Evolution of Big Data

A financial institution is facing challenges in fraud detection due to the massive volume of transactions and the diversity of data sources. They plan to implement a Big Data Analytics solution to enhance their fraud detection system. Question: Analyze how the financial institution can classify and manage the digital data involved in fraud detection. Discuss the evolution of Big Data in finance, the tools available for analytics, and how they can address the challenges of processing and analyzing large datasets.

Big Data Analytics

A healthcare organization is implementing Big Data Analytics to improve patient care by analyzing electronic health records (EHRs), medical imaging data, and real-time monitoring data from wearable devices. Question: Considering the case study, discuss the importance of Big Data Analytics in healthcare. Highlight the characteristics of the data involved, the potential challenges in handling this data, and how different types of analytics can be used to improve patient outcomes.

Hadoop Implementation in Retail Analytics

Analyze a retail company's use of Hadoop to process customer transaction data. Discuss the benefits and challenges faced during implementation. Reg no : 13 to 22, 73 to 82

Hadoop in Financial Services

Study a financial institution’s approach to using Hadoop for risk management and fraud detection. What are the key components of their architecture? Reg No : 83 to 92, 23 to 32

Hadoop Overview

Explain the core components of the Hadoop ecosystem and their roles. Compare and contrast Hadoop with traditional RDBMS. What are the strengths and weaknesses of each? Reg . No : 1 to 12 , 61 to 72

Data Processing with Hadoop

Illustrate the process of data ingestion in Hadoop. What tools can be used for this purpose? Explain how data is processed in a Hadoop environment using MapReduce. Provide a simple example. Reg.No : 93 to 102,33 to 42

MapReduce

What are the main phases of the MapReduce model? Explain the significance of each phase. Discuss the features of MapReduce that make it suitable for large-scale data processing. Reg.No : 43 to 60 , 103 to 120

YARN

What is YARN, and how does it improve resource management in Hadoop clusters? Compare the architecture of YARN with the traditional MapReduce framework. Reg.No : 501 to 513