Solution

The main distinction between big data and traditional data lies in the infrastructure required to handle them. Big data involves massive volumes of data, high velocity, and variety, often requiring scalable and distributed storage systems like Hadoop or cloud-based databases. Traditional data, typically smaller in volume and simpler in structure, can be managed with conventional databases like SQL without additional processing tools. Big data analytics also often demands parallel processing capabilities (e.g., MapReduce), given the data’s complexity. This difference in required infrastructure and technology is essential for data analysts as it dictates the tools, processing time, and resources needed for analysis. The other options are incorrect because: • Option 1 is incorrect as big data encompasses diverse data types, including categorical, numerical, text, and multimedia. • Option 2 is incorrect; big data is used by organizations of all sizes, depending on the data volume and business needs. • Option 4 is partially correct; while big data often supports real-time analytics, traditional data can also be analyzed in real time depending on the system setup. • Option 5 is incorrect; both big and traditional data require cleaning to ensure accuracy and reliability.