Abstract
This paper contrasts three main data storage technologies-data lakes, data warehouses, and NoSQL databases-in the framework of contemporary analytics systems. For data accessibility, performance, scalability, cost-effectiveness, and integration capability every storage system presents special benefits and drawbacks. As data volumes and analytics needs continue to rise, making the right choice of storage technology becomes progressively crucial in striking the right balance among the critical verticals: cost, performance, and flexibility. This paper provides knowledge about most suitable use cases and environments for every technology by means of a comprehensive study of the traits, advantages, and limitations of each storage solution. The article also addresses new trends and future prospects including artificial intelligence-driven data optimization methods, real-time analytics support, and hybrid storage systems. These results seek to direct IT experts and data architects in creating scalable, robust, and effective data infrastructure fit for advanced analytics.
Published Version
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