Increasing Data Discovery in Data Lake Platforms Through AI Integration

With the current big data, business organization accommodate digital platform and collected enormous structured and unstructured dossier.There is significant centralized repository established to store big data in raw without predetermined schema called data lakes.The Data lakes provide an effective solution to massive data, accommodating influx information, supporting data scalability and flexibility.Noteworthy Data Lakes faces underpinning challenges in metadata effective management silos data, and inadequate query processing techniques.Similarly lacking predetermined schema in Data lakes may cause significant concerns on data discovery retrieval, understanding and applications.In this paper, there is an underpinning exploration on how specific Artificial Intelligence (AI) integration into data lakes supports efficiency, applicability, and big data analytical capabilities.AI presents underpinning techniques such as machine learning, automated metadata tagging, and natural language processing are promising in Data lakes and integration to counteracts and manage current challenges in data discovery in Data Lakes through AI.Integrating AI specific techniques such as AI-based automation is integral in discovering in data in data lakes, supporting sematic search, and increased predictive data analysis for insightful vast datasets management.The main focus of this study is https://ijcserd.com46 ijcserd