Optimizing Data Refresh Mechanisms for Large-Scale Data Warehouses

Abstract

This research paper explores advanced techniques and strategies for optimizing data refresh mechanisms in large-scale data warehouses. As organizations increasingly rely on data-driven decision-making, the need for efficient and timely data updates has become critical. This study examines various approaches to data refresh, including incremental updates, real-time processing, and change data capture techniques. It also investigates the impact of modern technologies such as in-memory computing, columnar storage, and distributed processing frameworks on refresh performance. The paper provides a comprehensive analysis of performance metrics, optimization strategies, and emerging trends in data warehouse refresh mechanisms, offering valuable insights for data warehouse architects and administrators.