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 traditional BI tools access data stored in Big Data storage technologies without having to make separate connections to these technologies?

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

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

The Operational Data Store (ODS) compound pattern represents a solution environment such that the Big Data platform’s inexpensive NoSQL storage can be utilized as a traditional ODS where transactional data from operational systems across the enterprise is collected for operational BI and reporting.

How can large amounts of data be imported into a Big Data platform from a relational database?

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?

How can the execution of a number of data processing activities starting from data ingress to egress be automated?

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 very large amounts of data be processed with maximum throughput?