Architecting scalable cloud-based marketing platforms: Principles and best practices

Madare, Prasenjeet Mahadev (2025) Architecting scalable cloud-based marketing platforms: Principles and best practices. World Journal of Advanced Research and Reviews, 26 (1). pp. 1806-1812. ISSN 2581-9615

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Abstract

This article examines the architectural principles and implementation strategies for building scalable cloud-based marketing platforms capable of supporting modern digital marketing operations. We explore how evolving architectural paradigms—particularly microservices, containerization, and event-driven designs—create systems that accommodate current operational demands while adapting to changing business requirements. The article encompasses critical aspects of platform design, including data integration frameworks that unify customer interactions across channels, security architectures that protect sensitive marketing data, and performance optimization techniques that maintain responsiveness during high-volume campaigns. The article analyzes how containerization and orchestration technologies enable consistent deployments across environments, while various scaling strategies balance performance requirements with cost considerations as customer bases grow. The article also investigates emerging technologies including artificial intelligence, edge computing, and serverless architectures that are reshaping marketing platform capabilities. Through article examination of theoretical frameworks, practical implementation patterns, and real-world case studies, the article provides a comprehensive architectural blueprint for marketing technology leaders tasked with building next-generation platforms that deliver personalized customer experiences at scale while maintaining operational efficiency and security.

Item Type: Article
Official URL: https://doi.org/10.30574/wjarr.2025.26.1.1232
Uncontrolled Keywords: Cloud-Based Marketing Architecture; Microservices Scalability; Marketing Data Integration; Container Orchestration; AI-Driven Personalization
Depositing User: Editor WJARR
Date Deposited: 25 Jul 2025 14:48
Related URLs:
URI: https://eprint.scholarsrepository.com/id/eprint/1879