Performance evaluation of distributed framework over YARN cluster manager

Vellaipandiyan, Solaimurugan; Raja, P.

doi:10.1109/iccic.2016.7919520

Cited by 3 publications

(1 citation statement)

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“…Furthermore, for massively large datasets, in fault tolerance Spark was reported to be crashed with JVM heap exception while Hadoop still performed its task. So, in this study, we prefer to use Hadoop in lieu of spark [24,25].…”

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confidence: 99%

Big data clustering with varied density based on MapReduce

et al. 2019

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With the recent growth and advancement in Information Technology, data has produced at a very high rate in a variety of fields, which have presented to users in a structured, semi-structured, and non-structured mode [1]. New technologies for storing and extracting useful information from this volume of data (big data) have needed because the discovery and extraction of useful information and knowledge from this data volume are difficult, hence, other traditional relational databases cannot meet the needs of users [2]. If you are dealing with data beyond the capabilities of existing software, you are, in fact, dealing with big data. Large data is commonly referred to as a set of data that exceeds the extent to which it can be extracted, refined, managed, and processed by standard management tools and databases. In other words, the term "big data" refers to data that is complex in terms of volume and variety; however, it is not possible to manage them with traditional tools, and therefore, they cannot extract their hidden knowledge and knowledge at predetermined times [3, 4]. Big data is, therefore, defined with three attributes of volume, velocity, and variety that are called Gartner's commentary; some scholars have in addition; IBM cited the

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confidence: 99%