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Introduction to HBase for Hadoop

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We all know processing big data was a problem for many years, but, later, that was successfully solved with the invention of Hadoop. In this HBase tutorial, we are going to cover all the concepts in detail and will consider a use case to know how it will work in real time. Let’s get into the topics now.

Before getting into the HBase tutorial, its very essential to know its roots and the situations that gave birth to HBase. Let's have a glance over those causes. 

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Below topics are covered in this article.

Limitations of Hadoop and the Emergence of HBase

Hadoop is capable of only batch processing, and it requires access to data in a sequential manner, which means, one has to search entire data to get the specific information. 

In Hadoop, it is required to process the enormous data sets and the result is, we get is the same huge data sets which again need to be processed. This caused a redundancy and therefore needed a solution to access the specific point of data. 

What is HBase

HBase is defined as an Open Source, distributed, NoSQL, Scalable database system, written in Java. It was developed by Apache software foundation for supporting Apache Hadoop, and it runs on top of HDFS (Hadoop distributed file system). HBase provides capabilities like Bigtable which contains billions of rows and millions of columns to store the vast amounts of data. It allows users to save billions of records and retrieves the information instantly. For example, the HBase consists of 5 billion records, and in that, if you wish to find 20 large items, HBase does it for you immediately: that's how it works. 

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HBase tables act as an input and output device for MapReduce jobs that run on Hadoop. It is a column-oriented key-value data store. HBase is well suited for faster read and write tasks on large data sets. HBase is not a direct replacement to the SQL database, but Apache Phoenix provides an SQL layer and JDBC (Java Database Connectivity) Driver that allows in integrating with analytics and business intelligence applications.

[Related Page: Introduction to HDFS]

HBase Storage Mechanism

HBase is a column-oriented  NoSQL database in which the data is stored in a table. The HBase table schema defines only column families. The HBase table contains multiple families, and each family can have unlimited columns. The column values are stored in a sequential manner on a disk. Every cell of the table has its timestamp (which means a digital record of time or  occurrence of a particular event at a future  time.)

HBase Table consists of the following components:

  • Table: It’s a collection of rows. 
  • Row: It is nothing but a collection of family columns. 
  • Column family: It’s a collection of families. 
  • Column: It is a group of key-value pairs.
  • Timestamp: It is a record of digital time and date. 

[Related Page: Understanding Data Parallelism in MapReduce]

Features of HBase:

Below mentioned are some of the essential features of the HBase. Let's discuss them in detail.

  • Atomic read and write.
  • Consistent reads and writes.
  • Linear and modular scalability.
  • Automatic and configurable sharding of tables.
  • Easy to use Java API for client access.
  • Thrift gateway and RESTful Web services.

[Related Page: Prerequisites for Learning Hadoop]

HBase continuously works on reads and writes to fulfil the high-speed requirements of data processing. 

Linear and modular scalability: It is highly scalable, which means, we can add more machines to its cluster. By using fly, we can add more clusters to the network. When a new RegionServer  is up,  the cluster automatically begins rebalancing, it starts the RegionServer on the new node and scales up.  

Automatic and configurable sharding of tables: An HBase table is made up of regions and is hosted by the RegionServers. All the regions are distributed across all region servers on various DataNodes. HBase automatically splits these regions into small subregions till it reaches to a threshold size to reduce I/O time and overhead.

Easy to use Java API for client access: HBase has been developed with the robust Java API support (client/server) which is simple to create and easy to execute.  

Thrift gateway and RESTful Web services: To support the front end apart from Java programing language, it supports Thrift and REST API.

[Related Page: Leading Hadoop Vendors in BigData]

Where to Use HBase?

  • Hadoop HBase is used to have random real-time access to the Big data. 
  • It can host large tables on top of cluster commodity. 
  • HBase is a non-relational database which modelled after Google's big table.  It works similar to a big table to store the files of Hadoop. 
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Applications of HBase 

Below mentioned are the areas where HBase is being used widely for supporting data processing.  

Medical: The medical industry uses HBase to store the data related to patients such as patient diseases,  information such as age, gender, etc., to run MapReduce on it.

Sports: Sports industry uses HBase to store the information related to the matches. This information would help perform analytics and in predicting the outcomes in future matches. 

Web: The web is using the HBase services to store the history searches of the customers. This search information helps the companies to target the customer directly with the product or service that they had searched for. 

Oil and petroleum: HBase is used to store the exploration data which helps in analysing and predicting the areas where oil can be found. 

E-commerce: E-commerce is using HBase to record the customer logs and the products they are searching for. It enables the organizations in targeting the customer with the ads to induce him to buy their products or services.       

Other fields: Hbase is being employed in different fields where data is the most important factor and needs to store petabytes of data to conduct the analysis. 

[Related Page: Hadoop Jobs Salary Trends in the USA ]

Apache HBase Architecture 

We know HBase is acting like a big table to record the data, and tables are split into regions.  Again, Regions are divided vertically by family column to create stores. These stores are called files in HDFS. The below-shown image represents how HBase architecture looks like. 

