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  • Learning Hadoop 2
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Learning Hadoop 2

Learning Hadoop 2

By : GABRIELE MODENA
3.8 (4)
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Learning Hadoop 2

Learning Hadoop 2

3.8 (4)
By: GABRIELE MODENA

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

An overview of Pig

Historically, the Pig toolkit consisted of a compiler that generated MapReduce programs, bundled their dependencies, and executed them on Hadoop. Pig jobs are written in a language called Pig Lat in and can be executed in both interactive and batch fashions. Furthermore, Pig Latin can be extended using User Defined Functions (UDFs) written in Java, Python, Ruby, Groovy, or JavaScript.

Pig use cases include the following:

  • Data processing
  • Ad hoc analytical queries
  • Rapid prototyping of algorithms
  • Extract Transform Load pipelines

Following a trend we have seen in previous chapters, Pig is moving towards a general-purpose computing architecture. As of version 0.13 the ExecutionEngine interface (org.apache.pig.backend.executionengine) acts as a bridge between the frontend and the backend of Pig, allowing Pig Latin scripts to be compiled and executed on frameworks other than MapReduce. At the time of writing, version 0.13 ships with MRExecutionEngine (org.apache.pig.backend.hadoop...

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