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.

Explain the concept of Big Data and its significance in today's digital world.

Discuss the major challenges of handling Big Data including storage, processing, security and governance.

How is data analytics applied to Big Data? Discuss the role of predictive analytics, machine learning and real-time data processing.

Provide an overview of technologies for managing and analyzing Big Data, such as Hadoop, Spark and NoSQL databases.

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.

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

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

Describe the core components of Hadoop, including HDFS, MapReduce and YARN. What are their respective functions?

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.

Introduce Apache Pig and explain how it differs from Hive. What are the key advantages of using Pig for data processing?

Discuss the ETL (Extract, Transform, Load) process using Pig and how Pig facilitates large-scale data transformation.

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.

Describe the main types of NoSQL databases (Key-Value, Document, Column-Family and Graph). Provide examples of each type.

Discuss the data model of MongoDB and how it stores and manages data using JSON-like documents.

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

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

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.

Discuss the role of graph-based machine learning in social network mining for community detection and recommendation.

Compare collaborative filtering and content-based filtering in recommender systems. How do they apply to social networks.