Data and Analytics Big Data and Cloud Analytics 1 — Questions and Answers
Question 1: What are the 3 Vs traditionally used to define Big Data?
- Velocity, Volume, Variety (Correct answer)
- Value, Veracity, Velocity
- Volume, Visibility, Validity
- Variety, Veracity, Value
Correct answer: Velocity, Volume, Variety
The original three defining characteristics of Big Data are Volume (size), Velocity (speed of generation), and Variety (different data types).
Question 2: What is Apache Hadoop primarily used for?
- Real-time stream processing
- Distributed storage and batch processing of large datasets (Correct answer)
- In-memory data analytics
- Relational database management
Correct answer: Distributed storage and batch processing of large datasets
Apache Hadoop is a distributed computing framework that uses HDFS for storage and MapReduce for batch processing of large datasets across clusters.
Question 3: What is the main advantage of Apache Spark over Hadoop MapReduce?
- Spark uses less disk space
- Spark processes data in-memory, making it significantly faster (Correct answer)
- Spark is easier to install
- Spark requires fewer servers
Correct answer: Spark processes data in-memory, making it significantly faster
Apache Spark's in-memory processing can be up to 100x faster than Hadoop MapReduce for iterative algorithms and interactive queries.
Question 4: Which AWS service is a fully managed data warehouse solution?
- Amazon RDS
- Amazon DynamoDB
- Amazon Redshift (Correct answer)
- Amazon Aurora
Correct answer: Amazon Redshift
Amazon Redshift is AWS's fully managed, petabyte-scale cloud data warehouse optimized for analytics workloads.
Question 5: What is a data lake?
- A relational database optimized for analytics
- A centralized repository storing raw data in native format at any scale (Correct answer)
- A data warehouse with real-time capabilities
- A cloud-based ETL pipeline
Correct answer: A centralized repository storing raw data in native format at any scale
A data lake stores structured, semi-structured, and unstructured data in its raw format, enabling flexible analysis without predefined schemas.
Question 6: What does the term 'ETL' stand for in data engineering?
- Extract, Transform, Load (Correct answer)
- Evaluate, Test, Launch
- Export, Transfer, Link
- Execute, Track, Log
Correct answer: Extract, Transform, Load
ETL (Extract, Transform, Load) is the process of extracting data from sources, transforming it to the desired format, and loading it into a target system.
Data and Analytics Big Data and Cloud Analytics 1
What are the 3 Vs traditionally used to define Big Data?