Modern hardware trends and economics combined with cloud/virtualization technology are radically reshaping today’s data management landscape, ushering in a new era where many machine, many core, memory-based computing topologies can be dynamically assembled from existing IT resources and pay-as-you-go clouds. Arguably one of the hardest problems—and consequently, most exciting opportunities—faced by today’s solution designers is figuring out how to leverage this on-demand hardware to build an optimal data management platform that can:

• Exploit memory and machine parallelism for low-latency and high scalability grow and shrink elastically with business demand
• Treat failures as the norm, rather than the exception, providing always-on service
• Span time zones and geography uniting remote business processes and stakeholders
• Support pull, push, transactional, and analytic based workloads increase cost-effectiveness with service growth