Mrs. Rajani Kumari Vaddepalli is a senior data engineer whose research and professional work span data engineering, artificial intelligence, machine learning, and cloud-native systems, with a strong emphasis on scalable, reliable, and ethically aligned data ecosystems. Her scholarly contributions explore advanced topics such as real-time stream processing, schema drift adaptation, hybrid consensus blockchain models, AI security, cross-platform interoperability, culturally adaptive AI visualizations, and responsible data governance. She has also advanced methods in anomaly detection, automated feature engineering, explainable AI, and federated learning for secure multi-institutional collaboration. Her publications demonstrate a consistent focus on integrating technical innovation with practical industry challenges, offering frameworks that bridge regulatory expectations, operational efficiency, and organizational trust in AI-driven decision systems. Complementing her academic footprint, her professional background reflects deep expertise in designing enterprise-grade data pipelines, optimizing cloud data warehousing, and ensuring resilient distributed architectures across diverse sectors including healthcare, retail, finance, logistics, and public governance. She brings a strategic understanding of how AI, metadata automation, and dynamic fault-tolerance mechanisms can enhance the transparency and reliability of modern data platforms. Through both research and practice, she contributes to building data and AI systems that are scalable, culturally aware, fair, and aligned with global standards for security and accountability, making her a significant voice in the evolving landscape of intelligent data engineering.
Profile: Google Scholar
Featured Publications
Vaddepalli, R. K. (2022). Streaming vs. batch at scale: How Snowflake’s real-time processing stacks up against on-premises data warehouses. ISCSITR – International Journal of Cloud Computing (ISCSITR-IJCC), 3(1), 9–26.
Vaddepalli, R. K. (2024). Toward a greener blockchain for document verification: Balancing energy efficiency and security with hybrid consensus models. European Journal of Advances in Engineering and Technology, 11(4), 186–191.
Vaddepalli, R. K. (2024). Moving beyond generic solutions: Crafting industry-tailored ethical frameworks for unbiased generative AI in B2B sales. Journal of Scientific and Engineering Research, 11(6), 173–179.
Vaddepalli, R. K. (2021). Adaptive AI-driven data integration: Navigating regulatory challenges in healthcare, finance, retail, and logistics. International Journal of Artificial Intelligence and Machine Learning (QIT Press).
Vaddepalli, R. K. (2023). AutoSchema: A self-learning framework for detecting and adapting to schema drift in real-time data streams. European Journal of Advances in Engineering and Technology, 10(7), 94–100