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  • Book Overview & Buying Learning Hadoop 2
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Learning Hadoop 2

Learning Hadoop 2

By : GABRIELE MODENA
3.8 (4)
Buy this Book
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Learning Hadoop 2

Learning Hadoop 2

3.8 (4)
By: GABRIELE MODENA
Buy this Book

Overview of this book

If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. You are expected to be familiar with the Unix/Linux command-line interface and have some experience with the Java programming language. Familiarity with Hadoop would be a plus.
Table of Contents (13 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction
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1. Introduction
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A note on versioning
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The background of Hadoop
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Components of Hadoop
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Hadoop 2 – what's the big deal?
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Distributions of Apache Hadoop
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A dual approach
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AWS – infrastructure on demand from Amazon
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Getting started
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Running the examples
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Data processing with Hadoop
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Summary
2
2. Storage
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2. Storage
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The inner workings of HDFS
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Command-line access to the HDFS filesystem
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Protecting the filesystem metadata
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Apache ZooKeeper – a different type of filesystem
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Automatic NameNode failover
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HDFS snapshots
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Hadoop filesystems
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Managing and serializing data
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Storing data
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Summary
3
3. Processing – MapReduce and Beyond
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3. Processing – MapReduce and Beyond
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MapReduce
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Java API to MapReduce
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Writing MapReduce programs
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Walking through a run of a MapReduce job
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YARN
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YARN in the real world – Computation beyond MapReduce
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Summary
4
4. Real-time Computation with Samza
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4. Real-time Computation with Samza
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Stream processing with Samza
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Summary
5
5. Iterative Computation with Spark
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5. Iterative Computation with Spark
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Apache Spark
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The Spark ecosystem
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Processing data with Apache Spark
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Comparing Samza and Spark Streaming
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Summary
6
6. Data Analysis with Apache Pig
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6. Data Analysis with Apache Pig
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An overview of Pig
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Getting started
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Running Pig
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Fundamentals of Apache Pig
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Programming Pig
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Extending Pig (UDFs)
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Analyzing the Twitter stream
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Summary
7
7. Hadoop and SQL
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7. Hadoop and SQL
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Why SQL on Hadoop
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Prerequisites
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Hive architecture
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Hive and Amazon Web Services
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Extending HiveQL
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Programmatic interfaces
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Stinger initiative
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Impala
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Summary
8
8. Data Lifecycle Management
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8. Data Lifecycle Management
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What data lifecycle management is
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Building a tweet analysis capability
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Challenges of external data
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Collecting additional data
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Pulling it all together
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Summary
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9. Making Development Easier
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9. Making Development Easier
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Choosing a framework
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Hadoop streaming
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Kite Data
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Apache Crunch
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Summary
10
10. Running a Hadoop Cluster
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10. Running a Hadoop Cluster
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I'm a developer – I don't care about operations!
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Cloudera Manager
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Ambari – the open source alternative
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Operations in the Hadoop 2 world
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Sharing resources
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Building a physical cluster
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Building a cluster on EMR
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Cluster tuning
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Security
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Monitoring
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Troubleshooting
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Summary
11
11. Where to Go Next
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11. Where to Go Next
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Alternative distributions
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Other computational frameworks
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Other interesting projects
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Other programming abstractions
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AWS resources
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Sources of information
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Summary
12
Index
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Index

Chapter 5. Iterative Computation with Spark

In the previous chapter, we saw how Samza can enable near real-time stream data processing within Hadoop. This is quite a step away from the traditional batch processing model of MapReduce, but still keeps with the model of providing a well-defined interface against which business logic tasks can be implemented. In this chapter we will explore Apache Spark, which can be viewed both as a framework on which applications can be built as well as a processing framework in its own right. Not only are applications being built on Spark, but entire components within the Hadoop ecosystem are also being reimplemented to use Spark as their underlying processing framework. In particular, we will cover the following topics:

  • What Spark is and how its core system can run on YARN
  • The data model provided by Spark that enables hugely scalable and highly efficient data processing
  • The breadth of additional Spark components and related projects

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