Analysis of the Big Data market by IDC's global analyst team covers the technologies, integration strategies, use cases, and go-to-market strategies of vendors in this arena and provides insight into the evolution of these technologies and application of Big Data technologies for economic gain.


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From a compute perspective, most data-intensive jobs can be partitioned and can be run efficiently on standard clusters with memories that are physically and logically distributed. A smaller number of problems that are less uniform and more communications dependent.


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These technologies include relational databases, NoSQL databases, key value stores, text analytics, ontologies, categorizers, schema extractors, search indexes, parallel file systems, complex event processing engines, graph databases, and other technologies that organize and manage data at rest and in flight.


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Another dimension that identifies specific analysis and discovery software is the complexity of analysis. There are tools for highly complex descriptive and predictive analysis and tools that simply help with basic aggregation and access to information. Newer tools such as Hadoop, MapReduce, and Dryad (along with methodologies including graph analysis) are proving adept at expediting searches through the large, irregular data sets that characterize many Big Data problems.


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