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2019
This chapter introduces you to MapReduce programming. You will see how functional abstraction lead to real-life implementation. There are two key technical solutions that enable the use of map and reduce functions in practice for parallel processing of big data.
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This chapter introduces you to MapReduce programming. You will see how functional abstraction lead to real-life implementation. There are two key technical solutions that enable the use of map and reduce functions in practice for parallel processing of big data.
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On the performance projectability of MapReduce
4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, 2012A key challenge faced by users of public clouds today is how to request for the right amount of resources in the production datacenter that satisfies a target performance for a given cloud application. An obvious approach is to develop a performance model for a class of applications such as MapReduce.
Di Xie +2 more
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2016
The system of MapReduce (or Hadoop for an equivalent open source in Java) offers a simple framework to parallelize and execute parallel algorithms on massive data sets, commonly called Big Data (with size ranging from a few gigabytes to a few terabytes or even petabytes).
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The system of MapReduce (or Hadoop for an equivalent open source in Java) offers a simple framework to parallelize and execute parallel algorithms on massive data sets, commonly called Big Data (with size ranging from a few gigabytes to a few terabytes or even petabytes).
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2014
So, what’s next after discussing data modeling, security, and user role privileges management? With Cassandra query language (CQL), we can definitely manage basic query-based analytics via primary key and secondary indexes and keep data model denormalized as much as possible.
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So, what’s next after discussing data modeling, security, and user role privileges management? With Cassandra query language (CQL), we can definitely manage basic query-based analytics via primary key and secondary indexes and keep data model denormalized as much as possible.
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Proceedings of third international workshop on MapReduce and its Applications Date, 2012
The volume and complexity of data that must be analyzed in scientific applications is increasing exponentially. Often, this data is distributed, thus efficient processing of large distributed datasets is required, whilst ideally not introducing fundamentally new programming models or methods.
Andre Luckow +2 more
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The volume and complexity of data that must be analyzed in scientific applications is increasing exponentially. Often, this data is distributed, thus efficient processing of large distributed datasets is required, whilst ideally not introducing fundamentally new programming models or methods.
Andre Luckow +2 more
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2013
From automatically translating documents to analyzing electoral voting patterns; from computing personalized movie recommendations to predicting flu epidemics: all of these tasks are possible due to the success and proliferation of the MapReduce parallel programming paradigm.
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From automatically translating documents to analyzing electoral voting patterns; from computing personalized movie recommendations to predicting flu epidemics: all of these tasks are possible due to the success and proliferation of the MapReduce parallel programming paradigm.
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2016
Apache Hadoop is a distributed framework for storing and processing large quantities of data. Going over each of the terms in the previous statement, "distributed" implies that Hadoop is distributed across several (tens, hundreds, or even thousands) of nodes in a cluster.
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Apache Hadoop is a distributed framework for storing and processing large quantities of data. Going over each of the terms in the previous statement, "distributed" implies that Hadoop is distributed across several (tens, hundreds, or even thousands) of nodes in a cluster.
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Tagged MapReduce: Efficiently Computing Multi-analytics Using MapReduce
2011MapReduce is a programming paradigm for effective processing of large datasets in distributed environments, using the map and reduce functions. The map process creates (key, value) pairs, while the reduce phase aggregates same-key values. In other words, a MapReduce application defines and reduces one set of values for each key, which means that the ...
Pavlos Mitsoulis-Ntompos +2 more
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2014
The MapReduce model was developed by Google and Yahoo for their internal use. Google created the Hadoop distributed file system and Yahoo developed Pig Latin to handle their volume of data. These products became open source. Hadoop dominates the NoSQL market as part of the SMAQ stack, the NoSQL counterpart of the LAMP stack for websites.
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The MapReduce model was developed by Google and Yahoo for their internal use. Google created the Hadoop distributed file system and Yahoo developed Pig Latin to handle their volume of data. These products became open source. Hadoop dominates the NoSQL market as part of the SMAQ stack, the NoSQL counterpart of the LAMP stack for websites.
openaire +2 more sources