Exploring Big Data Concepts

What is Big Data?

• Big Data is a type of software used for data visualization.

• Big Data is only relevant to social media platforms.

• Big Data is large and complex data sets that require advanced tools and techniques for processing and analysis.

• Big Data refers to small data sets that are easy to analyze.

List the 4 V's of Big Data.

• Volume, Velocity, Variety, Viscosity

• Volume, Velocity, Value, Veracity

• Volume, Variety, Velocity, Validation

• Volume, Velocity, Variety, Veracity

Explain the term 'Volume' in the context of Big Data.

• Volume in Big Data signifies the large quantity of data generated and stored.

• Volume indicates the value of the data being analyzed.

• Volume is the variety of data types in Big Data.

• Volume refers to the speed at which data is processed.

What does 'Velocity' refer to in Big Data?

• The accuracy of data storage in Big Data.

• The volume of data collected in Big Data.

• The variety of data types in Big Data.

• The speed of data generation and processing in Big Data.

Define 'Variety' in relation to data types.

• Variety indicates the volume of data stored in a database.

• Variety refers to the different types of data that can be represented.

• Variety refers to the speed at which data is processed.

• Variety is the measure of data accuracy and precision.

What is meant by 'Veracity' in Big Data?

• Veracity refers to the volume of data in Big Data.

• Veracity is about the speed of data processing.

• Veracity indicates the variety of data types in Big Data.

• Veracity in Big Data means the accuracy and trustworthiness of the data.

Name two types of data commonly used in Big Data analytics.

• Temporal data and spatial data

• Static data and dynamic data

• Quantitative data and qualitative data

• Structured data and unstructured data

What is distributed computing and how is it used in Big Data?

• Distributed computing is a method for storing data on a single server.

• Big Data refers to small datasets that can be processed on a single computer.

• Distributed computing is only used for gaming applications.

• Distributed computing is a method where multiple computers work together to process large datasets, essential for efficiently handling Big Data.

Explain parallel computing in the context of Big Data.

• Parallel computing reduces the amount of data processed in Big Data.

• Parallel computing slows down the processing of Big Data.

• Parallel computing enhances Big Data processing by enabling simultaneous execution of tasks across multiple processors, improving speed and efficiency.

• Parallel computing is only applicable to small datasets.

What are some challenges associated with Big Data?

• Big Data eliminates the need for data storage.

• Big Data guarantees perfect data quality.

• Challenges associated with Big Data include data storage, processing, quality, security, and the need for advanced analytics.

• Big Data is only a concern for small businesses.