Optimizing Data Migration Strategies: Leveraging Big Data and AI for Seamless Enterprise Transitions
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I1P110Keywords:
Data Migration, Artificial Intelligence, Big Data, Cloud Transformation, Redshift, Snowflake, Retail Analytics, Healthcare Data ManagementAbstract
Organizations across various industries are swiftly adopting digital transformation initiatives to enhance responsiveness, boost operational efficiency, and facilitate data-driven decision-making. A key component of this transition involves transferring extensive amounts of data from outdated systems to contemporary cloud-based platforms while maintaining performance, data integrity, and security. This paper introduces a novel method for data migration that harnesses the synergy of Big Data analytics and Artificial Intelligence (AI) to enable seamless, intelligent, and secure transitions for businesses. The research concentrates on large-scale migrations from Teradata databases to Snowflake and Amazon Redshift, particularly within the retail and healthcare sectors, recognized for their substantial data volumes, sensitivity, and compliance challenges. By employing CRAWLER360 for detailed workload evaluations of over 14,000 database objects, the proposed approach accomplished 99% automation in converting SQL and stored procedures, while cutting migration timelines by 95% compared to traditional manual methods. Furthermore, AI-enhanced workload optimization and adaptive tuning strategies improved query performance by 35% and increased processing efficiency by 25%, while AI-driven anomaly detection bolstered governance and security measures for data. The introduction of an NLP-enabled query assistant improved data interaction, making advanced analytics more accessible to users without technical expertise. Overall, these results illustrate how the integration of AI and Big Data can create a migration process that is seamless, scalable, and self-optimizing, offering organizations a framework for intelligent and autonomous data modernization
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