Building an E-commerce Platform with the MERN Stack
The swift expansion of e-commerce has demanded the creation of strong, adaptable, and user- friendly platforms. This study looks at building an e- commerce platform with the MERN stack, which is an effective combination of Node.js, Express.js, React, and MongoDB. Using the full-stack JavaScript solution provided by the MERN stack, developers may create dynamic and effective web apps. Examining the MERN stack's architectural elements in detail, this study highlights the system's capacity to manage heavy traffic loads and deliver flawless user experiences. Our goal in doing this research is to illustrate the benefits of using the MERN stack while creating modern e-commerce applications. To guarantee a dependable and secure platform, we also discuss performance optimization, security precautions, and best practices. The study's findings demonstrate how well the MERN stack works to create e-commerce platforms that are both maintainable and scalable. By using performance benchmarks and case studies, we show how the MERN stack can accommodate the many demands of contemporary e-commerce companies. According to the results, using the MERN stack may produce high- quality software at a much lower cost and in a much shorter amount of time than traditional methods. This article adds to the expanding body of knowledge on web development technologies by providing businesses and developers wishing to use the MERN stack for e- commerce solutions with useful advice and insights.
Authors
- Ashish Tiwari (ORCID: https://orcid.org/0000-0001-6278-9607)
- Dhiraj Kapila (ORCID: https://orcid.org/0000-0001-8064-6298)
- Paras Nath Singh
- Sarvesh Kumar
- Akash Shukla
- Apoorv Pachori
Institutions
- Lovely Professional University (IN)
- University of Lucknow (IN)
- Institute of Chartered Financial Analysts of India University, Jaipur (IN)
- Institute of Chartered Financial Analysts of India (IN)
Publication Details
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1109/ariia63345.2024.11051850
- Citations
- 3
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 1.59