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Difference Between Hadoop And Big Data

There are many people that confuse between Big Data and Hadoop and consider both to be the same. However, there is a fundamental difference between the two and makes sense to understand the difference in order to utilize the technology to the fullest in the future. Big Data is an asset to companies. On the other hand, the latter is an open source software program with a set of goals and objectives to deal with the asset. Both these things go with each other in order to boost the performance and get optimal functioning. In addition to that, risks of loss during failure can be easily eliminated.

The Idea Of Big Data:

Business and companies have large volumes of data as assets. These data are used for different operations and accomplishing wide varieties of tasks. Well, there can be many different types of data in different sizes and formats. After all, these are used by different companies for specific operations. For instance, a particular company might collect thousands of data pieces in purchasing currency formats or on product information in the forms of inventories or sales numbers. The large chunks of information can be referred to as Big Data. The data is raw and unsorted in most cases. These data are collected through various handlers and tools.

The Idea Of Hadoop:

As far as Hadoop is concerned, it is one of the biggest tools till date designed for handling big data. It is one of the best software products used for interpreting the results of big data searches. It is designed for specific algorithms and methods, and is maintained by a global community of users. Hadoop is an open source software program with Apache license. The tool is popular all over the world and includes wide varieties of components like MapReduce and Hadoop distributed file system. These components are designed to offer a set of specific functions.

Using Various Features:

Developers, database administrators, and other technical professionals can make use of Hadoop and its various features to deal with the big data with large numbers of ways. Different companies have already implemented Hadoop in the process to pursue data strategies for targeting and clustering non-uniform data. At times, there are data that does not fit the traditional table or do not respond to simple queries. In such situation, Hadoop proved to work wonders in managing data solution benefiting large numbers of companies. It is not only efficient, but also helps in saving time in performing lots of complicated operations.

Filtering Raw Data:

The various components in Hadoop play a big role in filtering raw data and sorting them for efficient operations. For instance, MapReduce helps in managing and mapping a large data set and reduce the content for specific results. Reduction can be referred to as filtering of raw data. Following this, the HDFS system helps in distributing data to a wide variety of networks. These data are distributed and migrated, as per the need arises. Thus, it can be said that Hadoop and Big Data are interrelated.