Which of the following best defines big data?

The definition that big data is "data that is too complex for traditional data processing applications" captures the essence of the challenges associated with big data. Big data is characterized not only by its volume but also by its variety and velocity, meaning it can come in various forms (structured, unstructured, semi-structured) and is generated at high speeds. Traditional data processing applications often struggle to manage, analyze, and derive insights from such large, complex datasets due to limitations in their architecture, processing capabilities, and analytical functions.

This definition emphasizes the need for advanced tools and techniques, such as distributed computing frameworks, machine learning algorithms, and specialized databases that can handle complex, diverse datasets effectively. The significance lies in the ability of modern systems to extract meaningful information from vast amounts of data where traditional methods fall short.

Other definitions proposed don’t accurately capture the nature of big data. For instance, describing big data as something that does not require processing overlooks its critical need for analysis and insights. Asserting that big data consists of small batches contradicts the common understanding, which inherently involves large volumes. Finally, defining it as purely qualitative ignores the quantitative aspects that are crucial in many big data applications. Therefore, the most suitable definition indeed revolves around complexity and the