Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Learning Hadoop 2
  • Table Of Contents Toc
Learning Hadoop 2

Learning Hadoop 2

By : GABRIELE MODENA
3.8 (4)
Buy this Book
close
close
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)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Introduction
Icon
1. Introduction
Icon
A note on versioning
Icon
The background of Hadoop
Icon
Components of Hadoop
Icon
Hadoop 2 – what's the big deal?
Icon
Distributions of Apache Hadoop
Icon
A dual approach
Icon
AWS – infrastructure on demand from Amazon
Icon
Getting started
Icon
Running the examples
Icon
Data processing with Hadoop
Icon
Summary
2
2. Storage
chevron up
Icon
2. Storage
Icon
The inner workings of HDFS
Icon
Command-line access to the HDFS filesystem
Icon
Protecting the filesystem metadata
Icon
Apache ZooKeeper – a different type of filesystem
Icon
Automatic NameNode failover
Icon
HDFS snapshots
Icon
Hadoop filesystems
Icon
Managing and serializing data
Icon
Storing data
Icon
Summary
3
3. Processing – MapReduce and Beyond
Icon
3. Processing – MapReduce and Beyond
Icon
MapReduce
Icon
Java API to MapReduce
Icon
Writing MapReduce programs
Icon
Walking through a run of a MapReduce job
Icon
YARN
Icon
YARN in the real world – Computation beyond MapReduce
Icon
Summary
4
4. Real-time Computation with Samza
Icon
4. Real-time Computation with Samza
Icon
Stream processing with Samza
Icon
Summary
5
5. Iterative Computation with Spark
Icon
5. Iterative Computation with Spark
Icon
Apache Spark
Icon
The Spark ecosystem
Icon
Processing data with Apache Spark
Icon
Comparing Samza and Spark Streaming
Icon
Summary
6
6. Data Analysis with Apache Pig
Icon
6. Data Analysis with Apache Pig
Icon
An overview of Pig
Icon
Getting started
Icon
Running Pig
Icon
Fundamentals of Apache Pig
Icon
Programming Pig
Icon
Extending Pig (UDFs)
Icon
Analyzing the Twitter stream
Icon
Summary
7
7. Hadoop and SQL
Icon
7. Hadoop and SQL
Icon
Why SQL on Hadoop
Icon
Prerequisites
Icon
Hive architecture
Icon
Hive and Amazon Web Services
Icon
Extending HiveQL
Icon
Programmatic interfaces
Icon
Stinger initiative
Icon
Impala
Icon
Summary
8
8. Data Lifecycle Management
Icon
8. Data Lifecycle Management
Icon
What data lifecycle management is
Icon
Building a tweet analysis capability
Icon
Challenges of external data
Icon
Collecting additional data
Icon
Pulling it all together
Icon
Summary
9
9. Making Development Easier
Icon
9. Making Development Easier
Icon
Choosing a framework
Icon
Hadoop streaming
Icon
Kite Data
Icon
Apache Crunch
Icon
Summary
10
10. Running a Hadoop Cluster
Icon
10. Running a Hadoop Cluster
Icon
I'm a developer – I don't care about operations!
Icon
Cloudera Manager
Icon
Ambari – the open source alternative
Icon
Operations in the Hadoop 2 world
Icon
Sharing resources
Icon
Building a physical cluster
Icon
Building a cluster on EMR
Icon
Cluster tuning
Icon
Security
Icon
Monitoring
Icon
Troubleshooting
Icon
Summary
11
11. Where to Go Next
Icon
11. Where to Go Next
Icon
Alternative distributions
Icon
Other computational frameworks
Icon
Other interesting projects
Icon
Other programming abstractions
Icon
AWS resources
Icon
Sources of information
Icon
Summary
12
Index
Icon
Index

Chapter 2. Storage

After the overview of Hadoop in the previous chapter, we will now start looking at its various component parts in more detail. We will start at the conceptual bottom of the stack in this chapter: the means and mechanisms for storing data within Hadoop. In particular, we will discuss the following topics:

  • Describe the architecture of the Hadoop Distributed File System (HDFS)
  • Show what enhancements to HDFS have been made in Hadoop 2
  • Explore how to access HDFS using command-line tools and the Java API
  • Give a brief description of ZooKeeper—another (sort of) filesystem within Hadoop
  • Survey considerations for storing data in Hadoop and the available file formats

In Chapter 3, Processing – MapReduce and Beyond, we will describe how Hadoop provides the framework to allow data to be processed.

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Learning Hadoop 2
End of Section 2
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options
font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon