In the era of big data, Hadoop has emerged as a pivotal framework for managing vast data warehouses due to its scalability and cost-effectiveness. However, efficiently handling data updates and ensuring data quality remain significant challenges. This research explores incremental load and deduplication techniques within Hadoop-based data warehouses to enhance data processing efficiency and integrity. Incremental load strategies aim to minimize the volume of data processed by only updating modified or new records, thereby reducing processing time and resource consumption. Deduplication techniques focus on identifying and eliminating redundant data, which is crucial for maintaining data quality and optimizing storage utilization. This study conducts a comprehensive analysis of existing incremental load and deduplication methods, evaluates their performance in Hadoop environments, and proposes optimized approaches tailored for large-scale data warehouses. Experimental results demonstrate that the proposed techniques significantly reduce data processing time and storage requirements while maintaining high data accuracy. The findings provide actionable insights for organizations leveraging Hadoop for data warehousing, enabling them to achieve more efficient data management and improved system performance. This research contributes to the ongoing efforts to enhance big data technologies by addressing critical aspects of data loading and quality assurance in Hadoop ecosystems.

Hadoop, Data Warehouses, Incremental Load, Deduplication, Big Data