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Big Data is a term closely related with the development of the Internet. Due to the existence of such a global network, it is possible to share and collectthis huge amount of data as well as to provide Big Data processing and analytics as a service to a broader audience throughout the entire network.
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Hadoop is the de facto standard for implementation of MapReduce applications. It is composed of one or more master nodes and any number of slave nodes.
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In this article we will cover three new features which give us extended parameter handling.
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So far, the cardinalities and distributions that characterize the data have been discussed. Here, one will assess the task at hand in terms of the computational workload relative to the resources you have at one’s disposal.
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Back in the day, advertising was not too efficient. But with digital advertising comes the opportunity to discover what works and what doesn’t via the data collected as users interact with online ads.
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Dealing with Big Data may require powerful machines and vast storage. Focusing on a specific type of data, however, it is possible to use commodity computers in a small lab environment to handle Big Data generated from large complex graphs such as online social networks (OSNs) and other types of Internet applications, and produce useful information.
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This section provides a complete MapReduce implementation of the secondary sort problem using the Hadoop framework.
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A secondary sort problem relates to sorting values associated with a key in the reduce phase. Sometimes, it is called value-to-key conversion. The secondarysorting technique will enable us to sort the values (in ascending or descending order) passed to each reducer. Concrete examples will be provided of how toachieve secondary sorting in ascending or descending order.
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MapReduce is a programming paradigm that allows for massive scalability across hundreds or thousands of servers in a cluster environment. The term MapReduce originated from functional programming and was introduced by Google in a paper called “MapReduce: Simplified Data Processing on Large Clusters.” Google’s MapReduceimplementation is a proprietary solution and has not yet been released to the public.
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In this article we are going to turn to mobile testing. If you are a quality assurance engineer having interest in mobile device testing as well as in mobile device application testing, this article will turn out to be a helping guide.
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We’ll start with a sample of 9 million observations, a small-enough sample to fit into memory in order to do some quick calculations of cardinality and distributions.
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