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Bringing Hadoop to the mainframe by Paul Miller:
According to market leader IBM, there is still plenty of work for mainframe computers to do. Indeed, the company frequently cites figures indicating that 60 percent or more of global enterprise transactions are currently undertaken on mainframes built by IBM and remaining competitors such as Bull, Fujitsu, Hitachi, and Unisys. The figures suggest that a wealth of data is stored and processed on these machines, but as businesses around the world increasingly turn to clusters of commodity servers running Hadoop to analyze the bulk of their data, the cost and time typically involved in extracting data from mainframe-based applications becomes a cause for concern.
By finding more-effective ways to bring mainframe-hosted data and Hadoop-powered analysis closer together, the mainframe-using enterprise stands to benefit from both its existing investment in mainframe infrastructure and the speed and cost-effectiveness of modern data analytics, without necessarily resorting to relatively slow and resource-expensive extract transform load (ETL) processes to endlessly move data back and forth between discrete systems.
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