Data Processing Big Data Processing

Question 1

What are the three Vs traditionally used to define big data?

Accepted Answer

Volume, Velocity, Variety

Answer

The three Vs of big data are Volume (large amounts of data), Velocity (high speed of data generation), and Variety (diverse data types and sources).

Question 2

What is Apache Hadoop primarily used for?

Accepted Answer

Distributed storage and processing of large datasets across clusters of computers

Answer

Apache Hadoop is an open-source framework that enables distributed storage (HDFS) and parallel processing (MapReduce) of massive datasets across computer clusters.

Question 3

What does HDFS stand for?

Accepted Answer

Hadoop Distributed File System

Answer

HDFS (Hadoop Distributed File System) is Hadoop's storage layer that splits large files into blocks and distributes them across multiple nodes for fault tolerance.

Question 4

What is MapReduce in big data processing?

Accepted Answer

A programming model that processes large datasets in parallel using map and reduce functions

Answer

MapReduce is a parallel processing framework where the Map phase splits and processes data, and the Reduce phase aggregates the results across distributed nodes.

Question 5

What is Apache Spark used for in big data?

Accepted Answer

Fast, in-memory distributed data processing for batch and streaming workloads

Answer

Apache Spark is a unified analytics engine that processes large-scale data in memory, making it significantly faster than disk-based MapReduce for iterative operations.

Data Processing Practice Test

Data Processing Practice Test

Data Processing Big Data Processing