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  • Book Overview & Buying Hbase Essentials
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Hbase Essentials

Hbase Essentials

By : Garg
3.5 (2)
Buy this Book
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Hbase Essentials

Hbase Essentials

3.5 (2)
By: Garg
Buy this Book

Overview of this book

This book is intended for developers and Big Data engineers who want to know all about HBase at a hands-on level. For in-depth understanding, it would be helpful to have a bit of familiarity with HDFS and MapReduce programming concepts with no prior experience with HBase or similar technologies. This book is also for Big Data enthusiasts and database developers who have worked with other NoSQL databases and now want to explore HBase as another futuristic, scalable database solution in the Big Data space.
Table of Contents (9 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. Introducing HBase
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1. Introducing HBase
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The world of Big Data
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The origin of HBase
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Use cases of HBase
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Installing HBase
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Understanding HBase cluster components
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Summary
2
2. Defining the Schema
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2. Defining the Schema
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Data modeling in HBase
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Designing tables
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Accessing HBase
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Summary
3
3. Advanced Data Modeling
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3. Advanced Data Modeling
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Understanding keys
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HBase table scans
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Implementing filters
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Summary
4
4. The HBase Architecture
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4. The HBase Architecture
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Data storage
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Data replication
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Securing HBase
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HBase and MapReduce
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Summary
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5. The HBase Advanced API
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5. The HBase Advanced API
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Counters
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Coprocessors
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The administrative API
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Summary
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6. HBase Clients
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6. HBase Clients
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The HBase shell
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Kundera – object mapper
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REST clients
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The Thrift client
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The Hadoop ecosystem client
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Summary
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7. HBase Administration
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7. HBase Administration
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Cluster management
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Cluster monitoring
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Performance tuning
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Troubleshooting
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Summary
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Index
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Index

Chapter 1. Introducing HBase

A relational database management system (RDBMS) is the right choice for most of the online transactional processing (OLTP) applications, and it also supports most of the online analytical processing (OLAP) systems. Large OLAP systems usually run very large queries that scan a wide set of records or an entire dataset containing billions of records (terabytes or petabytes in size) and face scaling issues. To address scaling issues using RDBMS, a huge investment becomes another point of concern.

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