Building Scalable Data Infrastructure for Generative AI Models: Challenges and Solutions
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-132Keywords:
Generative AI, Data Infrastructure, Scalability, Data Engineering, Cloud Computing, Real-time Data Processing, AI WorkloadsAbstract
The rapid advancement of Generative AI models, such as large language models (LLMs) and diffusion-based image generators, has significantly increased the demand for sophisticated data infrastructures. These infrastructures must efficiently manage vast, heterogeneous datasets and support complex computational pipelines across training, fine-tuning, and inference stages. This paper investigates the multifaceted challenges involved in building and maintaining such systems, including scalable data acquisition, distributed storage solutions, high-throughput data processing frameworks, and low-latency access mechanisms required for real-time AI applications. We explore existing technologies and architectural paradigms such as data lakes, data meshes, and hybrid cloud architectures that have emerged to support the growing needs of Generative AI. Key considerations such as data governance, privacy, model versioning, and compliance with regulatory frameworks are also examined. Through detailed analysis of real-world deployments and case studies from leading AI organizations, we identify critical trade-offs and present a set of best practices for infrastructure design. The paper culminates in the proposal of a modular and extensible reference architecture that balances performance, cost-efficiency, and adaptability, aimed at supporting current and next-generation Generative AI workloads. This comprehensive framework serves as a guide for researchers, data engineers, and AI practitioners involved in the development of scalable AI systems
References
[1] Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113. https://doi.org/10.1145/1327452.1327492
[2] Patibandla, K. K., Daruvuri, R., & Mannem, P. (2025, April). Enhancing Online Retail Insights: K-Means Clustering and PCA for Customer Segmentation. In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT) (pp. 388-393). IEEE.
[3] Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. Proceedings of the 2nd USENIX conference on Hot topics in cloud computing.
[4] Gopichand Vemulapalli Subash Banala Lakshmi Narasimha Raju Mudunuri, Gopi Chand Vegineni ,Sireesha Addanki ,Padmaja Pulivarth, (2025/4/16). Enhancing Decision-Making: From Raw Data to Strategic Insights for Business Growth. ICCCT'25– Fifth IEEE International Conference on Computing & Communication Technologies. IEEE.
[5] Optimizing Boost Converter and Cascaded Inverter Performance in PV Systems with Hybrid PI-Fuzzy Logic Control - Sree Lakshmi Vineetha. B, Muthukumar. P - IJSAT Volume 11, Issue 1, January-March 2020,PP-1-9,DOI 10.5281/zenodo.14473918
[6] Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. https://doi.org/10.48550/arXiv.2005.14165
[7] Enhancement of Wind Turbine Technologies through Innovations in Power Electronics, Sree Lakshmi Vineetha Bitragunta, IJIRMPS2104231841, Volume 9 Issue 4 2021, PP-1-11.
[8] Hazell, J., & Huang, A. (2023). Data infrastructure for generative AI: Principles and patterns. Databricks Blog. https://www.databricks.com/blog
[9] Sudheer Panyaram, (2025/5/18). Intelligent Manufacturing with Quantum Sensors and AI A Path to Smart Industry 5.0. International Journal of Emerging Trends in Computer Science and Information Technology. 140-147.
[10] Puvvada, R. K. "The Impact of SAP S/4HANA Finance on Modern Business Processes: A Comprehensive Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11.2 (2025): 817-825.
[11] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28.
[12] Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., ... & Zaremba, W. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374. https://arxiv.org/abs/2107.03374
[13] Mohanarajesh, Kommineni (2024). Generative Models with Privacy Guarantees: Enhancing Data Utility while Minimizing Risk of Sensitive Data Exposure. International Journal of Intelligent Systems and Applications in Engineering 12 (23):1036-1044.
[14] Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253. https://doi.org/10.1017/S0140525X16001837
[15] Lakshmi Narasimha Raju Mudunuri, Praveen Kumar Maroju, Venu Madhav Aragani, (2025/1/9), Leveraging NLP-Driven Sentiment Analysis for Enhancing Decision-Making in Supply Chain Management. 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 1-6 IEEE.
[16] Sadilek, A., Kautz, H., & Silenzio, V. (2012). Predicting disease transmission from geo-tagged micro-blog data. AAAI Conference on Artificial Intelligence.
[17] S. Panyaram, "Automation and Robotics: Key Trends in Smart Warehouse Ecosystems," International Numeric Journal of Machine Learning and Robots, vol. 8, no. 8, pp. 1-13, 2024.