HBase has three major components which are master servers, client library, and region servers.  It's up to the requirement of the organization whether to add region servers or not. 


  • It allocates regions to the region servers with the help of zookeeper. 
  • It balances the load across the region servers.
  • MasterServer is responsible for changes like schema changes and metadata operations, like creating column families and tables.
[Related Page: Hadoop HDFS Commands]


Regions are nothing but tables which are split into small tables and spread across the region servers. 


Region servers communicate with other components and complete the below tasks:

  • It communicates with the client to handle data related tasks.
  • It takes care of the read and write tasks of the regions under it.
  • It decides the size of a region based on the threshold it has.

[Related Page: Cloudera Hadoop Certification]

The below-mentioned graph shows how a region server contains regions and stores.  

As shown in the above image, the store contains Memory store and HFiles. The memory here acts as a temporary space to store the data. When anything is entered into Hbse, it is initially stored in the memory, and later, it will be transferred to HFiles where data is stored in blocks. 

Zookeeper: Zookeeper is an open source project, and it facilitates the services like managing the configuration data, providing distributed synchronisation, naming, etc. It helps the master server in discovering the available servers.  Zookeeper helps the client servers in communicating with region servers. 

Frequently Asked Hadoop Interview Questions


Limitations of HBase

Below mentioned are some of the limitations of HBase.

  • It takes a very long time to recover if the HMaster goes down. It takes a long time to activate another node if the first nodes go down. 
  • In HBase, cross data operations and join operations are very difficult to perform,  even if we join operations by using MapReduce, it requires a lot of time to design and develop.
  • HBase needs a new format when we want to migrate from RDBMS external sources to HBase servers. 
  • It is very challenging in HBase to support querying process. We need integration support from SQL layers like Apache Phoenix to write queries to trigger the information from the database.  
  • It takes enormous time to develop security factor to grant access to the users.
  • HBase allows only one default sort for a table, and it does not support large size of binary files.  
  • HBase is expensive in terms of hardware requirement and memory blocks’ allocations.  

[Related Page: Reasons to Learn to Hadoop & Hadoop Administration]

Importance of NoSQL Databases in Hadoop   

When we see Hbase as a separate entity from Hadoop, it is a very powerful database. It has got a real-time query and performs offline and batch processing via MapReduce. HBase enables the user to perform the questions to get the data of individual information as well as to retrieve the aggregate analytics reports from the large data. 

[Related Page: Hadoop Ecosystem]

Types of NoSQL Database management systems.

There was a need for the giant companies to store the data which was getting generated round the clock. Companies like Amazon, Facebook, Google, etc., require a storage mechanism that could possess the capability to store the large volumes of data.  Storage issue has paved way for the development of NoSQL database.  

Below mentioned are the four types of NoSQL databases available to support various tasks. 

  • Key-value store NoSQL database. 
  • Document store NoSQL database. 
  • Column store NoSQL database. 
  • Graph-based NoSQL database.

[Related Page: Big Data Analytics]

HBase vs Hive: 

Features HBase Hive
SQL Support It doesn’t take any supportIt uses hive query language
Data Schema No Schema It consists of Schema
Particion methods ShardingSharding 
Database modelIt has wide column storeRelational DBMS
Consistency levelHigh consistencySlow consistency
Replication methodsSelectable replication Selectable replication
Secondary IndexesNo Yes 

[Related Page: Hadoop Installation and Configuration]

HBase vs RDBMS

The below-mentioned table compares the HBase with the relational database management system. Let’s examine them by taking essential aspects into consideration. 

  HBase  RDBMS
It consists of columnsContains rows
Developed to store any kind of data Suitable for storing the Denormalized data
It has automatic partitioning feature It has no automatic partitioning function 
It contains wide tables to store the databaseIt has very thin tables in database 
Suits best for OLAP systemsSuitable for OLTP systems 
Reads specific data from databaseReads even unnecessary data from database to retrieve specific data
All kinds of data is processed and storedOnly structured data is processed and stored

[Related Page: MapReduce In Bigdata ]

Career growth in HBase technology

We know how fast the data is getting generated, and the Hadoop is becoming faster day by day. HBase is a perfect platform to work on Hadoop distributed file system. Learning one of the top technologies like HBase will be an added advantage to get a job. Companies across the world are depending on data to invest in the present as well as future projects. Learning Hbase will help you in working with various other technologies of Hadoop. All these things will help you in getting into a successful career.


Till now, we have been through the different aspects of the HBase, like how it works, installation, architecture, etc. HBase is playing an essential role in contributing its share for the development of Big Data. If you want to excel your career in HBase, it's better to take training from the institutes like Mindmajix, as it is an added advantage. Having certification in Hbase would help you stand apart from the crowd. Happy learning!  

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List of Other Big Data Courses:

 Hadoop Adminstartion MapReduce
 Big Data On AWS Informatica Big Data Integration
 Bigdata Greenplum DBA Informatica Big Data Edition
 Hadoop Hive Impala
 Hadoop Testing Apache Mahout


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Last updated: 29 Apr 2023
About Author

Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.

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