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How can very large amounts of data be stored without degrading the access performance of the underlying storage technology?
How can a variety of unstructured data be stored in a scalable manner such that it can be randomly accessed based on a unique identifier?
How can large amounts of non-relational data that conforms to a nested structure be stored in a scalable manner so that the data retains its internal structure and sub-sections of a data unit can be accessed?
How can very large datasets comprising entities that are connected together be stored in a way that enables efficient analysis of such connected entities?
How can large amounts of non-relational data be stored in a table-like form where each record may consist of a very large number of fields or related groups of fields?
The Random Access Storage compound pattern represents a part of a Big Data platform capable storing high-volume and high-variety data and making it available for random access.