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Price performance: The Hadoop big data technology provides significant cost savings (think a factor of approximately 10) with significant performance improvements (again, think factor of 10).Bringing compute and storage together on commodity hardware: The result is blazing speed at low cost.There are many factors contributing to the hype around Big Data, including the following. The Hadoop technology brings new and more accessible programming techniques for working on massive data stores with both structured and unstructured data. Via extensibility they also manage unstructured data. Traditional relational databases, like Informix and DB2, provide proven solutions for structured data. As of the year 2012, clusters that are big are in the 100 Petabyte range.īig Data can be both structured and unstructured. The examples in this article use small samples to illustrate the capabilities of the technology. Using Big Data technology is not restricted to large volumes.

There is so much hype in this space that there could be an extended debate just about the definition of big data. These factors make Big Data difficult to capture, mine, and manage using traditional methods. If you are doing the exercises without network connectivity, use the MySQL examples.īig Data is large in quantity, is captured at a rapid rate, and is structured or unstructured, or some combination of the above. There is a MySQL instance built into the VMWare image.
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This article will provide a working definition of Big Data and then work through a series of examples so you can have a first-hand understanding of some of the capabilities of Hadoop, the leading open source technology in the Big Data domain. There is a lot of excitement about Big Data and a lot of confusion to go with it.
