MASTERING DATA PLATFORM DESIGN: INDUSTRY-AGNOSTIC PATTERNS FOR SCALE - Amarnath Immadisetty (INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 2024)
Modern enterprises across industries face unprecedented challenges in managing and leveraging their data assets effectively. This comprehensive technical article presents industry-agnostic best practices for building scalable data platforms that can handle increasing data volumes while maintaining performance and reliability. By examining key architectural components, including modular design patterns, real-time processing capabilities, and cloud-native solutions, this article provides a practical framework for platform development. The article delves into critical considerations such as data governance frameworks, quality assurance mechanisms, and platform observability while addressing security requirements and cost optimization strategies. Through detailed analysis of automation approaches, elasticity patterns, and future-ready design principles, we demonstrate how organizations can build resilient data platforms that adapt to evolving business needs. Our findings, supported by real-world implementation examples, offer actionable insights for technical leaders and architects tasked with designing and maintaining enterprise-scale data infrastructure. This framework not only ensures operational excellence but also enables organizations to establish a strong foundation for data-driven innovation and sustainable growth.