Which framework is NOT commonly used in Big Data?

Which framework is NOT commonly used in Big Data?

• A. Hadoop - Map/Reduce
• B. Apache Spark
• C. Relational Database Management System
• D. Hadoop - Apache Hive

Answer: (C)

The choice of a Relational Database Management System (RDBMS) as the option that is not commonly used in the context of Big Data is accurate due to the fundamental differences between RDBMS and Big Data frameworks. Big Data technologies are designed to handle massive volumes of unstructured or semi-structured data, which go far beyond what traditional RDBMS can process efficiently. RDBMS usually deal with structured data, adhering to a fixed schema, which limits their scalability and flexibility when working with the type of data generated in big data scenarios. In contrast, frameworks like Hadoop with MapReduce, Apache Spark, and Hadoop with Apache Hive are specifically built to store, process, and analyze large datasets that may not conform to strict schema requirements, allowing for more versatile data handling. Hadoop - Map/Reduce provides a framework for distributed data processing, Apache Spark offers fast in-memory data processing capabilities, and Hadoop - Hive serves as a data warehousing solution for Hadoop that allows for data summarization, querying, and analysis. All of these are built to tackle the challenges presented by Big Data, whereas an RDBMS lacks the necessary tools and structure to effectively address these challenges.

The choice of a Relational Database Management System (RDBMS) as the option that is not commonly used in the context of Big Data is accurate due to the fundamental differences between RDBMS and Big Data frameworks.

Big Data technologies are designed to handle massive volumes of unstructured or semi-structured data, which go far beyond what traditional RDBMS can process efficiently. RDBMS usually deal with structured data, adhering to a fixed schema, which limits their scalability and flexibility when working with the type of data generated in big data scenarios. In contrast, frameworks like Hadoop with MapReduce, Apache Spark, and Hadoop with Apache Hive are specifically built to store, process, and analyze large datasets that may not conform to strict schema requirements, allowing for more versatile data handling.

Hadoop - Map/Reduce provides a framework for distributed data processing, Apache Spark offers fast in-memory data processing capabilities, and Hadoop - Hive serves as a data warehousing solution for Hadoop that allows for data summarization, querying, and analysis. All of these are built to tackle the challenges presented by Big Data, whereas an RDBMS lacks the necessary tools and structure to effectively address these challenges.