Good day everyone. My name is a Sal from End Living Technologies. Today I will be discussing how we can optimize Oracle and skill server databases in cloud, focusing on practical frameworks for achieving high performance on AWS and Azure Clouds. First, the critical role of database performance As robotics automation and data driven system continues to expand database performance become mission critical. Every milliseconds of the latency can influence real time decision. I will cover why optimization of database on Modern Cloud platform is not just a beneficial, but it's essential foundational optimization techniques. Before we drive into cloud native tools, it is important to understand core database optimization principles like a scale plan management, a WR analysis, query, store insights. These help maintain stability, diagnose bottlenecks, and track very performance over time. Next, cloud Native Performance Tools, both AWS and Azure provide powerful tools. Like a WR. Performance Insights helps you to visualize and diagnose database load in real time. While Azures intelligent tuning uses machine learning to automatically adjust and improve the performance. Next query optimization query execution. Identifying and resolving problematic queries is central to optimization. Using tools like a WR and Performance Insights, you can pinpoint slow queries. Review the execution plan and apply targeted improvements like indexing or query rewriting. Next now we'll see into resource utilization and workload throughputs. Efficient resource management ensures maximum throughput memory. Ivo and CPU must. Appropriately while connection pooling helps reduce the latency in high concurrency environments. Exhibition plan stability ensures predictable performance. Using SQL Plan Management in Oracle and a query stored plan, forcing in a scale server, you can capture and enforce the optimal plans to prevent the regressions. Partitioning strategics for sales partitioning improves performance and simplified management with a range hash or list partitioning. It helps to target specific data sets efficiently and reduce the maintenance windows, workload, governance, and resource manage. Governance ensures critical workloads get the resources and they need Oracle Resource Manager and Escal Server Resource Governance allows fine tuned control over CPU and Memory and Sessions cross-platform benchmark. Comparing AWS and Azure performance through standardized benchmarks helps to determine optimal workload par placement testing with KPS, like query response time and TPS gives clarity on trade offs. Now we'll go to the strengths and of AWS and. AWS offers deep visibility and flexibility tools like Performance Insights and Aurora Serverless. Meanwhile, Azure provides machine learning driven through hyperscale database that supports massive workloads. Cost management in cloud databases. Cost to optimization is just as vital. A right sizing instance. Committing to reserve capacity, optimizing storage, and setting up monitoring alerts are key strategies for sustainable cloud spending. Now we'll go through the emerging trends. We are seeing automat automation evolve with machine learning driven through serverless database. Edge computing and greener infrastructure that reduce the carbon footprint. Now we'll see the roadmap, how the roadmap will be. Start by assessing your current performance improvements, quick wins, established governance, and plan strategic improvements. Remember, optimization is a continuous process. We cannot stop on a day. It should continue as data grows or infrastructure grows. Finally, the takeaways. Integrated traditional techniques with cloud tools optimized across all dimensions and prepare for the future with adapt to architectures. Thank you Finally. Thank you for your time. I hope this session give you the accountable insights into optimizing your database performance, cost effective, and scalability in the cloud. Thank you.