Historically, most data processing has been accomplished with RDBMs. Given the variety, velocity, and volume of data today, the tendency to force all data types into a relational model has become untenable.
Goals and Objectives
To develop a data management architecture and platform consisting of broad capabilities for transactional, behavioral, time series, spatial, and other data types.
Relational databases, NoSQL or dynamic databases, document orientated databases (XML and JSON), key-accessible databases, graph databases, scalable data connection managers (Hadoop)
Use Case Summary
A multi-database and/or multi-engine data management environment with optimized processing based on specific workloads.