- Advanced big data analytics require a highly scalable system with extreme parallel processing capability and dense, modular packaging. A compute system with more memory, bandwidth, and throughput can run multiple tasks simultaneously, respond to millions of events per second, and parallel process advanced analytics algorithms in matter of seconds.
- Big data needs a computing system that is reliable and resilient and is able to absorb temporary increases in demand without failure or changes in architecture. This limits security breaches and enhances workload performance with little or no downtime.
- To support new big data workloads, computing systems must be built with open source technologies and support open innovation. Open source architecture allows more interoperability and flexibility and simplifies management of new workloads through advanced virtualization and cloud solutions.
Big data is a new, extraordinary resource to help companies gain competitive advantage. Applying real-time analytics to big data enables companies serve customers better, identify new revenue potential, and make lightning-quick decisions based on market insights. For companies to capitalize on the real-world business benefits of big data, they must first let go of their love for older technologies and look to newer, optimized alternatives.
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