SSIS DATA MIGRATION AND CLOUD PERFORMANCE EVALUATION BY OPTIMIZING QUERY USING THE BULK TRANSACTION LOCK TUNING ALGORITHM

Abstract

Data migration plays a critical role in modern data warehousing, ensuring seamless transfer of large datasets between systems. However, the performance of such migrations is often hindered by sub-optimal query execution and inefficient locking mechanisms, especially when dealing with bulk transactions. This project focuses on enhancing the efficiency of data migration using SQL Server Integration Services (SSIS) by implementing advanced query optimization techniques and introducing the Bulk Transaction Lock Tuning Algorithm. The proposed algorithm minimizes lock contention during high-volume data transfers, enabling concurrent operations and reducing execution time. By integrating query optimization practices, such as indexing strategies and execution plan analysis, the solution further enhances data pipeline throughput. Comprehensive performance evaluations demonstrate significant improvements in migration speed, resource utilization, and system scalability. A detailed performance evaluation of the proposed approach is conducted through a series of benchmarks on large-scale datasets. Results demonstrate significant reductions in migration times, improved resource utilization, and increased system throughput. The integration of these techniques within SSIS pipelines provides a robust framework for enterprises to handle complex data migration scenarios with minimal downtime and optimal performance. This research provides a robust framework for organizations to optimize SSIS-based data migration workflows, ensuring reliable, high-performance data transfer for large-scale enterprise applications.