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Persistence. Many big data applications involve regulatory compliance that dictates data be saved for years or decades. Medical information is often saved for the life of the patient. Financial information is typically saved for seven years. But big data users are also saving data longer because it’s part of an historical record or used for time-based analysis. This requirement for longevity means storage manufacturers need to include on-going integrity checks and other long-term reliability features, as well as address the need for data-in-place upgrades.

Flexibility. Because big data storage infrastructures usually get very large, care must be taken in their design so they can grow and evolve along with the analytics component of the mission. Data migration is essentially a thing of the past in the big data world, especially since data may be in multiple locations. A big data storage infrastructure is essentially fixed once you begin to fill it, so it must be able to accommodate different use cases and data scenarios as it evolves.

Application awareness. Some of the first big data implementations involved application-specific infrastructures, such as systems developed for government projects or the white-box systems invented by large Internet services companies.

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Application awareness is becoming more common in mainstream storage systems as a way to improve efficiency or performance, and it’s a technology that should apply to big data environments.

Smaller users. As a business requirement, big data will trickle down to organizations that are much smaller than what some storage infrastructure marketing departments may associate with big data analytics. It’s not only for the “lunatic fringe” or oddball use cases anymore, so storage vendors playing in the big data space would do well to provide smaller configurations while focusing on the cost requirements.

BIO: Eric Slack is a senior analyst at Storage Switzerland.

This was first published in April 2012

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