How can the size of the data be reduced to enable more cost effective storage and increased data movement mobility when faced with very large amounts of data?

How can large amounts of processed data be ported from a Big Data platform directly to a relational database?

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.

How can processed data be ported from a Big Data platform to systems that use proprietary, non-relational storage technologies?

The Poly Source compound pattern represents a part of a Big Data platform capable of ingesting high-volume and high-velocity data from a range of structured, unstructured and semi-structured data sources.

How can data stored in a Big Data solution environment be kept private so that only the intended client is able to read it?

How can the same dataset be consumed by disparate client programs?

How can large amounts of data be stored in a fault tolerant manner such that the data remains available in the face of hardware failures?

The Online Data Repository compound pattern represents a solution environment where the Big Data platform’s inexpensive storage is used to store data from internal and external data sources in its raw form available for consumption by any downstream application.