INTRODUCTION

Welcome to a comprehensive HBase tutorials. Big data is a sector that is growing exponentially with even the tech giants’ using it to enhance operations. A very important and crucial data solution for Big Data is Hadoop. Before we delve into what Hadoop is let us first understand the others aspects in the HBase tutorials.  

  1. What is HBase?
  2. History of HBase
  3. Why Do We Need HBase?
  4. Differences between HDFS and HBase
  5. Architecture of HBase
  6. Storage in HBase
  7. HBase Applications

1.What is HBase?

Like the Big Table of Google, HBase is a model of data that gives you rapid access to a huge quantity of structured data. A product of the Apache software foundation, HBase is part of Hadoop. HBase in Hadoop is written in the Java language and it is an open-source and non-relational distributed database that runs on the HDFS or the Hadoop distributed file system.

HBase is consistent, distributed, sparse, and multi-dimensional. Sparse data is similar to finding a needle in a stack of hay.

HBase can be used in several data quantities and variable schema and can be put to use in several ways.

2.History of HBase

Let us now find out about the history of HBase and what are its functions and features. The prototype of HBase was created in the year 2007 which was in the market in October 2007 along with Hadoop. It was in the year 2008 that HBase became a subproject of Hadoop and only in the year 2010 did HBase become a top-level Apache project. HBase is believed to have been developed alongside Hadoop and its many components.

3.Why Do We Need HBase?

Before Big Data was introduced it was RDBMS that took care of the major solution for problems in data storage. However, with the increase in the amount of data companies saw the need for better data management and storage solutions and this is where Hadoop came into the picture.

It uses a storage system that is distributed and has the MapReduce that is used to process data. Hadoop comes with many components like MapReduce and HDFS.

HBase is the leading component and because of its features, it is an important member of the ecosystem of Hadoop. It allows working on vast data quantities and quickly. It offers secure management of the data.

Hadoop can however only do the batch processing and sequentially access the data. HBase enables Hadoop to randomly access the data sequentially.

A comprehensive HBase tutorials would be remiss if we didn’t discuss the differences between HDFS and HBase:

4.Differences between HDFS and HBase

Let us understand the difference between HDFS and HBase.

HBase and HDFS both are Hadoop components which can make it confusing to understand their differences. This is even though they both have different tasks to perform.

  • HDFS is a distributed file system in Hadoop and it is used to store huge amounts of data. HBase is a database that is based on HDFS. It is not possible to look at the individual records fast in HDFS but that is possible when you use HBase.
  • With HDFS, batch processing with high latency is possible. HBase on the other hand gives very low access to latency. 
  • With HDFS you get only sequential file access. However, with HBase, you can get random access. In simple words, HBase helps to increase the speed of any specific operation that you can do using HDFS.

5.Architecture of HBase

HBase offers a key-value column focused on data storage and this is the best way to define its architecture. This works well on HDFS and it enhances the speed and the accessibility of the operation.

The three main parts of HBase are:

  • Region Servers
  • HMaster Server
  • Zookeeper

HMaster is responsible to take care of the administrative functions and the coordination of the Region servers. Zookeeper allows configuration of the information and a distributed synchronization.

6.Storage in HBase

HBase stores the tables in the form of rows. The scheme in HBase defines the column families that are the parts of key value. One table can comprise several column families and the column family can have many columns. Each cell on the table contains a timestamp.

HBase is a column-oriented database. The row-oriented database is perfect for online transaction processes and a column-oriented database is perfect for online analytical processing.

7.HBase Applications

HBase enhances the accessibility and speeds up data storage which is why it is used in several industries. With several advancements and updates in HBase, it is today an important tool for any professional managing Big Data.

  • HBase is used to write heavy applications
  • It helps to perform online log analytics to create the compliance report
  • It is used when there is a need to access any random and fast data that is stored in the HDFS
  • HBase finds use when there is a need for real-time read-and-write data to access a huge quantity of Big Data

CONCLUSION

This brings us to the end of the HBase tutorials. HBase is a vital part of Hadoop and it is best to go through the HBase tutorials. In this article, we have discussed the basics of HBase, its history, architecture, and applications.

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