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.
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.
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.
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:
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.
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:
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.
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.
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.
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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