Big Data Application Performance Monitoring in Retail E-Commerce using Spark

Authors(2) :-Lavanya Marasa, Kalyani Kunchum

The global economy, today, is an increasingly complex environment with dynamic needs. Retailers are facing fierce competition and clients have become more demanding - they expect business processes to be faster, quality of the offerings to be superior and priced lower. Consequently, the quantum of data accumulate is at an all-time high as retailers generate giant volumes of data from numerous customer touch points across channels. For any fruitful business, we need to know more about customer preferences, interests, intent to purchase and more. It’s important to have answers to questions such as: “who are my customers?”, “what are they looking at?”, “how similar are they to one another” and “what else might they be interested in viewing?”. Apache Spark, the trendy big data processing engine that offers faster solutions for any failures compared to Hadoop, can be effectively utilized in finding patterns of relevance useful for the common man from these sites.

Authors and Affiliations

Lavanya Marasa
M.Tech, Department of Computer Science and Engineering, ALITS College, Affiliated to JNTUA, Andhra Pradesh, India
Kalyani Kunchum
Assistant Professor, Department of Computer Science and Engineering, ALITS College, Affiliated to JNTUA, Andhra Pradesh, India

Big Data Analytics; Retail Stream Analysis; Spark Streaming, Data Analysis, resource constraints, application bottlenecks, Globally Unique Identifier(GUI).

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 230-233
Manuscript Number : CSEIT172699
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Lavanya Marasa, Kalyani Kunchum, "Big Data Application Performance Monitoring in Retail E-Commerce using Spark ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.230-233, November-December-2017.
Journal URL : http://ijsrcseit.com/CSEIT172699

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