For big data analytics, Hadoop is a lifesaver. Data gathered about people, processes, objects, tools, etc. is useful only when meaningful patterns emerge that, in turn, result in better decisions. We are using Hadoop technologies in the following area of study:
Healthcare Data Analysis:
Healthcare data is among the most voluminous and complex data produced in the world today. Lying among this massive pile of healthcare data is precious insights that can impact and improve the quality of human lives. It was even impossible a decade ago, but progress in Big Data Analytics has made Healthcare Analytics a distinct reality today.
Hadoop is the technology that is used in many healthcare data analytics platforms. This is because Hadoop is the right fit to handle the vast and complex healthcare data that effectively deal with the challenges plaguing the healthcare industry. Currently, we are trying to predict many diseases like Cancer, Heart Disease, Diabetes, Abnormal mental health, and many more.
Financial Data Analysis:
The financial sector, owing to the sensitive financial big data, needs to synchronize with numerous other sectors like stock exchanges, tax authorities, central banks, securities controlling authorities, revenue department, etc. It has to ensure to fulfil the regulatory requirements of the government authorities, and at the same time, it has to continually think about introducing secure, more comfortable, and faster ways to ease the transaction processing for the customers. The emergence of Big-Data in the financial sector has necessitated the development of software capable enough of handling it in real-time. Currently, we are using Big data and machine learning to detect Card frauds, analyze the transaction data to forecast the profit, and in many other predictions.
Security Management:
Security breaches usually show warning signs. Storing and analyzing big data in Hadoop is a great way to identify these problems before they happen. There are typically early warning signs like even suspicious emails, unusual server pings, IMs, or other forms of communication that could suggest internal clash. Fortunately, with the ability to now mine and correlate people, business, and machine-generated data all in one seamless analytics environment, we can get a complete picture of who is doing what and when. This includes the early detection of bribery, collusion, or an Ed Snowden in progress even before he has left the building.
Fig: Hadoop Applications
Education data analysis:
The amount of data generated in recent years in the educational sector is overgrowing. It is required for institutes to extract knowledge from the massive amount of data collected for better decision making. Data Mining is defined as extracting information or mining knowledge from vast sets of data. Data mining in the field of educational environment is known as Educational Data Mining. Educational Data Mining is a fast-growing discipline, concerned with developing methods for exploring the unique and increasingly huge-scale data that come from educational settings and using those methods to understand students better, and the settings which they learn in.
Communication and Media:
Every day we are generating a considerable volume of communication and media data, and we are currently storing them in the database to analyze further those data, but we are facing some potential problems like finding patterns in real-time media data and Leveraging social media and mobile content. Companies in this industry analyze customer behavioural data to create a specialized customer profile. They use Apache Hadoop to generate content for different target audiences, recommend content on-demand, and so on.
Study Material