Hello everyone, and thank you for joining me. My name is Loh Kumar Ma and I'm a senior Azure data engineer with over 15 years of experience in designing advanced data solutions for large enterprises. Today, I will walk you through a transformative architecture I design that combines. Data factory with the predictor, modeling and real time analytics to manage risk more intelligently, especially during times of financial and market and ity. This framework not only process, reduce this processing time and cost, but also improve pipeline reliability and give leadership teams the ability to act quickly on risk signals with data. Let's begin with the problem. So today's data challenge, let's begin. Modern organizations are joining in data spread, so we are talking about the petabytes of data structure, unstructured, scattered across cloud, enter around, and legacy systems, and third party APAs and internal database. This creates. Challenges, and there are three major challenges explosive data growth. So constant increase in data from web, iot, traditional transactional systems and third parties and integration complexity. So data comes from the many formats. At a different speeds. And with the varying schema and business rules. So maintenance burdens. So traditional ETL frameworks are code heavy, error prone, and time consuming to maintain. So when risk is dynamic and data is in timely, and your business decisions are all already outdated, So the solution is Azure Data Factory. It's a game changer. So we needed something that could scale with the data or respond to change quickly and reduce manual effort. So that's where Data Factory came in. So my solution leverage A DF is low-code pipeline design with visual interfaces and pre-built connectors to a hundred plus data sources and reusable components for consistency across projects. Template driven workflows to register them to market. So we developed limited data driven architecture where business logic could be modified without changing a single line of code. This meant faster iterations, better testing, and Jira deployment overheads. Let's move on to the next. And measurable business impacts. Let's talk about the numbers because measurable outcomes. By creating Azure Data Factory pipelines and we reduced operational cross dropped 40% thanks to dynamic scaling and optimized scheduling pipeline success rates improved in 99.9%, virtually eliminating manually restarts and broken chains. And these aren't just stats. They really reflect agility, resilience, and especially when making high stake. So let's talk about the architecture best practices. That's, we followed the backbone of this success was carefully architecture framework, built on four best practices, so model design. So breaking pipeline into functional units like ingestions and clinging transformations and loading pipeline template. Templates using parameterize and templates to quickly roll out new use cases. Metadata driven executions to control everything. Source file types, schema mapping from a tables incremental loading, so load only delta, and registering both. Compute this usage and pipeline runtime. So this allowed us to scale seamlessly, reuse proven logic, and adapt rapidly to new data sources or business roles. And the governance and line monitoring that we followed a robust data system is incompatible without governance. Here is what we implemented track. Thing. Every transformations from how to refine. Along with the timestamped logs, audit frameworks ev Every activity was logged researchable, searchable and tied to user credentials and operational dashboards built using Power BI and Log Analytics to monitor pipeline health alerting systems, alerts for performance. Threshold to notifications. This not only supported complaints but us proactively detect before. Once the pap was stable and we focused on. So using Power BI and Azure Analysis Services, we enable self-service analysis for business teams, reduce report generation time by up to 70% and connected directly to streaming sources for real time dashboards for financial services. This meant instant visibility into key metrics like exposure, risk, operational losses, and market trends. Let's compliance and security, so financial end compliance, encryption and transit using Azure. So we were stored all the keys into the Azure Q keyword. So role robust access controls to enforce least privileges. So audit trail generations and version controlled and configuration for every change. Thatm masking right to be forgotten and constant tracking for privacy laws like GDPR. So this control ensure we passed multiple compliance audits with. So the solution I built is not just a pipeline, it's a strategic platform. We have already started enterprise rollouts across departments. We're also training internal teams to manage the platform, build new use cases, and improve data maturity levels across the board. So looking ahead, I see this evolving into see self-learning, AI-driven risk intelligence engine with auto-tuned pipelines ready to failure alerts and embedded models that forecast financial risks based on real data patterns. This is the future of data-driven risk management, and we are ready on the path. We are already on the path. So to wrap up I invite you assess your current data architecture. Identify areas where intelligent automation could reduce cost and complexity, and consider how scalable frameworks like the one I have presented, can help your organization make faster, safer, and smarter relations. Thank you for attending. I'm excited to share this journey with the Conference 42 Community and I, and with thank.