Data Processing Big Data Processing 1 — Questions and Answers
Question 1: What are the three Vs traditionally used to define big data?
- Volume, Velocity, Variety (Correct answer)
- Value, Validity, Visibility
- Virtual, Vertical, Variable
- Volume, Verification, Visualization
Correct answer: Volume, Velocity, Variety
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?
- Distributed storage and processing of large datasets across clusters of computers (Correct answer)
- Building real-time web applications
- Managing relational database transactions
- Creating data visualization dashboards
Correct answer: Distributed storage and processing of large datasets across clusters of computers
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?
- Hadoop Distributed File System (Correct answer)
- High Definition File Storage
- Hierarchical Data Format System
- Hybrid Data Flow Service
Correct answer: Hadoop Distributed File System
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?
- A programming model that processes large datasets in parallel using map and reduce functions (Correct answer)
- A tool for creating geographic data visualizations
- A compression algorithm for large files
- A type of NoSQL database schema
Correct answer: A programming model that processes large datasets in parallel using map and reduce functions
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?
- Fast, in-memory distributed data processing for batch and streaming workloads (Correct answer)
- Managing relational database schemas
- Scraping web pages for SEO analysis
- Monitoring network traffic in real time
Correct answer: Fast, in-memory distributed data processing for batch and streaming workloads
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.
Question 6: What is a data lake?
- A central repository that stores raw data in its native format until needed (Correct answer)
- A cloud service for streaming video
- A type of normalized relational database
- A backup system for transactional databases
Correct answer: A central repository that stores raw data in its native format until needed
A data lake is a large-scale storage repository that holds raw, unprocessed data in its native format — structured, semi-structured, or unstructured — until it is needed for analysis.
Data Processing Big Data Processing 1
What are the three Vs traditionally used to define big data?