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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
Lock Free Chapter
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
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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
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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
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Index
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Index

Chapter 4. Real-time Computation with Samza

The previous chapter discussed YARN, and frequently mentioned the breadth of computational models and processing frameworks outside of traditional batch-based MapReduce that it enables on the Hadoop platform. In this chapter and the next, we will explore two such projects in some depth, namely Apache Samza and Apache Spark. We chose these frameworks as they demonstrate the usage of stream and iterative processing and also provide interesting mechanisms to combine processing paradigms. In this chapter we will explore Samza and cover the following topics:

  • What Samza is and how it integrates with YARN and other projects such as Apache Kafka
  • How Samza provides a simple callback-based interface for stream processing
  • How Samza composes multiple stream processing jobs into more complex workflows
  • How Samza supports persistent local state within tasks and how this greatly enriches what it can enable
CONTINUE READING
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Tech Concepts
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Programming languages
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Learning Hadoop 2
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