Hey, this is ala from Technologies, and I'm thrilled to be here at Conference 42. Today I'm going to present how we can achieve mastering of multi-cloud database resilience. Today we are diving into problem that keeps a lot of us awake at night. Keeping our database resilient in multi-cloud world, if your organization uses more than one cloud provider, AWS Azure, Oracle Cloud, you know exactly how complex things have become. And if you are not doing multi-cloud yet, trust me, you will be database administrator, face a completely different world than they did few years ago. We are no longer limited to single cloud strategy. Most of our organization now run hybrid or multi-cloud environments. Let's talk about how to make that complexity manageable with resilience, automation, and observability. In this session, I'm going to walk through four things. First, we'll compare the major platforms. Like autonomous database, Azure scale database, and Amazon RDS for a scale server to see how they stack up in terms of capabilities, scalability, and cost. Then we will look at strategies for resilience, backup, failover, compliance. After that, I will talk about automation, how tools like Terraform. Cloud providers, CLI and scripting can make life much easier. Finally, we'll explore monitoring and observability so we can stay ahead of issues rather than reacting to them in this journey. We'll first go to Oracle Autonomous Database. Let's start with Oracle Autonom Database. This platform really shines with itself, managing ties like automated patching, upgrades, tuning building, ma machine learning for performance optimization, and automatic scaling for workload changes. Yes, you can run in it a serverless mode. Also for variable workloads, or if you want to decide. Infrastructure for pre predict, pre predictable strategy, state workload and paper use pricing you can be very costly, cost effective. Next, we'll go to Azure S SCALE database. Azure database takes slightly different approach focusing on intelligent performance. It uses AI to provide recommendations and automatic tuning, and the hyperscale architecture allows database to grow up to a hundred terabytes. Security is very strong with features like transparent data encryption and Azure rating integration. This also has flexible pricing models, so you can choose DTU based, eco based, or serverless, depends upon your workload needs. Next we'll go to Amazon RDS for S Scale server, Amazon RDS. For S Scale server is more about enterprise grade reliability. It offers automated backups, pointing time recovery, and multi easy deployments for high availability and read replicas for better performance. It supports enterprise features like always on. Availability groups. S-S-I-S-S-S-R-S, and let's use save cost through reserve instances or stay flexible with on demand pricing. So we've gone through all the capabilities of different cloud platforms. Now let's compare, when we compare all these three platforms, you can see. They each have their own strength and capabilities. Rack is excellent for self-management. Azure is great for intelligent performance, and RDS provides reliable enterprise grade service. So Ally, everything. The key is to pick the right platform for right workload rather than sticking to a single vendor out for habit. So in with this, we completed comparing three cloud offerings. Now we'll see the resilient strategies, how we make this cloud strategies workable for our real time and environments resilience strategies in that we'll first cover multi-cloud backup strategies. Platform choice is just one piece. In pail actually, to achieve real resilience, we need multi-cloud backup strategies and cross-cloud replication. And the backup should be taken when to avoid performance impact. And we have to also validate our backups frequently, whether we have, we should know. The backups actually works when we restore it. Next, we'll cover failover, and high availability for failover. We can design active passive steps setups with the automated health checks and DNS based routing, or even we can set up active architectures for zero downtime. Next we'll cover regulator compliance across clouds. Resiliency doesn't end up with uptime. We also need to be compliant. So compliant is very critical. We have to respect data residency rules, enforce encryption standards, centralized audit, logging, and consistent across the controls across the cloud. This consistency across clouds avoid regulatory headaches and keeps auditors happy. So by this we'll complete we complete resiliency. Next major topic is automation. Automation is one of the key features in all the cloud offerings. So how we achieve automation. This is using Terraforms Automation is very things vary depends upon our requirement. Currently we focus on the terraforms. For Terraform, we can define the infrastructure as a code, keeping everything version controlled and reproducible with Terraforms and with A-W-S-C-L-I. We A-W-S-C-L-I operations, we can automate backups, scaling, maintenance, all the regular activities, combining terraforms with the tools A-W-S-C-L-I or Azure C-L-I-O-C-S-C-L-I. We can script snapshot or scaling, patching everything, and we can also do the performance check. Automat automation is not just about speed. It's about reducing human error and avoiding re repeatability. Let's come to the final topic of our presentation. That is monitoring and observability, so no matter how well we plan things will break. The question is, will you know about it before your customer do? That's a question we have to ask for any, every monitoring. So monitoring and observability tied together, we need to track CPU, memory IO availability and response time, and also security. We have, we should have a threat detection system and alerts should be triggered sensibly and meaningfully. When there is an issue and it cannot be a weakness, so tools like AWS CloudWatch, Azure Monitor, Prometheus, graph, Datadog, or Splunk, we can build a unified view of our system. Even we can use artificial inter intelligence to predict issues before they occur. Finally. Performance tuning also can be automated. We can establish we can establish baselines and using machine learning learning and trigger, pre trigger, pre predefined responses for common issues and continuously optimize. So the database always performs best. By automating baseline collection, using machine learning based anomaly detection and automated remediation, we can fix issues before the impact users. The goal is to shorten MTTR means meantime to resolution, to point to the point where incidents are barely noticeable. Finally. The takeaways for this presentation is how we choose best cloud offerings, how we distribute our workloads in different cloud platforms. First thing is pick your database platform based on workload requirements, not vendor loyalty. Second, embrace automation like infrastructure as code scripting. Monitoring pipelines will save you time and reduce mistakes. Third. Build for resilience. Make sure you have cross cloud backups tested, failover and compliance build from the start, start, finally monitor everything. Observability is what keeps system healthy and users happy. The future database management is ably, multi-cloud and automated and resilient. The organization that embrace these principles will be once the best prepared for what's ahead. Thank you so much.