AI-optimized data Lakes for Real-Time Big Data Management and Autonomous Query Optimization

Due to the massive explosion of big data, efficient data lakes with ‘real-time’ data management and ‘autonomous’ query optimization have become inevitable. One of the problems attached to traditional data lakes is the difficulty of retrieving data, which is slow to query, inefficiently, and with high complexities of schema evolution the ability of the data lake to optimize workloads dynamically. To overcome these problems, the work presented in this paper brings forward the concept of an AI-optimized data lake framework that exploits machine learning and deep learning for intelligent data ingestion, adaptive indexing as well as self-optimizing query execution. The system is proposed to incorporate hierarchical storage management with AI-driven, self-studying indexing mechanisms, and reinforcement learning-aided query optimization to improve data retrieval efficiency. Furthermore, through an integrated AI-powered anomaly detection system, data processing is made robust and reliable in real time. On top of that the framework also involves autonomous governance and security that can use explainable AI for compliance auditing and a dynamic role-based access control (RBAC). Experimental evaluations show that, with up to a 90% time saving, and with real-time decision-making capabilities, it improves over both aspects in a variety of data workloads. This research brings forth the integration of AI-based optimizations for the development of a broad and flexible framework for intelligently and scaleably building large data lake architectures, thus becoming the future-ready solution for any big data ecosystem within healthcare, financial, or IoT-based smart systems.