Difference between Big Data and Hadoop

Difference between Big Data and Hadoop

The difference between open source software program Hadoop and Big Data is recognized to be a fundamental and distinct one. Big Data happens to be an asset which is often an ambiguous and complex one. On the other hand, an open source software program is considered to be a program which completes a set of specific objectives and goals in order to deal with the asset.

Big Data is huge, raw and unsorted

Big Data is essentially larger sets of data which is put together by businesses as well as other parties for serving specific operations as well as goals. Big Data can include varied types of data in a wide variety of format. For instance, businesses may require putting a lot of work into collections several pieces of data on customer identifiers such as social security number, name, on purchases in currency formats or product details in the form of sales numbers, model numbers or inventory numbers. All of this along with other larger mass of information are known to be as Big Data. As the rule, it is known to be raw and unsorted till it is put via varied types of handlers as well as tools.


Difference between Big Data and Hadoop


Hadoop is used for handling Big Data

Hadoop is recognized to be one of the tools which are used for handling Big Data. Hadoop as well as other software products work for parsing or interpreting the results of searches of Big Data via specified proprietary algorithms as well as methods. Hadoop is considered to be an open source program that comes under Apache license. Hadoop can be maintained by the global community of users. It is inclusive of different primary components such as Hadoop distributed file system, MapReduce set of functions.

The primary idea behind MapReduce is that Hadoop is able to map first to a large set of data. It is capable of performing a reduction on the content for specified results. A reduce function can be thought of as a type of filter for raw data. The HDFS systems act for the distribution of data across one network or migrate the same as required.

Developers, database administrators, and others are capable of using the different features of Hadoop for dealing with Big Data in a wide number of ways. For instance, you can use Hadoop for pursuing data strategies such as targeting, clustering with the aid of nonuniform data or data which does not respond to simple queries in a perfect way or fit in a neat manner into the traditional table.

The core differences between Hadoop and Big Data

  • Big Data is nothing but a concept that represents a larger amount of data and how it can be handled. On the other hand, Apache Hadoop is recognized to be the framework that is used for handling a large amount of data.
  • Big Data is an asset which is complex often along with several interpretations. Hadoop, on the other hand, is a program that aids in completing a set of objectives and goals.
  • Since Big Data is a collection of data, it comprises of multiple formats of data. On the other hand, Hadoop requires to be handled and various codes which are required to be written for varied formats of data that can be semi-structured, structured as well as completely unstructured.

Check out this article on Advantages of Learning Big Data Hadoop.

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