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.
RDBMS Database
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.
HBase Database
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.
Difference Between RDBMS and HBase
| Parameters |
RDBMS |
HBase |
| Query Language |
SQL ( Structured Query language)
|
No-SQL (non-relational)
|
| Schema |
It has a fixed schema. |
It has dynamic schema. |
| Database Type |
Structured |
Unstructured / Semi-structured. |
| Scalability |
RDBMS allows ( Vertical Scaling ). That means, rather to adding new servers, we should upgrade the current server to a more capable server whenever there is a requirement for more memory, processing power, and disc space. |
HBase allows ( Horizontal scaling ) , means when we require extra memory and disc space, we must add new servers to the cluster rather than upgrade the existing ones. |
| Nature |
It is static in nature |
Dynamic in nature |
| Data retrieval |
In RDBMS, slower retrieval of data. |
In HBase, faster retrieval of data. |
| Rule |
It follows the ACID (Atomicity, Consistency, Isolation, and Durability) property. |
It follows CAP (Consistency, Availability, Partition-tolerance) theorem. |
| Sparse data |
It cannot handle sparse data. |
It can handle sparse data. |
| Volume of data |
The amount of data in RDBMS is determined by the server’s configuration. |
In HBase, the amount of data depends on the number of machines deployed rather than on a single machine. |
| Transaction Integrity |
In RDBMS, mostly there is a guarantee associated with transaction integrity. |
In HBase, there is no such guarantee associated with the transaction integrity. |
| Referential Integrity |
Referential integrity is supported by RDBMS. |
When it comes to referential integrity, no built-in support is available. |
| Normalize |
In RDBMS, you can normalize the data. |
The data in HBase is not normalized, which means there is no logical relationship or connection between distinct tables of data. |
|
Setup Complexity
|
Simple to Design
|
Complex , it must require Hadoop ecosystem.
|
|
Use Cases
|
Transaction-heavy applications
|
Big Data real-time analytics.
|
|
Data Partitioning
|
No automatic partitioning , although some system support data sharding .
|
Automatic partitioning occur.
|
|
Backup & Recovery
|
RDBMS provide native recovery & backup option.
|
Backup & recovery mechanism is complex and depends on underlying Hadoop infrastructure.
|
Conclusion
The choice to utilize either RDBMS or HBase depends incredibly on the type of datasets we have or the particular use cases of our application . RDBMS is perfect for managing structured , relational data with transaction support. Though HBase can effortlessly handles large amount of unstructured data spread over numerous servers.
Difference Between RDBMS and HBase -FAQs
Can we HBase for transactional based applications ?
Since HBase doesn’t support fully ACID Properties like RDBMS , so it is not best choice to use HBase for transaction based applications that requires more strict consistency.
Is it possible to use HBase without Hadoop ecosystem ?
No , HBase relies and it works over Hadoop ecosystem , specifically HDFS to function effectively.
How HBase handle scalability compared to RDBMS ?
HBase is basically designed for the horizontal scaling (means we can add more server when data grows ) . While RDBMS typically scales Vertically ( means increasing power of that single server ) so Scaling in RDBMS is limited , but you can add as many as server for case of horizontal scaling.
Why is RDBMS is still used despites of its scalability issues ?
RDBMS is ideal for the structured data sets and it also support fully ACID Compliances and transaction property which ensures data integrity , strong consistency.
Can we shift easily from RDBMS to HBase ?
Migrating from RDBMS to HBase is possible but somewhat challenging because it require proper planning & proper re-structuring of the whole data with careful approach , also with lots of expenditure.
Is HBase is not suitable for small datasets ?
HBase is optimized for large datasets and distributed system. Even though HBase is also suited for small scale dataset , but it require more expenditure , whereas RDBMS Is cost effective and more efficient to store structured and small datasets.
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