Data Analytic Models That Redress the Limitations of MapReduce

Uttama Garg
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Uttama Garg: Chandigarh University, Ajitgarh, India

International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 2021, vol. 16, issue 6, 1-15

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.

Date: 2021
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