Hello everyone. My name is Alman ti and I am a senior database administrator at Tech Axel. Over the years, I have had the opportunity to work with numerous financial clients, helping them streamline their database environments to achieve optimal performance. Today I am excited to. Present on a topic that is crucial to modern enterprise landscape leveraging AI powered predictive analytics to optimize enterprise database performance. As many of enterprise databases are the backbone of critical business operations. However, they often face performance bottlenecks that can significantly affect business continuity and user experience. These issues can lead to downtime, slow query responses, and ultimately lost opportunities. In this presentation, I will introduce an AI powered framework for predictive analytics that is designed to tackle these challenges by utilizing. Historical pattern analysis and reinforcement learning techniques. This framework can predict performance issues before they occur and provide real time optimization recommendations. One of the most powerful aspects of this framework is its ability to automate routine tasks such as query indexing and performance monitoring. This automation allows DBS to focus on more strategic decisions. While the AI system handles RIP two optimization work. Ultimately, the collaborative potential between human expertise and artificial intelligence is what sets this framework apart by combining the sense of both. We can maintain optimal database performance in the face of increasingly dynamic enterprise environments. Let's dive into how this AI powered solution can transform the way we approach database performance optimization, the challenge of enterprise database performance. Let's see about the complexity of the database. As enterprises continue to scale, the complexity of their database environments grows exponentially. Enterprise databases are no longer just a collection of tables and rows in a single database. Today's databases handle vast amounts of data that span across multiple systems and geographical locations supporting a wide array of. Business operations. This includes everything from transaction processing and data analytics to customer relationship management and enterprise resource planning. The architecture has become more distributed and multifaceted. In many cases, organizations rely on a combination of relational databases like MySQL and Oracle alongside NoSQL databases such as MongoDB or Cassandra. To handle specific use cases. Additionally, many enterprises are transitioning to cloud-based solutions. Integrating platforms like AWS Azure and Google Cloud for better scalability and efficiency. However, this diverse ecosystem introduces significant challenges in managing performance. The different types of databases, relational, NoSQL, and cloud databases have varying characteristics, including different query optimizers, indexing methods, and storage architectures. This heterogene genetically makes it difficult to apply uniform performance management practices across the entire environment. Managing these multiple layers of databases often. With thousands or even millions of transactions per second requires not just expertise, but a more sophisticated approach than traditional methods offer. These systems must support concurrent taxes by thousands of users, handle high velocity transactions, and ensure real time analytics. All of this must occur while maintaining high availability, security, and scalability. In addition to the technology complexity, the business demands are constantly shifting. Enterprises are increasingly adopting. Digital transformation strategies such as e-commerce platforms, IOT systems, and big data analytics, which generate new types of data at an unprecedented rate. This requires the database infrastructure to not only scale in size, but also. To adapt in real time to dynamic workloads, the need for seamless integration across these systems becomes even more important, ensuring that data flows efficiently across departments, regions, and even cloud providers. Therefore, managing these complex systems requires more than just knowing how to tune individual database systems. It demands a holistic, agile approach that can manage diverse technologies across multiple layers, keeping all systems running efficiently and ensuring that performance bottlenecks don't disrupt business operations. Limitations of traditional approaches. Now let's talk about how traditional database performance management. Approaches have struggled to keep pace with this increasing complexity. Historically, database administrators have relied on manual reactive methods to address performance issues. Traditional performance tuning was typically reactive. Problems were identified only after they had. Already impacted the system. This often meant dealing with performance degradation, slow queries, or outages only after the damage was already done. For example, when query performance begins to degrade, DBS might manually step through a diagnostic process, checking execution plans, running performance tests, and tweaking settings in an attempt to identify the root cause. However. This process can be time consuming and error prone, especially when working with complex systems that span multiple environments. What might have seemed like a minor issue can escalate, impacting everything from transaction processing to user experience. The consequences of this reactive approach are far reaching downtime becomes inevitable. Systems may go offline during troubleshooting, especially when significant changes are made to configurations in an attempt to resolve issues. Productivity laws is common, particularly when users experience slow database response times or application downtime. Affecting operations such as order processing, customer support, and inventory management. Customer dissatisfaction can result when critical systems such as e-commerce platforms like Amazon experience delays or outages during peak traffic periods. In summary. Traditional database performance management approaches are ill-equipped to handle the complexity and scale of modern enterprise environments. They can only respond to issues after they have already impacted the business, leaving organizations in a perpetual state of crisis management rather than proactive performance optimization, traditional database management phases. Several inherent limitations that contribute to performance issues and inefficiencies. One of the most significant challenges is related to configuration issues. 