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 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 very large datasets comprising entities that are connected together be stored in a way that enables efficient analysis of such connected entities?

How can very large amounts of data be stored without degrading the access performance of the underlying storage technology?

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.