My name is. Today we're gonna discuss about realtime data analytics in the cloud, enabling enterprise making. We're gonna discuss about how, modern cloud architecture and scalable infrastructure can revolutionize your organization's ability to harness data and enhance operational efficiency and deliver superior customer experiences. So the global cloud analytics market is experiencing a rapid growth valued at 22 point 23.2 billion in 2020, and projected to, expand at, CAGR of 24.3%. Increasing adoption of cloud-based business intelligence tools across various industries, particularly in banking, finance, and insurance sector. So the core components of realtime analytics are data ingestion layer, data crossing, layer, storage layer. So we're going to talk in detail about how these layers, will perform their operations. So in data ingestion, layer sing, seamlessly, integrates real-time data, streams from iot devices, applications, and third party source, as while maintaining data quality and consistency. So in the processing layer, we leverage, a distributed comp computing and in memory processing to execute complex analytics ML models and business logic with millisecond latency in the storage layer, it implements hybrid storage architecture, combining in memory casing and persistent storage to optimize both. Speed and durability. Modern real-time analytics systems are built on three, interconnected pillars of functions in perfect synchronization. The data ingestion layer acts as a robust front door, handling massive throughput of up to millions of events per second while ensuring data integrity and for format. Consistency this continuous stream flow into processing layer while sophisticated distributed algorithms and machine learning models transform raw data into business insights within milliseconds, the storage layer completes the, this architecture by implementing a hybrid approach combining, blazing fast in memory processing. persistence towards to maintain both performance, and data durability. This carefully orchestrated system empowers organizations to make precise, data driven at speed of their business operations. So it's performance. So Max Throughput is about 800 K. Stable throughput is about 300 K and utilization threshold around eight per 80%. The data ingestion layers serves as a critical foundation of realtime analytics success. Advance streaming platforms like Apache Kafka deliver ex exceptional throughput capabilities. Which perform with performance carefully balance between message volume and size to ensure consistent service quality organization should implement proactive monitoring and maintain utilization below ET threshold, which allows, headroom for unexpected traffic spikes while, System pon, responsiveness processing, layer optimization, horizontal scaling. we have three components here, horizontal scaling, resource allocation, consistent performance, horizontal, scaling, es, cloud native, elasticity. To automatically scale, processing capability kept up to 300% higher workloads while maintaining consistent performance. This ensures response times stay within 10% of baseline event during peak demand periods. efficiency by. Targeting 60 to 70% CP utilization in normal operations. This creates a safe buffer zone as performance degrades rapidly when utilizing exceeds 85%, leading to, missile processing deadlines and, potential system bottlenecks, consistent performance. So implement robust scaling mechanisms. Guarantee, stable response times within one 50 milliseconds, window. During dynamic scaling events, this architecture ensures, performance with 95% of, requests completing within 2200 milliseconds, even at a maximum system load. Storage layer, considerations. So while, we discuss about the storage layer consideration, we mainly consider three points. Yeah. Throughput performance, tied data, retention and scaling and late Z profile. So in right throughput performance. distributor storage systems maintain right throughput of up to, 100,000 events per second, per node when properly configured with the performance, heavily dependent on data, partitioning strategies. tide data retention systems, implementing tide storage, strategies, demonstrate 30% improvement in query response time, compared to our single tire implementation, particularly for data less than 24 scaling latency profile. so storage systems, maintains consistent latency, profiles up to 85% capacity threshold beyond this point, right? Latencies increase by approximately 20% for every 5% increase in. 85, 80 5% to monitor the system to 80%, 85% of the capacity threshold, event driven architecture. So there are four, key points we need to discuss here. One is handles, five 50,000 plus, concurrent events per second with zero message losses. So low latency sub, hundred milliseconds response. Time for real, time making improved efficiency, 35 per percent. Faster processing with 40% lower, resource utilization. So optimal, batch size. So 25 event, micro batches. Maximize throughput, latency, balance, event driven, architecture serve as a, cornerstone for modern realtime analytics enabling, data processing and immediate business insights. empirical testing demonstrate, 35% improvement in processing speed while reducing. Infrastructure costs by 40% compared to traditional batch processing system. By implementing a precise micro batch sizing and intelligent event, routing organizations can achieve the optimal balance between system response and, resource efficiency, security, and compliance. Encryption overhead. enterprise grade AEs encryption provides robust security with a minimal impact, adding only 2.3%, to crossing time while ensuring complete data protection across distribution system. So with, with less, process processing time. We can incorporate, encryption. So second authorization, authentication performance. So on high performance authentication systems, deliver exceptional reliability, with 99.5% successful verification rates while maintaining rapid sub hundred millisecond response time for seamless user experience. When it comes to compliance monitoring, advanced distributed monitoring capabilities handle thousand events per second in real time, maintaining 95 per 95% accuracy, and, compliance verification across all regulated frameworks and internal policies. So realtime security, enforcement processes, thousand or 10,000, compliance, rules simultaneously identifying, and flagging violations within 2.5 seconds to enable immediate, corrective action and maintain continuous compliance. Performance considerations. So latency management, network latency, in distribution systems includes trans transmission delay of 0.0 4.42, 3.3 millisecond. Prop delays between, 0.5 milliseconds per hundred, km Processing delays is between 0.522 milliseconds per no, querying delays 5 2 50 milliseconds, about 80% utilization. Processing or optimization. Adaptive resource management systems maintains the, stable performance when processing load varies between 42 80, 80 5% of maximum capacity. Optimal resource utilization is achieved when processing tasks are distributor distributed across. For both, computational and bandwidth resources, scalable patterns, system, implementing dynamic resource, allocation can achieve 25% better resource utilization. Then static allocation approaches, maintaining resource utilization between 60 to 80%. provides an optimal balance between performance and scalability, monitoring and, observation, observa variability. One is, when it comes to system health monitoring, machine learning based monitoring systems can achieve, prediction accuracy rates of 87% of system. Reducing files, positive rates to 0.3%. This approach has demonstrated a 40% reduction in system downtime compared to traditional threshold based monitoring, business metric monitoring organizations implement, implementing a comprehensive real time monitoring issue at 25% improvement. Making, speed and reduce response time to, market changes by an average of 15 minutes, compared to traditional batch processing, approaches. observe, observability, implementation system, implementing. Full Stack Observatory can achieve 94% visibility into system behavior patterns. Distributed, tracing with a 5% scalable rate, provides, statistically, significant insights while maintaining system or below 1.5%. Conclusion. so critical enabler, cloud-based, real time analytics can revolutionize enterprise decision making, transform, transforming raw data streams into actionable insights within seconds, which enabling organizations to respond strictly to market changes and customer needs. Balanced approach. success in realtime analytics demands a holistic strategy that, integrates high performance data ingestion, efficient processing, architecture, robust security protocols and continuous compliance monitoring, all working in harm, harmony to deliver reliable, actionable, and, intelligence. Future outlook. as cloud infrastructure continues to advance, realtime analytics will become more sophisticated and, democratize empowering organizations of all sizes too. harness, predictive insights, automate processing, and drive. Their operations. Thank you for watching.