20 per 27% of service disruptions are cost by misconfigurations, and 16% of system downtime can be attributed to errors in configuration. These problems arise because the DBS rely on static. Predefined configurations that fail for adapt to dynamic business needs. Traditional database management places a substantial burden on DBS. On average, 25% of DBS time is spent on performance tuning, but what's concerning is that. About 67% of that time is spent on manual trial and error approaches. This trial and error process is time consuming, inefficient, and often leads to suboptimal outcomes, especially as database environments grow more complex. Another major limitation is static configuration. In many enterprise environments, 87% of database configuration parameters remain unchanged, even though workloads fluctuate due to business cycles, seasonability and other factors. This inflexibility means that databases are either over provision leading to unnecessary costs or under provision. Resulting in performance bottlenecks during peak periods. Traditional approaches simply do not account for the ever-changing nature of enterprise operations, leaving the system vulnerable to inefficiencies and disruptions. In summary, the traditional methods of database management are react to manual and rigid, which makes them ill suited for today's rapidly evolving and data intensive environments. Now let's talk about a framework for AI powered database optimization. At the core of this AI powered optimization system is a layered architecture that is designed to tackle the complexity of modern enterprise database environments. The framework is structured to include distinct layers, software, data collection, analysis, decision making, and implementation by creating abstraction layers. This architecture ensures seamless integration with diverse systems, including relational databases, no SQL systems and cloud platforms. This modular design allows the system to scale efficiently and adapt to evolving business deals needs, making it suitable for heterogeneous environments and complex workloads. Historical. Let's talk about historical pattern analysis. The framework builds comprehensive knowledge basis for historical performance data, such as query execution, statistics, resource utilization metrics, and system logs. This historical analysis allows the system to recognize recurring patterns providing. A solid foundation for identifying potential performance issues before they occur. By leveraging this data, the framework enables proactive optimization, tailored to the database needs. So reinforcement learning models at the heart of the system is adapt to intelligence driven by reinforcement learning this model continuously learn from past experiences and refine the predictions as new data comes in this self-improvement. Ensures that as the system encounters new patterns or shifts in workload, it becomes increasingly accurate in its ability to suggest effective optimizations over time. The realtime recommendations framework generates. Actionable optimization suggestions in real time, allowing database administrators to implement changes quickly and effectively. These recommendations span areas such as query indexing, memory distribution, and resource allocation, ensuring that performance is continually optimized without requiring manual trial and error adjustments. In conclusion, the AI powered framework provides a dynamic and flexible. Solution for enterprise database optimization using machine learning and reinforcement learning to continuously monitor, analyze, and optimize database performance in real time. So now let's say the performance improvements for AI power techniques. So this graph highlights the significant performance improvements that AI powered techniques specifically reinforcement learning can bring to the database optimization. One of the most notable benefits is query latency reduction, where reinforcement learning approaches can reduce latency by up to 70 to 80% compared to traditional query optimizers. This improvement happens quickly with training. Times as short as 10 to 30 minutes for moderately complex database schemas, making it highly efficient. Additionally, index tuning and complex query performance benefit from AI techniques. Traditional optimizers can typically handle join order optimization for queries involving 10 to 15 tables, but AI models can manage up to 50 tables. Far beyond the capabilities of traditional methods, AI also excels in resource utilization and anomaly detection, proactively identifying the issues that would otherwise go unnoticed until they impact performance. This highlights the powerful potential of AI to improve database performance across multiple dimensions leading to more efficient, scalable, and responsive systems. Let's say the synergy of human and AI collaboration. So we can highlight how AI and the human expertise complement each other in database optimization. So let's talk about AI trends. Ai, Excel set processing vast amounts of performance data, identifying subtle. Patterns and continuously monitoring hundreds of metrics. It automate tasks like index management and resource optimization, and detects anos up to 47 minutes before traditional alerts, ensuring proactive intervention. On the other side, humans bring strategic decision making with business context, handle complex value optimization and plan for long-term scalability. They ensure that optimizations align with business requirements and manage risk assessments for implementation together. AI handles. The heavy lifting of optimization, while human expertise ensures that all actions align with broader business goals, creating a powerful collaboration for sustained database performance, so creating effective human AI partnership, now we can emphasize the collaborative model between AI systems and database administrators, where both contribute the sense to optimize database performance. So the AI gen identifies patterns and optimization opportunities in performance data proactively suggesting improvements. On the other hand, DBS evaluated these AI generated recommendations considering the business context to ensure. The changes align with organizational goals. The impact of the recommended optimizations is measured against predictions to verify effectiveness. Once the changes are approved, they are deployed with the necessary safeguards to identify any negative impacts. Organizations that adopt this AI augmented approach experience a 34% increase in strategic projects completed by DBS alongside a 47% reduction