Difference between RDBMS and HBase

RDBMS (Relational Database Management System) and HBase are both types of database management systems, but they differ in several ways:

Data Model: RDBMS uses a relational data model, where data is stored in tables with predefined columns and rows. HBase, on the other hand, uses a column-family data model, where data is stored in column families, which contain columns and rows. HBase is often referred to as a NoSQL database because of its non-relational data model.

Scaling: RDBMS is typically designed to scale vertically, which means adding more resources to a single machine to increase performance. In contrast, HBase is designed to scale horizontally, which means adding more machines to the system to increase performance. HBase’s ability to scale horizontally makes it more suitable for handling big data.

Consistency: RDBMS provides strong consistency, which means that all nodes in the system see the same data at the same time. HBase, on the other hand, provides eventual consistency, which means that different nodes may see different data at different times, but eventually, they will all converge on the same data.

Speed: HBase is designed to handle high-volume, high-velocity data, which means that it is faster than RDBMS when it comes to processing large amounts of data in real-time.

ACID compliance: RDBMS is typically ACID (Atomicity, Consistency, Isolation, Durability) compliant, which means that it ensures the reliability and consistency of data transactions. HBase, on the other hand, is not always ACID compliant, but it does provide strong consistency for read and write operations.

  RDBMS and HBase differ in their data model, scaling, consistency, speed, and ACID compliance. RDBMS is more suitable for traditional, transactional applications that require strong consistency, whereas HBase is better suited for big data applications that require horizontal scaling and high-speed processing.

Relational Database Management System (RDBMS): RDBMS is for SQL, and for all modern database systems like MS SQL Server, IBM DB2, Oracle, MySQL, and Microsoft Access. A Relational database management system (RDBMS) is a database management system (DBMS) that is based on the relational model as introduced by E. F. Codd. An RDBMS is a type of DBMS with a row-based table structure that connects related data elements and includes functions that maintain the security, accuracy, integrity, and consistency of the data. The most basic RDBMS functions are create, read, update and delete operations. Hbase follows the ACID Properties. 

Applications:

• Tracking and managing day-to-day activities and transactions such as production, stocking, income and expenses, and purchases.
• Management of normal activities in hospitals, banks, railways, schools, and institutions.

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, which are common in many big data use cases. It is an open-source, distributed database developed by Apache software foundations. Initially, it was named Google Big Table, afterwards, it was re-named 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. It stores a large amount of data in the form of tables. 

Application:

• For creating large applications.
• Random and fast accessing of data is provided using HBase. 
• HBase is used internally by companies including Facebook, Twitter, Yahoo, and Adobe.

Difference between RDBMS and HBase:

S. No.	Parameters	RDBMS	HBase
1.	SQL	It requires SQL (Structured Query Language).	SQL is not required in HBase.
2.	Schema	It has a fixed schema.	It does not have a fixed schema and allows for the addition of columns on the fly.
3.	Database Type	It is a row-oriented database	It is a column-oriented database.
4.	Scalability	RDBMS allows for scaling up. That implies, that rather than 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.	Scale-out is possible using HBase. It means that, while we require extra memory and disc space, we must add new servers to the cluster rather than upgrade the existing ones.
5.	Nature	It is static in nature	Dynamic in nature
6.	Data retrieval	In RDBMS, slower retrieval of data.	In HBase, faster retrieval of data.
7.	Rule	It follows the ACID (Atomicity, Consistency, Isolation, and Durability) property.	It follows CAP (Consistency, Availability, Partition-tolerance) theorem.
8.	Type of data	It can handle structured data.	It can handle structured, unstructured as well as semi-structured data.
9.	Sparse data	It cannot handle sparse data.	It can handle sparse data.
10.	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.
11.	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.
12.	Referential Integrity	Referential integrity is supported by RDBMS.	When it comes to referential integrity, no built-in support is available.
13.	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.
14.	Table size	It is designed to accommodate small tables.. Scaling is difficult.	It is designed to accommodate large tables. HBase may scale horizontally.