Big Data, according to one definition put forth by the IT research and consulting firm Gartner, is "a popular term used to acknowledge the exponential growth, availability and use of information in the data-rich landscape of tomorrow." This definition contains within it many of the keys to thinking about Big Data.

The first important element to understand about Big Data is its rate of growth. Calling the growth of data "exponential" is by no means hyperbole. In the early days of computing, data storage could typically be described and measured on the order of megabytes. As the ability of technological tools to generate, process, and store data has skyrocketed, the cost of doing so has plummeted. According to research done by NPR, a gigabyte's worth of data storage in 1980 cost $210,000; today, it costs 15 cents.

Finding a Standard Framework for Big Data Management

Content managers have long been grappling with the challenge of managing Big Data once they've captured it-and the challenge has led developers to create entirely new distributed computing frameworks to contend with today's data management needs.

One emerging industry standard for managing Big Data is Apache Hadoop, a project spearheaded by open source advocate Doug Cutting. Hadoop allows for the distributed processing of large data sets across clusters of computers using a simple programming model. According to The Apache Software Foundation, Hadoop, which was named after Cutting's son's toy elephant, "is designed to scale up from single servers to thousands of machines, each offering local computation and storage."