Building Scalable Ecommerce Platforms: Microservices and Cloud-Native Approaches
Keywords:
Ecommerce, Scalability, Microservices, Cloud-Native, KubernetesAbstract
One of the most important aspects of digital commerce today is e-commerce platforms that can grow progressively, are responsive to market needs, and also encourage continuous innovation and that is the only way to remain competitive. This post provides insights on why microservices architecture and cloud-native technologies are now the backbone of scalable ecommerce systems. It exemplifies the transition from traditional monolithic systems to distributed, service-oriented architectures that are more resilient and reliable due to the flexibility and the ability to launch faster. The author illustrates several important ways, such as running containers, using API gateways, service discovery ability, and the continuous delivery pipeline, in the context of how these activities of their e-commerce platforms respond to erratic traffic, create a completely different user experience, and scale on demand. Furthermore, the text also believes that cloud-native technologies, such as Kubernetes, serverless computing opportunities, and managed services, are the key to simplified infrastructure management and can lead to the fostering of operational efficiency. That is, the excerpt contains a discussion of each of these pieces of technology and the benefits they may bring. Besides the topic, readers may learn how changing a middle-sized retail brand’s inefficient legacy platform into a real-time, scalable platform that is already realized and new in the market is their response to the adoption of microservice and cloud-native practices, the result of that being quite perceptible in both performance and customer satisfaction. This content is centered on the fact that by not just introducing some refreshing techniques and tools but also being able to attain tangible business value, said piece can help illustrate why such approaches are not merely a technological update but a strategic move in digital-first world e-commerce businesses to be successful.
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References
Raj, Pethuru, Skylab Vanga, and Akshita Chaudhary. Cloud-Native Computing: How to design, develop, and secure microservices and event-driven applications. John Wiley & Sons, 2022.
Srivastava, Rajiv. Cloud Native Microservices with Spring and Kubernetes: Design and Build Modern Cloud Native Applications using Spring and Kubernetes (English Edition). BPB Publications, 2021.
Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “The Role of Generative AI in Salesforce CRM: Exploring How Tools Like ChatGPT and Einstein GPT Transform Customer Engagement”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 12, no. 1, May 2024, pp. 50-66
Chinamanagonda, S. (2023). Cloud-native Databases: Performance and Scalability-Adoption of cloud-native databases for improved performance. Advances in Computer Sciences, 6(1).
Mahajan, A., Gupta, M. K., & Sundar, S. (2018). Cloud-Native Applications in Java: Build microservice-based cloud-native applications that dynamically scale. Packt Publishing Ltd.
Kumari, A., & Mohan, K. S. (2023). A Cloud Native Framework for Real-time Pricing in e-Commerce. International Journal of Advanced Computer Science and Applications, 14(4).
Atluri, Anusha, and Teja Puttamsetti. “Mastering Oracle HCM Post-Deployment: Strategies for Scalable and Adaptive HR Systems”. American Journal of Autonomous Systems and Robotics Engineering, vol. 1, Apr. 2021, pp. 380-01
Veluru, Sai Prasad. “AI-Driven Data Pipelines: Automating ETL Workflows With Kubernetes”. American Journal of Autonomous Systems and Robotics Engineering, vol. 1, Jan. 2021, pp. 449-73
KODAKANDLA, N. (2021). Serverless Architectures: A Comparative Study of Performance, Scalability, and Cost in Cloud-native Applications. Iconic Research And Engineering Journals, 5(2), 136-150.
Paidy, Pavan, and Krishna Chaganti. “Securing AI-Driven APIs: Authentication and Abuse Prevention”. International Journal of Emerging Research in Engineering and Technology, vol. 5, no. 1, Mar. 2024, pp. 27-37
Gilbert, J. (2018). Cloud Native Development Patterns and Best Practices: Practical architectural patterns for building modern, distributed cloud-native systems. Packt Publishing Ltd.
Anand, Sangeeta, and Sumeet Sharma. “Self-Healing Data Pipelines for Handling Anomalies in Medicaid and CHIP Data Processing”. International Journal of AI, BigData, Computational and Management Studies, vol. 5, no. 2, June 2024, pp. 27-37
Al Kiswani, J. H. A. (2019). Smart-Cloud: A Framework for Cloud Native Applications Development (Doctoral dissertation, University of Nevada, Reno).
Yasodhara Varma. “Modernizing Data Infrastructure: Migrating Hadoop Workloads to AWS for Scalability and Performance”. Newark Journal of Human-Centric AI and Robotics Interaction, vol. 4, May 2024, pp. 123-45
Desai, P. (2022). Microservices-Based Storage Architectures for Scalable Data Platforms. International Journal of Emerging Research in Engineering and Technology, 3(2), 19-27.
Sangaraju, Varun Varma. "INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING."
Kratzke, N., & Siegfried, R. (2021). Towards cloud-native simulations–lessons learned from the front-line of cloud computing. The Journal of Defense Modeling and Simulation, 18(1), 39-58.
Kupunarapu, Sujith Kumar. "Data Fusion and Real-Time Analytics: Elevating Signal Integrity and Rail System Resilience." International Journal of Science And Engineering 9.1 (2023): 53-61.
Prosper, J. (2019). Optimizing Cloud-Native AI Architectures for Seamless Omni-Channel Retail Integration.
Syed, Ali Asghar Mehdi. “Edge Computing in Virtualized Environments: Integrating Virtualization and Edge Computing for Real-Time Data Processing”. Essex Journal of AI Ethics and Responsible Innovation, vol. 2, June 2022, pp. 340-63
Chaganti, Krishna Chiatanya. "Securing Enterprise Java Applications: A Comprehensive Approach." International Journal of Science And Engineering 10.2 (2024): 18-27.
Fitria, N. (2021). Comparing Software-Defined Perimeter and Zero-Trust Architectures for Secure, Cloud-Native Online Retail Infrastructures. International Journal of Applied Business Intelligence, 1(12), 12-22.
Atluri, Anusha. “Redefining HR Automation: Oracle HCM’s Impact on Workforce Efficiency and Productivity”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 1, June 2021, pp. 443-6
Nookala, G. (2023). Microservices and Data Architecture: Aligning Scalability with Data Flow. International Journal of Digital Innovation, 4(1).
Paidy, Pavan. “Adaptive Application Security Testing With AI Automation”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 1, Mar. 2023, pp. 55-63
Cocconi, D., & Villarreal, P. (2020, October). Microservices-based Approach for a Collaborative Business Process Management Cloud Platform. In 2020 XLVI Latin American Computing Conference (CLEI) (pp. 128-137). IEEE.
Khan, O. M. A., & Chandaka, A. (2021). Developing Microservices Architecture on Microsoft Azure with Open Source Technologies. Microsoft Press.
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