Converged Architectures and the World’s Largest Seashell Collection

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3 min read

In my recent article for insideBIGDATA “Converged Data Platforms: Part of a Larger Trend”, I talked about the inevitable direction of technology architecture towards a limitless mainframe model, a converged data center that will be composed largely of open source technologies like Linux, KVM, Hadoop, Spark, Mesos, and OpenStack. This is what CIOs and Enterprise Architects see on the distant horizon: a completely automated, agile and integrated development and deployment platform that gets the most value for every dollar spent. While there isn’t a lot to be gained from musing on things like this too long, the converged data center is a useful, albeit distant landmark from which to judge what is happening now.

For instance, the converged data center requires data to reside where it is most (cost) effective. That data may be in your primary data center, or on the manufacturing shop floor, or in the cloud, or it may be flowing from an ocean of sensors and smart devices to multiple data centers. Or, there may be data that you don’t currently own, but desperately need – like social data that can tell you how your customers feel about your products or services.

That means even smallish companies can be big data companies, and can afford access to the converged data center. This reminds me of a joke from Steven Wright: “I have the world's largest collection of seashells. I keep it scattered on beaches all over the world. Maybe you've seen some of it.” The data that is critical to your business may not always be created by your own systems. It may be scattered on cyber-beaches all over the world, waiting for you to find a way to collect it and benefit from it.

That’s where MapR comes in. The converged data center needs a unified method of capturing, aggregating, streaming, transforming, persisting, securing, and analyzing your digital seashell collection no matter how far, how fast, how big, and how varied it is. That is the design goal of the MapR Data Platform, and it’s here today. Maybe you’ve seen it.

This blog post was published March 31, 2016.

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