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 algorithms that require iterative processing of a large dataset that may or may not contain connected entities be processed in an efficient and timely manner?
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.
The Poly Sink compound pattern represents a part of a Big Data platform capable of egressing high-volume, high-velocity and high-variety data out to downstream enterprise systems.
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.
The Application Enhancement compound pattern represents a solution environment where the Big Data platform is used to ingest large amounts of data in order to calculate certain statistics or execute a machine learning and then to feed results to downstream systems.
How can the execution of a number of data processing activities starting from data ingress to egress be automated?
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?
How can large amounts of data be accessed instantaneously without any delay?
How can large amounts of raw data be analyzed in place by contemporary data analytics tools without having to export data?
How can very large amounts of data be processed with maximum throughput?