Hadoop real-work solutions cookbook: over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout

Cover
 Copyright
 Credits
 About the Author
 Acknowledgements
 About the Reviewer
 www.PacktPub.com
 Table of Contents
 Preface
 Chapter 1: Getting Started with Hadoop 2.X
 Chapter 2: Exploring HDFS
 Chapter 3: Mastering Map Reduce Programs
 Chapter 4: Data Analysis Using Hive, Pig, and Hbase
 Chapter 5: Advanced Data Analysis Using Hive
 Chapter 6: Data Import/Export Using Sqoop and Flume
 Chapter 7: Automation of Hadoop Tasks Using Oozie
 Chapter 8: Machine Learning and Predictive Analytics Using Mahout and R
 Chapter 9: Integration with Apache Spark
 Chapter 10: Hadoop Use Cases
 Index IntroductionInstalling a Single Node Hadoop Cluster
 Installing a multi-node Hadoop cluster
 Adding new nodes to existing Hadoop clusters
 Executing balancer command for uniform data distribution
 Entering and exiting from the safe mode in a Hadoop cluster
 Decommissioning DataNodes
 Performing benchmarking on a Hadoop cluster
 Introduction
 Loading data from a local machine to HDFS
 Exporting data from HDFS to local machine
 Changing the replication factor of an existing file in HDFS
 Setting the HDFS block size for all the files in a cluster Setting the HDFS block size for a specific file in a clusterEnabling transparent encryption for HDFS
 Importing data from another Hadoop cluster
 Recycling deleted data from trash to HDFS
 Saving compressed data in HDFS
 Introduction
 Writing the Map Reduce program in Java to analyze web log data
 Executing the Map Reduce program in a Hadoop cluster
 Adding support for a new writable data type in Hadoop
 Implementing a user-defined counter in a Map Reduce program
 Map Reduce program to find the top X
 Map Reduce program to find distinct values Map Reduce program to partition data using a custom partitionerWriting Map Reduce results to multiple output files
 Performing Reduce side Joins using Map Reduce
 Unit testing the Map Reduce code using MRUnit
 Introduction
 Storing and processing Hive data in a sequential file format
 Storing and processing Hive data in the ORC file format
 Storing and processing Hive data in the ORC file format
 Storing and processing Hive data in the Parquet file format
 Performing FILTER By queries in Pig
 Performing Group By queries in Pig
 Performing Order By queries in Pig
 Performing JOINS in Pig Writing a user-defined function in PigAnalyzing web log data using Pig
 Performing the Hbase operation in CLI
 Performing Hbase operations in Java
 Executing the MapReduce programming with an Hbase Table
 Introduction
 Processing JSON data using Hive JSON SerDe
 Processing XML data using Hive XML SerDe
 Processing Hive data in AVRO format
 Writing User Defined functions in Hive
 Performing table joins in Hive
 Executing map side joins in Hive
 Performing context Ngram in Hive
 Call Data Record Analytics using Hive
 Twitter sentiment analysis using Hive