in time spent on routine maintenance tasks. This partnership allows both AI and human expertise to evolve together continuously improving database management practices. So now let's take one of the case studies in e-commerce platform. This presents a real time case study of optimizing an e-commerce platform during peak shopping periods. Let's talk about the problem. The platform faced significant performance degradation during high traffic periods with query latencies increasing three to five times, and critical workflows like checkout, exceeding ten second response times. Severely affecting user experience and business operations. The AI framework was deployed alongside existing systems, analyzing three months of historical data to establish baseline performance and identify recurring bot limits during high traffic periods. So the analysis of the system found that just. 0.03% of distinct queries were responsible for over 70% of the database load during peak periods, providing clear targets for optimization, and the result after six months, the platforms saw a 40% reduction in resource over provisioning, 28% decrease in average query latency and successful. Successfully my mitigated 17 potential bottlenecks before they could impact customers resulting in improved performance and a better user experience during high demand periods. So the e-commerce platform performance improvement. So overall we have seen. The improvements in EE E-commerce platform performance after implementing the AI powered optimization framework, 40% of resource optimization, the AI framework reduce over provisioning of resources, ensuring efficient use of infrastructure, while maintaining optimal performance during demand spikes, such as during sales events or holiday seasons. And the 28%. Savings in query latency by optimizing the database, the system achieved at 28% decrease in average query response time during peak periods, significantly improving the user experience by reducing delays. 45% improvement in critical queries critical. Workflows like checkout and payment processing. So a 45% improvement in performance, ensuring smoother transactions and a better customer experience during high traffic times 60, 62% of DBA time saved. With Routine Maintenance Task, automated DBS saved 45 hours per week, allowing them to bring overall productivity and efficiency in the database management team. These improvements demonstrate how air driven optimization can lead to both technical gains and operational efficiencies for enterprises. The technical implementation details for this slide presents. The multilayered architecture of the AI powered database optimization framework, which is designed to manage database performance efficiency across enterprise environments. Now, let's talk about the implementation layer at the top of the stack, the implementation layer ensures, sorry. At the bottom of the stack is the implementation layer that ensures that approved optimization recommendations are. Deployed in a controlled manner with necessary safeguards in place for low risk optimizations. Changes are applied automatically while more complex changes are reviewed and implemented with human oversight, ensuring system stability. And next comes the optimization engine. The optimization engine generates targeted improvement recommendations based on insights gathered from the analysis. It suggests precise actions such as index tuning and resource allocation adjustments to address performance bottlenecks, and the analysis engine works by analyzing the data collected in the first layer. It identifies patterns in database performance and predicts potential issues. This analysis enables the system to proactively address problems before they affect performance data collection layer. The foundation of this, the top most layer is the data collection layer, and this layer, which gathers comprehensive telemetry from the database. This includes. Query execution, statistics, resource usage, and system logs, ensuring that the system has accurate up-to-date data to make informed decisions. This multi-layered approach allows the system to efficiency, efficiently optimized database performance, adapting to diverse environments, while maintaining seamless integration across components for consistent performance improvement. And the future directions of this AI powered database optimization framework is cross-platform optimization. The framework aims to expand beyond traditional single platform environments. The goal is to optimize across heterogeneous database systems. For example, relational NoSQL and cloud by developing unified. Performance models. The system will be able to identify optimization opportunities that span multiple technologies while allowing seamless integration and performance improvement across diverse platforms. The explainable AI component is to increase trust standard option. The framework will focus on explainable ai. It'll. Provide clear rationals for every optimization recommendation. Enabling database administrators to understand not just what optimizations are recommended, but also why they're expecting to expect to improve performance, ensuring transparency and better decision making. And the natural language interfaces, the framework will incorporate a natural language interface allowing DBS to interact with the system using conventional queries. This makes the system more user friendly, enabling administrators to express performance concerns, set optimization goals, and ask questions in familiar non-technical language. Eliminating the need for specialized syntax workload specific learning models. The future of the framework includes developing specialized learning models tailored to different types of workloads. This includes optimizing for transaction processing, analytical reporting, and even iot data management. By customizing optimizations for specific workload types, the framework will be able to deliver more targeted and effective performance improvements. These advancements will ensure that the framework continues to evolve, adapting to new technologies, and providing even more powerful optimization strategies for diverse enterprise needs. And in conclusion, as database. Environments continue to grow in complexity. The synergy between human expertise and AI driven optimization will be key to ensuring that enterprises can maintain peak performance, scalability, and elig agility, ultimately driving business success in an increasingly data driven world. I look forward to any questions you may have, and thank you for your time today.