Review of Data Pipelines and Streaming for Generative AI Integration: Challenges, Solutions, and Future Directions
Satyadhar Joshi · International Journal of Research Publication and Reviews · 2025
Generative AI (GenAI) is revolutionizing various industries, but its effectiveness heavily relies on access to timely and relevant data.This paper explores the critical role of real-time data pipelines in powering GenAI applications.We synthesize existing literature, categorizing it into key areas: data integration, streaming platforms, vector databases, and architectural patterns.We discuss the challenges and opportunities in building robust and scalable real-time data pipelines for GenAI, emphasizing the importance of data freshness, accuracy, and efficient processing.This work provides a valuable overview for practitioners and researchers seeking to leverage real-time data for enhanced GenAI capabilities.The intersection of generative artificial intelligence (GenAI) and big data infrastructure has led to novel data management techniques, including data streaming, integration, and vector databases.This paper explores these techniques, their applications, and the critical role of data pipelines in optimizing AI-driven decision-making.We survey contemporary methodologies and highlight future challenges and opportunities in deploying real-time GenAI applications.The integration of data streaming platforms with generative AI (GenAI) has emerged as a critical area of research, enabling real-time data processing and enhancing AI applications.This paper reviews the current state of the art, focusing on the role of technologies like Apache Kafka, vector databases, and cloud-based solutions in addressing challenges such as data freshness, scalability, and integration complexity.We also explore future directions, including the use of retrieval-augmented generation (RAG) and real-time data pipelines, to unlock the full potential of GenAI.This review synthesizes insights from recent studies, industry practices, and emerging trends to provide a comprehensive understanding of the field.