Data Analytics and Modern Data Architectures

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Published: Apr 30, 2026

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Dr. Pavika Sharma

Abstract

Data analytics plays a vital role in extracting meaningful insights from large volumes of data generated in the digital era.
Modern organizations rely on advanced data analytics techniques and modern data architectures to support data-driven
decision making, improve operational efficiency, and gain competitive advantage. This chapter explains the concept of data
analytics, types of data, and the evolution of data architectures from traditional systems to modern, cloud-based platforms. It
also discusses key components such as data lakes, data warehouses, and data pipelines, along with their role in enabling real
time and large-scale analytics. The chapter further highlights challenges related to data quality, security, and governance, and
outlines emerging trends that are shaping the future of data analytics and modern data architectures.

Article Details

How to Cite
Sharma, D. P. (2026). Data Analytics and Modern Data Architectures . Architecting the Intelligent Future, 1(1). Retrieved from https://publication.shreegprestige.com/index.php/book1/article/view/46
Issue
Vol. 1 No. 1 (2026): Architecting the Intelligent Future
Section
Articles
Author Biography

Dr. Pavika Sharma

Assistant Professor, Department of Electronics and Communication Engineering
Bhagwan Parshuram Institute of Technology (BPIT), New Delhi, India

References

Brahmandam, L. M. K. (2026). A Decision Framework for Multi-Cloud Microservice Deployment across AWS and GCP: Empirical Evaluation of EKS, Cloud Functions, Cloud Run, and Cross-Cloud Networking Patterns. International Journal of Emerging Trends in Computer Science and Information Technology, 7(1), 365-373.

Sunkara, R. (2026). Serverless Architecture Patterns for Enterprise AI Agents: ECS Fargate, OpenSearch k-NN, and DynamoDB for Knowledge-Grounded LLM Workflows. International Journal of AI, BigData, Computational and Management Studies, 7(2), 197-201.

Seknametla, P. R. (2026). Autonomous cloud infrastructure in the food industry: Leveraging AI for intelligent orchestration and monitoring. In Modernizing the Food Industry: AI-Powered Infrastructure, Security, and Supply Chain Innovation (pp. 121-144). IGI Global Scientific Publishing.

OpenAI. (2023). GPT-4 technical report. arXiv. https://arxiv.org/abs/2303.08774

Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64–88.

Schwab, K. (2017). The fourth industrial revolution. Crown Business.

Shneiderman, B. (2022). Human-centered AI. Oxford University Press.

Simon, H. A. (1996). The sciences of the artificial (3rd ed.). MIT Press.

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

World Economic Forum. (2023). The future of jobs report 2023.

Genkin, M., & McArthur, J. J. (2022). B-SMART: A reference architecture for artificially intelligent autonomic smart buildings. arXiv. https://arxiv.org/abs/2211.03219

Wooldridge, M. (2021). A brief history of artificial intelligence: What it is, where we are, and where we are going. Flatiron Books.

Gantikota, S. (2026). Securing Microservice Communication across WCF, JAX-RS, and Spring Boot: Authentication, Authorization, and Audit Patterns for Healthcare Interoperability. American International Journal of Computer Science and Technology, 8(2), 15-20.