Effective Analysis of Tweets Using Hadoop Ecosystem

Singh, Ravindra Kumar; Verma, Harsh Kumar

doi:10.1007/978-981-15-8297-4_48

Cited by 1 publication

(1 citation statement)

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“…Their experimental results showed that Apache Pig takes less time to execute than Apache Hive and is superior to Apache Hive for sentiment analysis on real-time tweets. A similar study was carried out by Singh and Verma (2021) recently and they also concluded that Apache Pig outperformed Apache Hive. According to Stanly Wilson (2019), Twitter generates a huge amount of valuable data every minute, and this data can be very beneficial for various decision-making.…”

Section: Literature Reviewsupporting

confidence: 55%

Developing a big data analytics platform using Apache Hadoop Ecosystem for delivering big data services in libraries

Singh

2024

DLP

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Purpose Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem. Design/methodology/approach The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries. Findings It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries. Research limitations/implications The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data. Practical implications The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time. Originality/value According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

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Section: Literature Reviewsupporting

confidence: 55%

Developing a big data analytics platform using Apache Hadoop Ecosystem for delivering big data services in libraries

Singh

2024

DLP

View full text Add to dashboard Cite

show abstract

Effective Analysis of Tweets Using Hadoop Ecosystem

Cited by 1 publication

References 18 publications

Developing a big data analytics platform using Apache Hadoop Ecosystem for delivering big data services in libraries

Developing a big data analytics platform using Apache Hadoop Ecosystem for delivering big data services in libraries

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