Big data define datasets that are usually very big. You can say it’s a reserve of large datasets that cannot be processed using the basic methods of computing. Big data is a vast concept rather than merely data that we can process using various techniques, tools, and frameworks. Hadoop is an open-source framework. It is on the basis of Java Programming. It also supports the storage and processing abilities of the large data sets in a computing environment spanning across branches. A team of computer scientists was the reason behind the advent of Hadoop. The team comprised of Mike Cafarella and Doug Cutting in 2005, in order to support the distribution capabilities of search engines. Let’s find out why Hadoop is used for Big Data Analytics.
Hadoop in Big Data Analytics
Hadoop is changing the impression of handling Big Data. Especially the handling of unstructured data. Let’s know how Hadoop, plays a vital role in handling Big Data. Hadoop allows excess data for streamlining of scattered processing systems across clumps of computers. They do this using simple programming models. This is to scale up from single servers to a large number of machines. All of them offering local computation, as well as storage space.
Benefits
Scalability: Hadoop is a storage platform that is highly expandable since it can easily store and dispense very large datasets at a time on servers that has the ability to operate parallel to each other. You can easily make your system handle more data simply by adding nodes. You will need little or no administration. Hadoop is beneficial for Big Data Analytics due to its scalability.
Cost-effective: Hadoop is very cost-effective in comparison to traditional DBMS. This open-source framework is free. Hadoop in fact uses commodity hardware to store large quantities of data. This is one of the main reasons why Hadoop is used for Big Data Analytics.
Fast: Hadoop manages data through clusters. It, thus, provides a unique storage method on the basis of scattering file systems. Hadoop’s unique feature is that it can map data on the clusters and hence, provide faster data processing.
Flexible: Hadoop is used for Big Data Analytics because it provides easy access and processes data in a very easy way. In order to generate the values in need by the company, thereby providing the enterprises with the tools to get better insights from diverse data sources.
Failure resistant: One of the many reasons why Hadoop is used for Big Data Analytics is its fault tolerance. This fault resistance is because of replicating the data to another node in the cluster. Therefore, in the event of a failure, the data from the replicating node can be in use, which in turn, can maintain data consistency.
Computing Power: Hadoop’s scattering computing model processes big data really fast. The more computing nodes you use, the more processing power you get.
Conclusion
It is very crucial to handle data in an organization. The rise of Hadoop is evident from the fact that there are many global MNCs that are using Hadoop today. It is an integral part of their functioning. There is a misconception that social media companies alone use Hadoop. In fact, most other industries now use Hadoop to manage Big Data. Hadoop has been changing the world of Big Data Analytics. It has more pros than cons and hence, is a great tool for handling Big Data.
