In this tutorial we will discuss about the main differences between RDBMS and Hadoop. As you know that there is various kind of data and that data need to be stored. It can be structured, semi-structured, and unstructured. Both RDBMS and Hadoop works on storing the data. RDBMS usually stores structured data whereas Hadoop stores unstructured, semi-structured, and even structured data. In this tutorial we will discuss the main differences between RDBMS and Hadoop.
What is RDBMS?
Relational Database Management System (RDBMS) is created from a set of described tables from which data can be assessed in a variety of ways without needing to reorder the whole database tables. Simply, RDBMS is the essentials for all SQL as well as all database management systems like Oracle and MySQL, Microsoft SQL Server.
RDBMS is a database management system that works with a relational model. RDBMS is the development of all databases.
What is Hadoop?
Hadoop software library is a framework that allows distributed processing of large data sets across clusters of computers with effortless programming models. It is considered to scale up from single servers to thousands of machines. And each offering local computation and storage. the library itself is considered to identify and handle failures at the application layer Before relies on hardware to deliver high-availability, so that able to delivering a highly-available service on top of a cluster of computers, each of which may be level to failures.
RDBMS vs Hadoop
| Parameters | RDBMS | Hadoop |
| 1. Data Storage | average data size in (Giga Bytes) | Use for large data set (Tera Bytes and Peta Bytes) |
| 2. Schema | (static schema)
Required on write |
(dynamic schema)
Required on reading |
| 3. Hardware Profile | High-End Servers | Commodity/Utility Hardware |
| 4. Scalability | Vertical | Horizontal |
| 5. Data Objects | Works on Relational Tables | Works on Key/Value Pair |
| 6. Integrity | High (ACID) | Low |
| 7. Throughput | Low | High |
| 8. Use Case | OLTP (Online transaction processing) | Analytics (Audio, video, logs, etc), Data Discovery |
| 9. Speed | Reads are fast | Both reads and writes are fast |
| 10. Querying | SQL Language | HQL (Hive Query Language) |
| 11. Cost | License | Free |
| 12. Data Variety | Significantly used for Structured data. | Significantly used for Structured, Semi-Structured and Unstructured data |
| 13. Application | Application is usually OLTP and complex ACID | Application is usually data discovery and storage |
Conclusion
With this comparison, we know that HADOOP is the most excellent technique for handling Big Data as compared to that of RDBMS. As day by day, data usage is increasing and it is increasing with high velocity.
A better way of handling such a vast amount of data is becoming a hectic task. Storage and analysis of Big Data are suitable only with the help of the Hadoop eco-system than a traditional RDBMS. Hadoop is a huge-scale, open-source software framework committed to scalable, distributed, data-intensive computing. This framework breakdowns huge data into smaller parallelizable data sets and handles scheduling. And maps each part to an intermediate value, reliable, Fault-tolerant, and supports thousands of nodes and petabytes(PBS) of data, currently used in the development, production, and implementation options and testing environment. In this tutorial, we have discussed the difference between RDBMS and Hadoop. If you are having any doubt, feel free to ask me in the comment box.
