Difference between RDBMS and HBase

Last Updated : 04 Oct, 2024

When we want to manage & store data, the Selection of the ideal database is very crucial, since different datatypes are suited for different types of data & workloads. Two major types of databases are present, they are RDBMS ( Relational Database Management System ) & HBase ( Hadoop Database ). Both are used for different purposes and have their own merits and demerits as per the use case.

Databases are the backbone of any organization. Databases facilitated us to perform various operations on the data including CRUD operation. But when we working with databases, it's essential to choose the right database system based on the requirements of the organization.

Two major types of databases are used by various organizations:

Relational Database Management System (RDBMS)
Hadoop Database (HBase)

Both will have their strengths and weaknesses, it is our choice to choose which type of DBMS based on the use cases of the organization.

More likely RDBMS are used for storing Structured & related data by using MySQL, PostgreSQL, and so on. On the Other hand, HBase is designed to store massive amounts of unstructured or semi-structured data, especially HBase is used for Big Data Environments.

What is RDBMS?

RDBMS stands for " Relational Database Management System ". It stores the data in the form of relations or Tables in various rows and columns, by utilizing the SQL (Structured Query Language).

Some popular RDBMS systems are MySQL, PostgreSQL, Oracle DB & many more. Data should be stored in a structured manner in the case of RDBMS, where each table has rows ( records ) & columns ( attributes ) and these tables are related to each other. SQL is used to manage the data in the RDBMS, also SQL is most suitable for structured and well-organized data. Everything is neat, organized, and exactly fits into the table concerning their attributes and properties.

Advantages of RDBMS

Structured Data : RDBMS is best suitable for storing the structured Data like employee details , financial records ,etc.
ACID Properties : RDBMS ensures Atomicity , Consistency , Isolation , Durability of the data , this make it reliable for transactions.
Data Integrity : Due the use of Key Constraints like Candidate key , Primary key , Foreign key it ensures the integrity of Data.
Simple to Write Query : RDBMS support the usage of SQL language to shoot the query.

Disadvantages of RDBMS

Scalability Issue : Scalability of RDBMS across the servers ( Horizontal scaling ) is not possible , as the data grows.
Rigid Schema : RDBMS have the fixed schema , so it became difficult to make change in structure like adding new column or other attribute .
Not Efficient for Big Data : While working with large dataset , RDBMS faces certain performance related issues.

What is HBase (Hadoop Database) ?

HBase is a NoSQL type of database means it support the flexible schema , that runs on the Hadoop Distributed File System (HDFS) . It is basically designed to handle large scale data , unstructured type of data , semi-structured type of data across the distributed system.

In case of HBase we need not to follow the fixed schema , HBase allows us to store data in more flexible manner , basically it only works when the particular system must contains the Hadoop Ecosystem . Hadoop Database is best choice to handle the massive semi-structured or unstructured data , because it allows to store very large amounts of data across multiple computers in the distributed system.

Advantages of HBase

Scalability : HBase are highly scalable and can handle very large of data even in petabytes of data across the server , so it is perfect for huge data oriented applications.
Flexible Schema : Not at all like RDBMS , HBase have flexible schema , which makes it appropriate to store the unstructured data.
Distributed architecture : HBase is designed to work proficiently in the distributed environment , providing higher availability of data with high fault resistance capability.

Disadvantages of HBase

Complex Design : Setting up and maintaining HBase is vey complex due to its complex architecture design .
Lack of ACID Properties : HBase doesn't fully support the ACID Properties , so it is not suitable for transactions property.
Doesn't Support SQL : HBase doesn't support SQL & in case we are working on structured data then , RDBMS is most superior option to utilize.

When to use RDBMS vs HBase ?

RDBMS :

Small to medium datasets : If the data size is manageable and can handled by the single server and structured type of datasets then RDBMS can handle effectively.
Transaction Support : Application that requires strong consistency , like banking application , e-commerce application , where the data integrity is very crucial then use RDBMS.
Structured Data : If dataset is properly structured , then we must opt for RDBMS.

HBase :

Big Data Application : If you're dealing with very large dataset petabytes of data , also it spread across the various server , then HBase is better choice.
Unstructured / Semi-structured Data : If data set is unstructured / semi-structured or data doesn't fit into rows and columns
Real-time Analytics : For application that requires real-time querying over the datasets.

More On RDBMS & HBase :

Relational Database Management System (RDBMS): RDBMS is a SQL type of database management system like MS SQL Server, IBM DB2, Oracle, MySQL. A Relational database management system (RDBMS) is a database management system (DBMS) that is basically based on the relational model which was introduced by E. F. Codd.

In RDBMS data is stored in table structure that connects related data elements and including certain CRUD Operation that maintain the security, accuracy, integrity, and consistency of the data. The most basic RDBMS Operation like create, read, update and delete operations.

HBase: HBase is a column-oriented database management system that runs on top of the Hadoop Distributed File System (HDFS). It is well suited for sparse data sets. It is an open-source, distributed database developed by Apache software foundations. Initially, it was named Google Big Table, later on , it was re-named as HBase and is primarily written in Java. It can store massive amounts of data from terabytes to petabytes. It is built for low-latency operations and is used extensively for reading and writing operations.