Why Do We Need Hadoop For Data Science?

Data has been increasing at an alarming rate. And this could be brought to control with special techniques or software. One of such is Hadoop. Being open-source software, Hadoop refers to the formulation of data sets which become difficult to be managed, processed and analysed.

Delving into the details, let us understand the components of Hadoop.

What is the component of Hadoop?

There are mainly three components of Hadoop. You will be able to learn about the same in details at the best data science training institute in Bangalore.

Hadoop Distributed File System

The first component serves in the distribution of the data and further stores it into the distributed file system. This system is known as Hadoop Distributed File System or HDFS. Data is spread among machines in advance. For conducting the initial process, there is no data transfer needed. Computation occurs when the data gets stored wherever feasible.

Map-Reduce (MapR)

It is mainly used for high-level data processing. It conducts the process of a large amount of data over the cluster of nodes.

Yet Another Resource Manager (Yarn)

It mainly supports for Resource Management and Job Scheduling which you will be able to do in Hadoop Cluster. Yarn will further allow you to control and manage resources without any hindrances.

Use of Hadoop in Data Science

Engaging of Data with Large dataset

Before, data scientists tend to have a restriction for using datasets from their Local machine. Data Scientist needs to make use of a large volume of data. With the increase in data and a great need for analyzing it. Big data and Hadoop paves for a common platform for exploring and analyzing the data. Through Hadoop, one will be able to formulate a MapR job, HIVE or a PIG script and launch it onto Hadoop to the full dataset and obtain results.

Processing Data

Data Scientists need to make use of most of the data pre-processing. They need to forward data acquisition, transformation, cleanup, and feature extraction. This is needed for transforming raw data into standardized feature vectors. Hadoop further makes large scale data-pre processing simple for the data scientists. It offers tools like MapR, PIG and HIVE for efficient handling of large scale data.

Data Agility:

Unlike other traditional database systems that you need to have a strict scheme structure for, Hadoop is highly flexible. This formulation of the schema helps in the elimination for the need for schema redesigning whenever a new field is required.

Dataset for Data Mining:

Proved again that for larger datasets ML algorithms offer enhanced results. Techniques or tricks like clustering, outlier detection, product recommenders pave the way for a good statistical technique. In the traditional methodology, ML engineers tend to deal with a limited amount of data. This has further resulted in the lower performance of their models. Also, through the support of the Hadoop ecosystem, there has been the benediction of linear scalable storage. So, here you will be able to store all the data in RAW format.