Big Data Application Performance Monitoring in Retail E-Commerce using Spark
 |
International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2017 by IJETT Journal | ||
| Volume-50 Number-2 |
||
| Year of Publication : 2017 | ||
| Authors : Lavanya Marasa, Kalyani Kunchum |
||
| DOI : 10.14445/22315381/IJETT-V50P211 |
Citation
Lavanya Marasa, Kalyani Kunchum "Big Data Application Performance Monitoring in Retail E-Commerce using Spark", International Journal of Engineering Trends and Technology (IJETT), V50(2),63-66 August 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
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.
Reference
[1] Amarbir Singh and Palwinder Singh "Analysis of various Tools in Big Data Scenario”, ISSN:2394-2231, vol. 03, Issue 02,Mar - Apr, 2016.
[2] Kiejin Park and Limei Peng "Second- Generation Big Data Systems," IEEE Computer, vol. 11, no. 14, pp. 8221-8225, 2016.
[3] S. Liu et al., "TASC: Topic-Adaptive Sentiment Classification on Dynamic transaction," IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 6, pp. 1696 - 1709,2015.
[4] T. Sakaki, O. Makoto and M. Yutaka, "Tweet analysis for real-time event detection and earthquake reporting system development," vol. 25, no. 4, pp. 919-931, 2013.
[5] AlexandarShkapsky, Mohan Yang and Matteo Interlandi,"Big Data Analytics with Datalog Queries on Spark”,2016.
[6] Sparks, Evan; Talwalkar, Ameet (2013-08-06). "Spark Meetup: MLbase, Distributed Machine Learning with Spark". slideshare.net. Spark User Meetup, San Francisco, California. Retrieved 10 February 2014.
[7] Jump up ^ "MLlib | Apache Spark". spark.apache.org. Retrieved 2016-01-18.
[8] Malak, Michael (1 July 2016). Spark GraphX in Action. Manning. p. 9. ISBN 9781617292521. Giraph is limited to slow Hadoop Map/Reduce.
Keywords
Big Data Analytics; Retail Stream Analysis; Spark Streaming, Data Analysis, resource constraints, application bottlenecks, Globally Unique Identifier(GUI).