Data Analytic Models That Redress the Limitations of MapReduce

Data Analytic Models That Redress the Limitations of MapReduce

Uttama Garg
Copyright: © 2021 |Pages: 15
DOI: 10.4018/IJWLTT.20211101.oa7
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Abstract

The amount of data in today’s world is increasing exponentially. Effectively analyzing Big Data is a very complex task. The MapReduce programming model created by Google in 2004 revolutionized the big-data comput-ing market. Nowadays the model is being used by many for scientific and research analysis as well as for commercial purposes. The MapReduce model however is quite a low-level progamming model and has many limitations. Active research is being undertaken to make models that overcome/remove these limitations. In this paper we have studied some popular data analytic models that redress some of the limitations of MapReduce; namely ASTERIX and Pregel (Giraph) We discuss these models briefly and through the discussion highlight how these models are able to overcome MapReduce’s limitations.
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Underlying Technologies

In this section we include some of the basic technologies/developments that have played a vital role in develop-ment of various models discussed in this paper. These are:

  • •

    Google File System

  • •

    BigTable

Both Google File System and BigTable were very important for the development of MapReduce. They also form the basis of various other data analytic models.

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