Five Key Areas to Consider When Implementing Real-Time Processing - Slide 4

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To meet new real-time demands, compute, network and storage technology must be able to scale up and out. Compute and network performance have followed Moore’s Law, leveraging the gains available by roughly doubling transistor counts every two years. Storage technology has not kept up with this rate of performance growth with the dependency on spinning disks and mechanical movement of drive heads. RAID algorithms have provided some improvements in throughput and availability, yet continue to be challenged with storage workloads that require a lot of random reads and writes. A way to try to improve this kind of performance has been to keep adding disk drives to stripe the data across more and more mechanical disks. This may also cause over-provisioning of compute and software to support this IO-bound overhead.

Solid-state storage technologies are now able to re-balance the workload between compute, network and storage. The same flash memory technology utilized by digital cameras and mobile devices has been designed into the next generation of enterprise storage. Primary, all-flash storage systems provide a new class of performance over spinning disk systems. This is especially important for these highly distributed services when they are required to scale.

Any number of business models have been built around the exchange of data on daily, weekly and monthly intervals, but scheduling processes to produce results in the future can be a significant barrier to today’s computing, mobile, Web and application needs with the increased connectivity of businesses and consumers.

Examples include real-time access to inventory, order management, services and data assets within your business. Successfully managing real-time processing enables partners to better integrate offerings and third-party developers to create mobile apps, expanding business reach.

There are many opportunities to better leverage existing data assets and services in a larger ecosystem by providing real-time access through APIs. A great example of this vision is Netflix, which provided a real-time API for its consumer accounts and content. It has been so successful that it is working on the next version. Today Netflix serves more than a billion requests per day, with peak loads of over 20k requests per second.

Storage IO performance is critical to scale these real-time services while keeping your level of service consistent. Your storage architecture can impact the performance of these services both operationally as well as capture and report service usage for analytics and optimization.

Now is the time to leverage business assets — data, services, etc. — as real-time services. In doing so, you will find new business opportunities, provided you have infrastructure that can scale up and out for the large data demands of today and tomorrow.

Scott Metzger, vice president for corporate development at Violin Memory, has identified five key areas to consider when implementing and scaling real-time processing systems and services.

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