Regardless, the architectural limitations exist when distributing objects across nodes. Instead of beginning with a full object copy, a Manta storage node already has the entire object over a very high-bandwidth PCIe 3.0 bus and a theoretical 256Gbps, and it can perform meaningful computation on that object with minimal data movement latency.
What types of meaningful computation, you ask? The biggest use cases we'ave seen are delightfully poetic one-liners operating on large data sets. They may extract user or other cohort data from logs for aggregate, operational, billing, sentiment, intention, or geographic insights. Another use case leverages the Internet-visible nature of Manta: bulk in-place media transcoding. These include more prosaic needs like e-commerce catalog thumbnail creation or video format changes. We're beginning to see higher-level machine learning and classification computations that exploit Manta's map/reduce framework even further.
Common, familiar tools
What really evokes ecstatic comments from Manta customers is its use of standard, familiar tools. It's big data without the steep learning curve. Developers familiar with a machine vision library like OpenCV or text-processing languages like Perl are free to use them at the parallel compute nodes in Manta without modification. This reduces training and retooling time, and it taps a relatively large pool of developers in addition to self-styled data scientists.
With the launch of Manta, it's only becoming clearer that object storage is eating the world of storage. There's no doubt that computation on big data is driving new business models. Our view is that simplified, high-performance compute on stored objects will unleash another wave of innovation and business advantage. Object storage that can also provide analytic insights -- what a concept. Now it's our new reality.
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