Difference between RDBMS and HBase
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.
- 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.
- 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:
|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.|