AI-Powered Data Migration Strategies for Cloud Environments: Techniques, Frameworks, and Real-World Applications
Keywords:
Cloud migration, AI-powered data migrationAbstract
Cloud-moving companies need secure data transfer due to exponential data collection and storage. Cloud adoption is hindered by lengthy, error-prone, manual data migration. This study examines AI's cloud data transmission benefits. Cloud migration challenges including data discovery, dependency mapping, transformation, and integration are addressed first. Next, we examine how AI, specifically ML algorithms, may automate and accelerate these important processes. AI data discovery technologies categorize data assets from application code and data schemas using NLP. Manual inventorying is removed, saving time and money. Data dependency mapping may be enhanced using AI to ensure transfer data integrity. Supervised learning algorithms can find data source-application links by analysing data access patterns. This improves migration by eliminating error-prone manual dependency mapping.
Data transformation by AI creates cloud-compatible data formats and structures. We can find data patterns and conflicts unsupervised. These results may teach AI models data purification, standardization, and transformation automation. This speeds migration and enhances cloud data consistency. The article covers cloud-native architecture and AI-powered data transfer. Serverless computing lets enterprises use pre-configured, scalable cloud resources to transfer data. Infrastructure provisioning and management elimination facilitates migration.
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