Given the promise of new analytics technologies, becoming more data-driven is on the minds of most IT decision makers these days. In a recent report on the impact of big data on analytics, "More than half of the organizations polled identified analytics as among their top five IT priorities," says Julie Lockner senior analyst and VP of data at the Enterprise Strategy Group (ESG), an IT strategic advisory firm based in Milford, Mass.

"With the promise big data is poised to bring," says Lockner, "organizations are exploring their options for solving business challenges with emerging [data] technologies. It's just not practical or cost-effective to use traditional [database] platforms and technologies that were designed before the big-data era."

Enter Apache's Hadoop, the open-source software framework named by its creator after his son's toy elephant. According to Lockner, the highly scalable Hadoop permits running analytics on massive data sets effectively and efficiently, whether that data is structured or unstructured.

"Where traditional databases hit their limits, Hadoop starts to emerge as a much better fit for solving unique analytics challenges," Lockner says. "Because data can be incorporated from multiple sources with varying types of data structures, Hadoop enables more analysis across multiple data feeds in a single platform -- solving some of the toughest data integration challenges commonly associated with relational data warehouse architecture."

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