The growth of big data has created a demand for ever-increasing processing power and efficient storage. DigitalGlobe’s databases, for example, expand by roughly 100TBs a day and cost an estimated $500K a month to store.
Compressing big data can help address these demands by reducing the amount of storage and bandwidth required for data sets. Compression can also remove irrelevant or redundant data, making analysis and processing easier and faster.