Big Data Analytics (AL-802 (C)) - Important Questions

Explain the concept, 5Vs characteristics, and evolution of Big Data.

Discuss challenges in Big Data and explain the infrastructure and technologies used for Big Data analytics.

Design a Big Data processing pipeline for a real-world application and justify the choice of tools.

Describe Hadoop architecture and explain the core components of the Hadoop ecosystem.

Explain the working of the MapReduce programming model, including its key phases and its strengths and limitations in real-world applications.

Compare Hadoop and RDBMS in terms of data processing, storage, scalability and performance, and scenarios where Hadoop is preferred over RDBMS.

Explain HDFS and YARN architecture and their role in distributed data processing.

Explain Apache Hive and its role in data warehousing, how it simplifies querying large datasets in Hadoop, and its limitations compared to traditional databases.

Evaluate the execution model of Apache Pig and explain how it supports data transformation tasks.

Write short notes on ETL processing, Big Data analytics tools, and data types in Hive and Pig.

Explain NoSQL databases and discuss the different NoSQL data models / architectural patterns and how they handle large-scale and unstructured data.

Describe MongoDB architecture and its role in Big Data applications.

Discuss how graph theory is used to model social networks and extract meaningful insights.

Discuss and evaluate applications of social network mining in real-world domains such as recommendation, fraud detection, marketing, healthcare and cybersecurity.

Examine clustering techniques in social network graphs and analyze their effectiveness for community detection.

Discuss the challenges involved in analyzing large-scale social network data and suggest possible solutions.