With the production of new methodologies in Data Science for data analyzing and handling, one more field of data science has gained much attention and that is Hadoop. Being an open source platform, it manages data processing and big data applications’ storage working in clustered systems. It is considered a center area of developing big data technologies supporting advanced analytics, including data mining, machine learning, AI as well as predictive analytics. Hadoop is capable of handling and managing various structured and unstructured data, providing users enough options to collect, analyze and process the data as required.
Components and Brief Working of Hadoop
Developed in Java programming language, it supports processing of huge and distinct data sets present in a distributed environment. Basically, it is based on Google’s MapReduce that breaks or divides the application into many smaller chunks or pieces and then each part can run on various nodes in a cluster. The output of each and every node is then combined together in order to get a final output that has certain information in a user-readable format. Its ecosystem includes MapReduce, HDFS (Hadoop Distributed File System), Hadoop Kernel and a series of related projects. The HDFS is used generally for the storage purpose, especially data and MapReduce are responsible for analyzing and computing of the data stored in HDFS servers.
Applications
Hadoop, possessing so much importance, has a number of important applications, be it in industrial or health. Its framework, design, and versatility enable it to have such wide applications all over the world. Some of the applications are as follows:
● Financial companies majorly use Hadoop analytics to assess risk factors, develop investment models and design algorithms according to it.
● Retailers use it to serve their customers by analyzing and understanding structured and unstructured data.
● It is an important aspect when the Internet of things (IoT) projects are considered. In an IoT application, current condition data is continuously sent by a network of sensors over a platform that analyzes processes that are essential and provides a suitable action.
● Healthcare Use: Billions of gigabytes (or Exabytes) of data have been generated and stored; all this data is majorly generated by records keeping, medical logs, patient care, and compliance. To process such an enormous amount of data, the Hadoop framework is used to efficiently process the data. Apart from this, Hadoop has done wonders when it comes to monitoring patient’s vitals. It also helped greatly in genomics and cancer treatments.
● Keep track of social media data. Hadoop has a unique ability to capture and analyze emotion or sentiment data. Sentiment data are the unstructured pieces of data that have emotions, attitudes, opinions which are seen over social media platforms and various blogs. It is very important to understand the audience’s emotion and how they feel about the products and services in general. So, to ensure all these facts are well managed, Hadoop takes up the responsibility to do so.
Resource Box
The use of data science, big data and Hadoop is infinite. In fact, the field of data science is incomplete without Hadoop and vice-versa. To ensure a bright future in these fields, data science course is what will help anyone to achieve their goals.
