Slide 3 of 8
Data and analytics architectures evolve
“Data and analytics architectures must evolve for the hybrid world,” Mizell says. “Cloud and on-premise, data in motion and at rest, transactional and analytic databases, in-memory and spinning disk, real-time and batch, AI and BI – all need to co-exist and interoperate. Organizations must look to bring together workload-specific, complementary analytic solutions to analyze all data, gain insights, and act. They must look at open, standard-based solutions that use APIs, micro-services, programming languages, and connectivity to seamlessly integrate with existing infrastructure and deliver business value while preserving existing investments.”


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