The paper analyses core data-management strategies that ensure a consistent, scalable, and cost-efficient transition from on-premises or monolithic relational databases to Amazon Aurora Serverless. Drawing on recent peer-reviewed research and industry reports, the study first frames serverless Aurora within a microservice-centric architecture, emphasising the “database-per-service” pattern, CAP-theorem trade-offs, and the complementary roles of transactional stores, data lakes, and data warehouses. The second section evaluates mechanisms for maintaining data integrity during and after migration, contrasting ACID guarantees in Aurora with BASE-oriented eventual consistency at the system boundary, and detailing patterns such as sagas and event sourcing for cross-service coordination. The third part (retained in full) offers a practice-oriented synthesis of automation techniques: AWS Database Migration Service for zero-downtime change-data-capture, AWS Schema Conversion Tool for heterogeneous schema conversion, and Infrastructure-as-Code pipelines for repeatable cluster provisioning and continuous delivery. Empirical evidence from large-scale migrations—including multi-billion-row financial and media platforms—is used to quantify benefits (e.g., up to 40 % cost reduction and sub-minute fail-over times) and to highlight common pitfalls. The paper concludes with a set of actionable guidelines that align architectural decisions, consistency requirements, and automation practices, demonstrating that a properly orchestrated move to Aurora Serverless not only preserves, but often enhances enterprise data reliability and agility.

Serverless databases, Amazon Aurora, data migration, data consistency, ACID vs BASE, CAP theorem, microservices architecture, AWS DMS, schema conversion

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