[18] Ashima Bhatnagar Bhatia Padmaja Pulivarthi, (2024). Designing Empathetic Interfaces Enhancing User Experience Through Emotion. Humanizing Technology With Emotional Intelligence. 47-64. IGI Global.
[19] Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561–2573. https://doi.org/10.1016/j.jpdc.2014.01.003
[20] Vegineni, Gopi Chand, and Bhagath Chandra Chowdari Marella. "Integrating AI-Powered Dashboards in State Government Programs for Real-Time Decision Support." AI-Enabled Sustainable Innovations in Education and Business, edited by Ali Sorayyaei Azar, et al., IGI Global, 2025, pp. 251-276. https://doi.org/10.4018/979-8-3373-3952-8.ch011
[21] Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3306618.3314244
[22] RK Puvvada . “SAP S/4HANA Finance on Cloud: AI-Powered Deployment and Extensibility” - IJSAT-International Journal on Science and …16.1 2025 :1-14.
[23] P. K. Maroju, "Conversational AI for Personalized Financial Advice in the BFSI Sector," International Journal of Innovations in Applied Sciences and Engineering, vol. 8, no.2, pp. 156–177, Nov. 2022.
[24] Kommineni, M., & Chundru, S. (2025). Sustainable Data Governance Implementing Energy-Efficient Data Lifecycle Management in Enterprise Systems. In Driving Business Success Through Eco-Friendly Strategies (pp. 397-418). IGI Global Scientific Publishing.
[25] Pulivarthy, P., & Whig, P. (2025). Bias and fairness addressing discrimination in AI systems. In Ethical dimensions of AI development (pp. 103–126). IGI Global. Available online: https://www.igi-global.com/chapter/bias-and-fairness-addressing-discrimination-in-ai-systems/359640 (accessed on 27 February 2025).
[26] Panyaram, S., & Kotte, K. R. (2025). Leveraging AI and Data Analytics for Sustainable Robotic Process Automation (RPA) in Media: Driving Innovation in Green Field Business Process. In Driving Business Success Through Eco-Friendly Strategies (pp. 249-262). IGI Global Scientific Publishing.
[27] Venu Madhav Aragani, 2025, “Implementing Blockchain for Advanced Supply Chain Data Sharing with Practical Byzantine Fault Tolerance (PBFT) Alogorithem of Innovative Sytem for sharing Suppaly chain Data”, IEEE 3rd International Conference On Advances In Computing, Communication and Materials.
[28] Mudunuri, L. N., Hullurappa, M., Vemula, V. R., & Selvakumar, P. (2025). “AI-Powered Leadership: Shaping the Future of Management. In F. Özsungur (Ed.), Navigating Organizational Behavior in the Digital Age With AI” (pp. 127-152). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8442-8.ch006
[29] B. C. C. Marella and D. Kodi, “Generative AI for fraud prevention: A new frontier in productivity and green innovation,” In Advances in Environmental Engineering and Green Technologies, IGI Global, 2025, pp. 185–200
[30] Sree Lakshmi Vineetha Bitragunta* and Muthukumar Paramasivan, Midterm Dynamic Simulation for the Governance of Reserves in Systems with Elevated Renewable Energy Integration, Journal of Artificial Intelligence, Machine Learning and Data Science, Vol: 1 & Iss: 1, PP-1-7, 2023.
[31] Bhagath Chandra Chowdari Marella, “From Silos to Synergy: Delivering Unified Data Insights across Disparate Business Units”, International Journal of Innovative Research in Computer and Communication Engineering, vol.12, no.11, pp. 11993-12003, 2024.
[32] Noor, S., Awan, H.H., Hashmi, A.S. et al. “Optimizing performance of parallel computing platforms for large-scale genome data analysis”. Computing 107, 86 (2025). https://doi.org/10.1007/s00607-025-01441-y.
[33] Arpit Garg, “CNN-Based Image Validation for ESG Reporting: An Explainable AI and Blockchain Approach”, Int. J. Comput. Sci. Inf. Technol. Res., vol. 5, no. 4, pp. 64–85, Dec. 2024, doi: 10.63530/IJCSITR_2024_05_04_007
[34] Vootkuri, C. (2025). Multi-Cloud Data Strategy & Security for Generative AI.
[35] Batchu, R.K., Settibathini, V.S.K. (2025). Sustainable Finance Beyond Banking Shaping the Future of Financial Technology. In: Whig, P., Silva, N., Elngar, A.A., Aneja, N., Sharma, P. (eds) Sustainable Development through Machine Learning, AI and IoT. ICSD 2024. Communications in Computer and Information Science, vol 2196. Springer, Cham. https://doi.org/10.1007/978-3-031-71729-1_12
