Learn Hbase in 24 Hours

Learn Hbase in 24 Hours

By Alex Nordeen

Copyright 2021 - All Rights Reserved – Alex Nordeen

ALL RIGHTS RESERVED. No part of this publication may be reproduced or transmitted in any form whatsoever, electronic, or mechanical, including photocopying, recording, or by any informational storage or retrieval system without express written, dated and signed permission from the author.

Table Of Contents

Chapter 1: Introduction

Chapter 2: Architecture, Data Flow, and Use cases

Storage Mechanism in Hbase

HBase Architecture and its Important Components

Data flow in HBase

HBASE vs. HDFS

Chapter 3: Installation Guide

How to Download Hbase tar file stable version

Hbase - Standalone mode installation

Hbase - Pseudo Distributed mode of installation

Hbase - Fully Distributed mode installation

Chapter 4: Shell and General Commands

General commands

Tables Managements commands

Data manipulation commands

Cluster Replication Commands

Chapter 5: Handling Tables

Creation of Table with Rows and Column names

Placing values into tables and retrieving values from table

Retrieving Inserted Values in HBase shell mode

Chapter 6: Limitations, Advantage & Problems

Chapter 7: Troubleshooting

Chapter 1: Introduction

HBase is an open source, distributed database, developed by Apache Software foundation.

Initially, it was Google Big Table, afterwards it was re-named as HBase and is primarily written in Java.

HBase can store massive amounts of data from terabytes to petabytes.

HBase Unique Features

HBase is built for low latency operations

HBase is used extensively for random read and write operations

HBase stores large amount of data in terms of tables

Provides linear and modular scalability over cluster environment

Strictly consistent to read and write operations

Automatic and configurable sharding of tables

Automatic failover supports between Region Servers

Convenient base classes for backing Hadoop MapReduce jobs in HBase tables

Easy to use Java API for client access

Block cache and Bloom Filters for real-time queries

Query predicate pushes down via server side filters.

Why to Choose HBase?

A table for a popular web application may consist of billions of rows. If we want to search particular row from such huge amount of data, HBase is the ideal choice as query fetch time in less. Most of the online analytics applications uses HBase.

Traditional relational data models fail to meet performance requirements of very big databases. These performance and processing limitations can be overcomed by HBase.

Importance of NoSQL Databases in Hadoop

In big data analytics, Hadoop plays a vital role in solving typical business problems by managing large data sets and gives best solutions in analytics domain.

In Hadoop ecosystem, each component plays its unique role for the

Data processing

Data validation

Data storing

In terms of storing unstructured, semi-structured data storage as well as retrieval of such data's, relational databases are less useful. Also, fetching results by applying query on huge data sets that are stored in Hadoop storage is a challenging task. NoSQL storage technologies provide the best solution for faster querying on huge data sets.

Other NoSQL storage type Databases

Some