Spark is superior to Map Reduce over Big Data

Farook, Shaik; Narayana, G.; Rao, Bingbing

doi:10.5120/ijca2016907721

Cited by 1 publication

(2 citation statements)

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“…The function of Spark extends over that of MapReduce in terms of one of its major functionalities: RDDs (Resilient Distributed Datasets), which is the primary data abstraction in Apache Spark [19,20] .…”

Section: Sparkmentioning

confidence: 99%

“…However, Spark saves the currently used data into HDFS and then builds new results by working on these saved historical data. The integration of powerful tools for addressing big data problems with high performance and which have easy access to unified programming APIs (including Python, Scala, and Java) is what has made Spark one of the major projects in Apache [19][20][21][22][23][24] .…”

Section: Sparkmentioning

confidence: 99%

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Big data storage and management in SaaS applications

Zheng

Chugh

2017

J. Commun. Inf. Netw.

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Abstract:As an important service model for advanced computing, SaaS uses a defined protocol that manages services and applications. The popularity of advanced computing has reached a level that has led to the generation of large data sets, which is also called Big data. Big data is evolving with great velocity, large volumes, and great diversity. Such an amplification of data has brought into question the existing database tools in terms of their capabilities. Previously, storage and processing of data were simple tasks; however, it is now one of the biggest challenges in the industry. Experts are paying close attention to big data. Designing a system capable of storing and analyzing such data in order to extract meaningful information for decision-making is a priority. The Apache Hadoop, Spark, and NoSQL databases are some of the core technologies that are being used to solve these issues. This paper contributes to the solutions to the issues of big data storage and processing. It presents an analysis of the current technologies in the industry that could be useful in this context. Efforts have been focused on implementing a novel Trinity model, which is built using the lambda architecture with the following technologies: Hadoop, Spark, Kafka, and MongoDB.

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Section: Sparkmentioning

confidence: 99%

Section: Sparkmentioning

confidence: 99%