Big Data -Tools and Technologies
Big Data -Tools and Technologies

Summary/Abstract: Big Data has become one of the hottest research areas today. Research suggests that analyzing Big Data can benefit businesses, scientific research and the public sector as well as development in every sphere. The need is to develop systems that can explore this potential to the maximum, without forgetting the challenges associated with its analysis, structure, scale, timeliness and privacy. Enterprise systems used to be the primary sources of data, but today many additional sources are contributing to the data pool, for instance sensors, social networking sites, online communities, web pages, email, images, documents, videos and music. The data captured is unstructured as opposed to the structured world of the past. Companies are facing the challenge of processing huge chunks of data, and have found that none of the existing centralized architectures can efficiently handle this huge volume of data. There has been a transformation in the data-processing systems architecture, moving from the centralized architecture to the distributed architecture. Hence, new technologies and distributed architectures are being created to harness this data. Several solutions to the Big Data problem have emerged which includes the Map Reduce environment created by Google, which is now available open-source in Hadoop. Hadoop’s distributed processing, Map Reduce algorithms and overall architecture are a major step towards achieving the promised benefits of Big Data. This paper presents a survey of various tools and technologies discussed in research and are being used to process Big Data. It also identifies the areas where further work is to be done. It is expected that identification of these problems would help researchers find solutions and help the industry make their implementation possible.

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