Most data processing has been accomplished with RDBMs and by separating analytic and transactional data processing. The variety, velocity, and volume of data today and the need for embedding intelligence into operational applications make these historical data platform practices untenable.
Goals and Objectives
Develop a data management architecture and platform with broad capabilities for transactional, behavioral, time series, spatial, and other data types and provide support for mixed workloads, including support for real-time insights and intelligent enterprise applications.
Relational databases (including those with analytic transactional capabilities, NoSQL or dynamic databases, document-orientated databases [XML and JSON]), key-accessible databases, graph databases, scalable data connection managers (Hadoop), in-memory databases, dynamic SQL databases; data management and integration tools
Use Case Summary
A multi-database and/or multi-engine data management environment with optimized processing based on specific workloads; incorporation of insights into the flow of operations to enable in-process decision support and more optimized transaction processing across applications