We are currently facing an avalanche of data, and this data contains many insights that hold the keys to success or failure in the data-driven world. Next generation Hadoop (v2) offers a cutting-edge platform to store and analyze these massive data sets and improve upon the widely used and highly successful Hadoop MapReduce v1. The recipes that will help you analyze large and complex datasets with next generation Hadoop MapReduce will provide you with the skills and knowledge needed to process large and complex datasets using the next generation Hadoop ecosystem.
This book presents many exciting topics such as MapReduce patterns using Hadoop to solve analytics, classifications, and data indexing and searching. You will also be introduced to several Hadoop ecosystem components including Hive, Pig, HBase, Mahout, Nutch, and Sqoop.
This book introduces you to simple examples and then dives deep to solve in-depth big data use cases. This book presents more than 90 ready-to-use Hadoop MapReduce recipes in a simple and straightforward manner, with step-by-step instructions and real-world examples.
Chapter 1, Getting Started with Hadoop v2, introduces Hadoop MapReduce, YARN, and HDFS, and walks through the installation of Hadoop v2.
Chapter 2, Cloud Deployments – Using Hadoop Yarn on Cloud Environments, explains how to use Amazon Elastic MapReduce (EMR) and Apache Whirr to deploy and execute Hadoop MapReduce, Pig, Hive, and HBase computations on cloud infrastructures.
Chapter 3, Hadoop Essentials – Configurations, Unit Tests, and Other APIs, introduces basic Hadoop YARN and HDFS configurations, HDFS Java API, and unit testing methods for MapReduce applications.
Chapter 4, Developing Complex Hadoop MapReduce Applications, introduces you to several advanced Hadoop MapReduce features that will help you develop highly customized and efficient MapReduce applications.
Chapter 5, Analytics, explains how to perform basic data analytic operations using Hadoop MapReduce.
Chapter 6, Hadoop Ecosystem – Apache Hive, introduces Apache Hive, which provides data warehouse capabilities on top of Hadoop, using a SQL-like query language.
Chapter 7, Hadoop Ecosystem II – Pig, HBase, Mahout, and Sqoop, introduces the Apache Pig data flow style data-processing language, Apache HBase NoSQL data storage, Apache Mahout machine learning and data-mining toolkit, and Apache Sqoop bulk data transfer utility to transfer data between Hadoop and the relational databases.
Chapter 8, Searching and Indexing, introduces several tools and techniques that you can use with Apache Hadoop to perform large-scale searching and indexing.
Chapter 9, Classifications, Recommendations, and Finding Relationships, explains how to implement complex algorithms such as classifications, recommendations, and finding relationships using Hadoop.
Chapter 10, Mass Text Data Processing, explains how to use Hadoop and Mahout to process large text datasets and how to perform data preprocessing and loading of operations using Hadoop.
You need a moderate knowledge of Java and access to the Internet and a computer that runs a Linux operating system.
If you are a big data enthusiast and wish to use Hadoop v2 to solve your problems, then this book is for you. This book is for Java programmers with little to moderate knowledge of Hadoop MapReduce. This is also a one-stop reference for developers and system admins who want to quickly get up to speed with using Hadoop v2. It would be helpful to have a basic knowledge of software development using Java and a basic working knowledge of Linux.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The following are the descriptions of the properties we used in the hadoop.properties file."
A block of code is set as follows:
Path file = new Path("demo.txt");
FSDataOutputStream outStream = fs.create(file);
outStream.writeUTF("Welcome to HDFS Java API!!!");
outStream.close();When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
Job job = Job.getInstance(getConf(), "MLReceiveReplyProcessor");
job.setJarByClass(CountReceivedRepliesMapReduce.class);
job.setMapperClass(AMapper.class);
job.setReducerClass(AReducer.class);
job.setNumReduceTasks(numReduce);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(MBoxFileInputFormat.class);
FileInputFormat.setInputPaths(job, new Path(inputPath));
FileOutputFormat.setOutputPath(job, new Path(outputPath));
int exitStatus = job.waitForCompletion(true) ? 0 : 1;Any command-line input or output is written as follows:
205.212.115.106 - - [01/Jul/1995:00:00:12 -0400] "GET /shuttle/countdown/countdown.html HTTP/1.0" 200 3985
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Select Custom Action in the Add Bootstrap Actions drop-down box. Click on Configure and add."
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