Which is the best Database language to learn – Hadoop or Database Management? A Database Management System (DBMS) makes it possible for end users to create, read, update and delete data in a database. DBMS essentially serves as an interface between the database and end users or application programs, ensuring that data is consistently organized and remains easily accessible.
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Today’s world is generating massive volumes of data at accelerating rates. As a result, big data analytics has become a powerful tool for businesses looking to leverage mountains of valuable data for profit and competitive advantage. In the midst of this big data rush, Hadoop, as an on premise or cloud-based platform has been heavily promoted as the one-size fits all solution for the business world’s big data problems. While Hadoop has lived up too much of the hype, there are certain situations where running workloads on a traditional database may be the better solution. Database Management is much cost effective as compared to Hadoop.
If we have to use the database for storing fewer details, it’s better to use traditional database rather than using Hadoop. The big data is mainly used for storing black box data, stock exchange data, social media data, power grid data, search engine data, etc.
Advantage of Hadoop:
1) Scalable: Hadoop is a highly scalable storage platform, it can store and distribute very large database across hundreds of server that operate in parallel.
2) Flexible: Hadoop can easily access new data source and tap into different type of data to get information from that data.
3) Fast: Hadoop is fast it search data fast and efficiently. Hadoop can efficiently process terabytes of data in just a minutes, and petabytes of data in an hour.
Advantage of Database:
1) Improved data sharing: DBMS helps to create an environment in which end users have better access to more and better-managed data.
2) Improved data security: The more users access the data, the greater the risks of data security breaches. DBMS provides a framework for better enforcement of data privacy and security policies.
3) Minimize data inconsistency: Data inconsistency exists when different versions of the same data appear in different places. DBMS helps to minimize the data inconsistency.
The benefits of big data analytics in providing deeper insights that lead to competitive advantages are real. Those benefits can only be realized by companies that exercise due diligence in making sure that Hadoop or traditional database is the analytics tool that best serves their needs.