Difference Between RDBMS and Hadoop

The key difference between RDBMS and Hadoop is that the RDBMS stores structured data while the Hadoop stores structured, semi-structured, and unstructured data.

The RDBMS is a database management system based on the relational model. The Hadoop is a software for storing data and running applications on clusters of commodity hardware.

What is RDBMS?

RDBMS stands for Relational Database Management System based on the relational model. In the RDBMS, tables are used to store data, and keys and indexes help to connect the tables. A table is a collection of data elements, and they are the entities. It contains rows and columns. The rows represent a single entry in the table. The columns represent the attributes.

For example, the sales database can have customer and product entities.  The customer can have attributes such as customer_id, name, address, phone_no. The item can have attributes such as product_id, name etc. The primary key of customer table is customer_id while the primary key of product table is product_id. Placing the product_id in the customer table as a foreign key connects these two entities. Likewise, the tables are also related to each other.  They provide data integrity, normalization, and many more.  Few of the common RDBMS are MySQL, MSSQL and Oracle. They use SQL for querying.

What is Hadoop?

The Hadoop is an Apache open source framework written in Java. It helps to store and processes a large quantity of data across clusters of computers using simple programming models. The main objective of Hadoop is to store and process Big Data, which refers to a large quantity of complex data. The throughput of Hadoop, which is the capacity to process a volume of data within a particular period of time, is high.

There are four modules in Hadoop architecture. They are Hadoop common, YARN, Hadoop Distributed File System (HDFS), and Hadoop MapReduce. The common module contains the Java libraries and utilities. It also has the files to start Hadoop. Hadoop YARN performs the job scheduling and cluster resource management.

Furthermore, the Hadoop Distributed File System (HDFS) is the Hadoop storage system. It uses the master-slave architecture. The Master node is the NameNode, and it manages the file system meta data. Other computers are slave nodes or DataNodes. They store the actual data. On the other hand, Hadoop MapReduce does the distributed computation. It has the algorithms to process the data. In the HDFS, the Master node has a job tracker. It runs map reduce jobs on the slave nodes. There is a Task Tracker for each slave node to complete data processing and to send the result back to the master node. Overall, the Hadoop provides massive storage of data with a high processing power.

What is the Difference Between RDBMS and Hadoop?

RDBMS vs Hadoop
RDBMS is a system software for creating and managing databases that based on the relational model.	Hadoop is a collection of open source software that connects many computers to solve problems involving a large amount of data and computation.
Data Variety
RDBMS stores structured data.	Hadoop stores structured, semi-structured and unstructured data.
Data Storage
RDBMS stores average amount of data.	Hadoop stores a large amount of data than RDBMS.
Speed
In RDBMS, reads are fast.	In Hadoop, reads and writes are fast.
Scalability
RDBMS has vertical scalability.	Hadoop has horizontal scalability.
Hardware
RDBMS use high-end servers.	Hadoop uses commodity hardware.
Throughput
RDBMS throughput is higher.	Hadoop throughput is lower.

Source: differencebetween.com