I'm ach. I'm happy to be here in Con 40 to Machine Learning 2025. I'm a senior software engineer. I have spent several years in building a high performance microservices. I'm here to talk about how to build a high performance scalable data pipeline for financial domain in. Today's volatile financial market where millisecond can means million in lost opportunities. Traditional financial data processing platform is failing to meet the increasing large volume of data processing in short time. This architecture explains how to build an high performance scalable data pipeline for the financial institution to handle millions of transaction in short period of time with exceptional performance. The challenges of modern financial data processing be categorized into volume constraint, latency requirement, and data heterogeneity, volume constraint, legacy system struggle to handle thousands of transaction in a second during normal processing time, often it completely failing. During market volatility, even when volumes can increase by order of magnitude latency requirements. Modern trading platform operate in microsecond environment where even millisecond delays can result in significant monetary losses and missed opportunities. For question adjustment data heterogeneity financial institution must integrate data from. Multiple formats like Swift Jsl file and in different delivery channels like FTP, MQ channels, Kafka topic, and many more, which creates complexity that compounds performance challenges. Let me explain this architecture of this data pipeline. This, let's start with the canonical schema. Normalization is the very first beginning. Stage where data represent in standardized format to enable the seamless data integration with cross platform capabilities. Layer is. Collects and the process data extremely quickly while having backup mechanism that automatically switches operation. If the primary system fails, which have the retry capabilities in the enrichment framework, raw financial data will be enhanced with additional information like security details, account details, and fx, and any conversion, and the calculation will be done as part of this section. Storage layer. So once this enrichment is done, the data will be flattened. That will be stored into our Oracle database with unique indexing and the parting for each data sets in the analytical consumption layer. We are using the advanced realtime visualization to generate dashboards using the predictive analytics with faster performance queries. Let we deeply explain about the canonical schema normalization in the input format, multiple trading platform, delivering data in proprietary formats with inconsistent field structure, different time zones, daytime format. So this transformation engine is capable of dynamically mapping the field based on the predefined schema structure. Which eliminates this input, format, issues, and validation layer. The schema validation is enforced to prevent from the incompatible data type and other formatting issue and the configurable exception handling. Ensure a data integrity through the throughout the pipeline, standardized output, uniformly structured transaction with consistent identifiers and the normalized field enabling seamless downstream processing regardless of data origin. As part of the enrichment framework, the perfor, our enrichment framework in corporates, multiple reference data to enrich the raw transaction into complete data endpoints. This help to spend less time to analyze multiple platform for different data points. Strategic data storage. We have, we are using the Oracle database for the better performance. As part of the strategic data storage. We are first analyze while onboarding the data set. We will. Analyze the query patents and data volumes will be for, to analyze that load of the data and partnering will be done for all the required dataset with. Advanced time series optimization. Custom design indexes and optimized query plans are used to better results. Our database architecture have dramatically reduced complex query response time and production environments. This strategy was engineered to optimally distribute load across multiple node for max availability. This achieving? Yes, 78% reduction in average query latency for a mys critical financial operation. So mission critical financial operation, auto-scaling mechanism. I. Our auto schooling mechanism that maintain consistent performance during market volatility automatically adjusting resource van tra transaction volumes spikes during peak periods. Our predictive scaling algorithm analyze historical patterns and current market conditions to proactively provision resources before demand materialize, maintaining latency SLAs even during extreme volume events. The performance benchmark. Our performance benchmark showcases the architecture throughput capability capabilities with substantial capacity to handle market driven search. When record. These metrics were validated under simulated market volatility conditions matching historical flash crash scenario, ensuring robust during extreme events our architecture is able to. Process 300 k transaction per minute in the normal scenario. But in the, during the burst, it able to process four 50 K messages between the within eight to 10 millisecond response time. The availability is 99.99 percentage with this architecture. Our implementation case studies results as categorized into three sections like transaction processing capacity, system availability, cost efficiency. Our implementation achieved the four 50 percentage increase in transaction processing capacity, enabling the system to handle significant higher volume without performance and degradation. This eliminated previously common processing backlogs during market events. The system reliability, unplanned downtime was virtually eliminated with a reduction from approximately few hours to under three minutes. Component. Component failures now resolve quickly compared to the lengthy outages and legacy system. With operational recovery, time improves from hours to minutes. Cost efficiency, pre-transaction infrastructure costs were reduced by 68% through more efficient resource utilization and the elimination of T processing. Complete pipeline lifecycle through accusation to the conception. So when the publisher is publishing that data into the data bus are raw, consumer is able to. Lesson messages from the database. For each dataset, we have the separate MQ channels to listen the messages. The ingestion layer is able to quickly read the messages from the database and it publish into the, our internal processing layers. There we have the enrichment layer, which transforms the raw data into the complete data set by invoking the reference data and enrich all the data endpoints. And once this is. Enriched. We are using the Oracle database, which is Oracle database where that data is stored in flattened format and also it able to perform like thousands of records and a second without compromising any performance and. We have, and then we have the analytical layer. We are creating the dashboard using the predictive analytic with the past of performance query. So we will not see any lag in the dashboards and and key takeaways and next steps from this architecture. The. This presented architecture has demonstrated the ability to process hundreds of thousand financial transactions per second with sub millisecond latency. Providing that properly designed system can meet the demands of modern financial market implementation consideration. When it comes to the implementation consideration. Organization should begin with the. Comprehensive audit of current data flows. Identify the most critical performance bench bottleneck and implement the architecture using an incremental approach that delivers measurable improvement at each stage. Architectural balance success depends on balancing multiple competing priorities like latency versus throughput, complexity versus maintainability, and specialized optimization versus flex. Flexibility, a adoption to change requirements future direction. Our research continue to. Continues into integrating machine learning for predictive scaling and anomaly detection, exploring graph database technologies for relationship modeling and implementing zero downtime upgrade paths. Thank you for visiting this presentation. I hope this presentation helped to build a high performance, scalable get up pipeline for the financial domain. Thank you.