Conversational Database Management Tool: AI Driven Data Pipeline Orchestrator

Abstract Nowadays almost every enterprise depends on both historical data and real time data which supports most of the decision making. However, such type of analysis sometimes requires expertise in SQL, distributed real time data management, and statistical structural modeling, which limits the access for users those who doesn’t belong to technical field. We represents this tool as AI Driven Data Pipeline Orchestrator (AIDPO), a conversational analytics platform which developed to reduce this dependency on specialized skills. This system enables users to extract data and understand the analytical workflows through natural language by combining NL2SQL translation with real time Kafka Spark which helps to handle real time data sources, processing and time series forecasting. Also a dual assistant is integrated which separates the analytical requests from system level pipeline operations, allowing users through commands and execute both SQL and live streaming data. This tool was created using real and simulated real world datasets, achieving a forecasting RMSE of 14.23, anomaly detection precision of 0.80, and 94% accuracy of SQL selection queries . Now after analysing the final result we can say that our AIDPO can efficiently translate user inputs into understandable data operation. Keywords Natural Language to SQL (NL2SQL), Data Pipeline Orchestration, Real-Time Streaming Analytics, Forecasting, Anomaly Detection, Conversational AI.