What type of tasks is Hadoop particularly suited for?

Hadoop is designed specifically for processing and analyzing large datasets, making it particularly well-suited for big data tasks. This framework allows for the distributed storage and processing of vast amounts of data across clusters of computers, leveraging parallel processing to enhance efficiency and speed. With its ability to handle petabytes of information from various sources, including structured and unstructured data, Hadoop excels in applications such as data mining, data warehousing, and large-scale data analytics.

In contrast, other options involve tasks that do not fall within the strengths of Hadoop. Processing small data sets typically does not require the scalability and distributed functions that Hadoop offers; simpler tools are often more efficient for such tasks. Designing physical products and creating simple spreadsheets require different skill sets and software tools that focus on design and basic data management, respectively, and do not engage with the large-scale data processing capabilities that Hadoop provides. Thus, the ability of Hadoop to manage and analyze large datasets is what solidifies its position as the correct answer in this context.