In this case study, we share our experience developing a real-time analytics dashboard for a major financial services provider. The client needed a solution that could process millions of transactions daily and provide instant insights to their analysts. The Challenge: The client's existing system had significant latency issues, with data updates taking up to 30 minutes to appear on dashboards. This delay was impacting decision-making and preventing timely responses to potential fraud cases. Our Approach: We implemented a microservices architecture using Node.js for the backend API services and React for the frontend dashboard. To handle real-time data processing, we utilized Apache Kafka for message queuing and Redis for caching frequently accessed data. The solution included customizable dashboards with drag-and-drop widgets, allowing users to configure their views based on their specific needs. We also implemented advanced filtering capabilities and configurable alerts. Results: The new system reduced data update latency from 30 minutes to under 2 seconds, allowing analysts to identify and respond to issues in real-time. The intuitive interface decreased training time for new users by 60%, and the overall efficiency of the fraud detection team improved by 35%.