Which of the following is a common type of Big Data workload?

Which of the following is a common type of Big Data workload?

• A. Real-time processing
• B. Document processing
• C. Batch processing
• D. Relational database management

Answer: (C)

Batch processing is a common type of Big Data workload primarily because it involves processing large volumes of data that are collected over a period of time. In a batch processing environment, data can be retrieved, processed, and analyzed in bulk, usually at scheduled intervals. This method is efficient for tasks such as report generation, where comprehensive analysis is required after accumulating data over hours, days, or weeks. Batch processing is particularly well-suited for scenarios where immediate results are not necessary, allowing for the use of system resources more efficiently without the need to continuously monitor data in real time. This contrasts with real-time processing, which involves immediate processing and analysis of streaming data, and can require more complex infrastructure to handle the continuous input. Document processing, while important, typically refers to the manipulation and analysis of textual data rather than large-scale data analytics associated with Big Data workloads. Relational database management, although often a feature in many data systems, is not specifically a type of Big Data workload since it generally handles structured data in a predefined schema, which may not necessarily scale well or apply to the velocity or variety aspects of Big Data. Therefore, batch processing is a strong fit for common Big Data workloads due to its scalability, efficiency, and effectiveness in handling large datasets over

Batch processing is a common type of Big Data workload primarily because it involves processing large volumes of data that are collected over a period of time. In a batch processing environment, data can be retrieved, processed, and analyzed in bulk, usually at scheduled intervals. This method is efficient for tasks such as report generation, where comprehensive analysis is required after accumulating data over hours, days, or weeks.

Batch processing is particularly well-suited for scenarios where immediate results are not necessary, allowing for the use of system resources more efficiently without the need to continuously monitor data in real time. This contrasts with real-time processing, which involves immediate processing and analysis of streaming data, and can require more complex infrastructure to handle the continuous input.

Document processing, while important, typically refers to the manipulation and analysis of textual data rather than large-scale data analytics associated with Big Data workloads. Relational database management, although often a feature in many data systems, is not specifically a type of Big Data workload since it generally handles structured data in a predefined schema, which may not necessarily scale well or apply to the velocity or variety aspects of Big Data.

Therefore, batch processing is a strong fit for common Big Data workloads due to its scalability, efficiency, and effectiveness in handling large datasets over