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Pravin, who leads engineering for Amazon Aurora, shared an anecdote about his son and friends using AI-assisted coding tools to develop an app without needing to worry about database setup. Elizabeth from AWS Databases noted that teams can now deliver projects in days instead of months, with a broader demographic of builders, including analysts and designers. Engineers in Pravin's organization are creating agents that significantly reduce on-call work, and product managers are drafting documents more efficiently. Aurora aims to address the challenges posed by rapid development changes by adhering to three core principles: meeting developers where they work, absorbing workload variability, and growing with applications. Aurora PostgreSQL is integrated into AI coding tools, allowing developers to set up databases quickly. It features a serverless model that automatically scales to meet fluctuating demands, accommodating workloads from small projects to large-scale applications. The database supports existing tools and frameworks, ensuring compatibility and easing migration challenges. Examples of successful transitions to Aurora PostgreSQL include SurveySparrow, which achieved cost savings and improved query latency, and Netflix, which reported significant performance improvements. Aurora's flexibility allows developers to use both serverless and provisioned instances within the same cluster, optimizing operations without data migration. It also provides options for tuning performance and maintaining an up-to-date database with minimal disruption. Aurora Global Database enables applications to expand across regions without overhauling the data layer, supporting cross-region disaster recovery and low-latency reads. Companies like S&P Dow Jones Indices and DraftKings have successfully leveraged Aurora to support their growth and operational needs. Aurora PostgreSQL is designed to empower developers, facilitating innovation across various project scales.