How to Cite:
R.Vishwa, "Building Scalable Data Infrastructure for Generative AI Models: Challenges and Solutions" American International Journal of Computer Science and Technology, Vol. 1, No. 1, pp. 32-37, 2025.

Abstract:
The rapid advancement of Generative AI models has underscored the necessity for robust and scalable data infrastructures capable of managing vast datasets and complex computational requirements. This paper explores the unique challenges encountered in building such infrastructures, including data acquisition, storage, processing, and real-time access. We analyze existing solutions and propose best practices for designing architectures that ensure efficiency, scalability, and reliability. By examining case studies and current industry practices, the paper provides a comprehensive framework for developing data infrastructures tailored to the demands of Generative AI applications.
Keywords: Generative AI, Data Infrastructure, Scalability, Data Engineering, Cloud Computing, Real-time Data Processing, AI Workloads.

References:
1. Ganguly, A. “Data Pipelines in Generative AI.” In Scaling Enterprise Solutions with Large Language Models. Apress, 2025. SpringerLink
2. Sarker, Arup Kumar; Alsaadi, Aymen; Halpern, Alexander James; Tangella, Prabhath; Titov, Mikhail; von Laszewski, Gregor; Jha, Shantenu; Fox, Geoffrey. “Deep RC: A Scalable Data Engineering and Deep Learning Pipeline.” arXiv preprint, February 2025. arXiv
3. Li, Shigang; Hoefler, Torsten. “Chimera: Efficiently Training Large Scale Neural Networks with Bidirectional Pipelines.” arXiv preprint, 2021. arXiv
4. Vasa, Yeshwanth; Jaini, Santosh; Singirikonda, Prudhvi. “Design Scalable Data Pipelines For AI Applications.” NVEO Journal, Vol. 8, Issue 1, 2021. nveo.org
5. Sirigade, Raghavendra. “Creating Efficient and Scalable Data Pipelines for Cloud Based Analytics.” International Journal of Computer Engineering and Technology (IJCET), Vol. 15, Issue 5, September October 2024. IAEME
6. Patnaik, Amlan Jyoti. “Generative AI and Machine Learning based Modern Data Architecture with AWS Cloud and Snowflake.” International Journal of Computer Trends and Technology (IJCTT), Vol. 71, No. 7, 2023. Seventh Sense Research Group®
7. Basani, Maria Anurag Reddy. “Generative AI Powered Framework for Scalable and Real Time Data Quality Management in Databricks.” International Journal of Computer Applications, Vol. 186, Number 80, 2025. IJCA
8. Guțu, Bogdan Mihai; Popescu, Nirvana. “Exploring Data Analysis Methods in Generative Models: From Fine Tuning to RAG Implementation.” Computers, 2024, 13(12), Article 327. MDPI
9. Mustafa, Fahad; Gilbert, Albert. “Scalable Data Architectures for Generative AI: A Comparison of AWS and Google Cloud Solutions.” ResearchGate, October 2024. ResearchGate
10. “On the Challenges and Opportunities in Generative AI.” arXiv e prints, March 2024, arXiv:2403.00025. ADS
11. “Data Governance Challenges in the Age of Generative AI.” DZone (article), 2024. DZone
12. “How Big Data Supports Gen AI.” Prasenjit, SQLServerCentral, May 2024. SQLServerCentral
13. Infrastructure for a RAG capable generative AI application using Vertex AI and AlloyDB for PostgreSQL. Google Cloud Architecture Center, reviewed December 2024. Google Cloud
14. “Building Reliable and Scalable Generative AI Infrastructure on AWS with Ray and Anyscale.” AWS Partner Network Blog, 2024.