Five Key Areas to Consider When Implementing Real-Time Processing - Slide 2

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In the batch world, integration largely depends on files to consume and produce information. Distributed systems architecture has had many challenges with RPC, CORBA, J2EE and others. In 2000, a Ph.D. thesis by Roy Fielding breathed new life into distributed computing with an architecture known as Representational State Transfer (REST). Fielding’s contribution leveraged Web infrastructure and standards with a pragmatic approach to keep it simple.

The simplicity of REST APIs put the architecture on a fast track to become the new standard of integrating disparate systems and those accessing their services. These RESTful API design practices preach the elimination of dependencies to create loosely coupled services to avoid the pains of distributed architectures of the past. RESTful services also make it much easier for developers to learn these APIs with a number of tools and resources.

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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