Final answer:
Big data encompasses massive datasets like those from the Sloan Survey, which can reach sizes of 15 terabytes, often necessitating supercomputers and citizen science projects for efficient analysis. Different types include quantitative and qualitative data, and fields like astronomy face challenges like the LSST's nightly 30 terabytes of data. Solutions involve advanced technology and methodologies like crowdsourcing.
Step-by-step explanation:
Big data refers to extremely large datasets that are difficult to process using traditional data processing applications. These datasets can include information from various fields such as astronomy, where projects like the Sloan Survey have gathered more than 15 terabytes of data, comparable to the Library of Congress. To manage and analyze this scale of data efficiently, advanced technologies such as supercomputers and sophisticated algorithms, as well as methodologies like citizen science, are employed.
Various types of datasets exist that can be quantitative, involving counts and measurements, or qualitative, involving non-measurable information. Examples of big data in different contexts include astronomical data from telescopes like the LSST, which is projected to generate up to 30 terabytes of data nightly after 2021, and real estate data samples used to determine housing market trends. The challenge lies not only in the storage and organization of this vast amount of data but also in extracting meaningful patterns and information that can be utilized in scientific discoveries or practical applications such as market analysis.
One example of big data at work is the 'Galaxy Zoo' project, where volunteers helped classify millions of galaxies, showcasing the power of crowdsourcing in dealing with extensive data sets. This not only democratizes scientific research but also improves the accuracy of categorizing data, which might be challenging for computers alone. Therefore, big data represents not just a collection of data but also a challenge that pushes the boundaries of computing and collaborative science.