hadoop-3. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/BigDataAnalyticswithHadoop3_ColorImages.pdf.

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "This file, temperatures.csv, is available as a download and once downloaded, you can move it into hdfs by running the command, as shown in the following code."

A block of code is set as follows:

hdfs dfs -copyFromLocal temperatures.csv /user/normal

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

Map-Reduce Framework -- output average temperature per city name
    Map input records=35
    Map output records=33
    Map output bytes=208
    Map output materialized bytes=286

Any command-line input or output is written as follows:

$ ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
$ chmod 0600 ~/.ssh/authorized_keys

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Clicking on the Datanodes tab shows all the nodes."