Real-Time Transaction Data Pipeline Enhancement for Payment Processing Platform

As a growing payment processing startup, we are facing challenges in efficiently handling and analyzing real-time transaction data. Our current data pipeline struggles with scaling demands, leading to latency issues and decreased observability of transaction data. To address this, we are seeking to enhance our data engineering capabilities by implementing a more robust and scalable pipeline using Apache Kafka for event streaming and Spark for real-time analytics. The project involves revisiting our existing architecture, identifying bottlenecks, and implementing a data mesh strategy to decentralize data ownership and improve data quality. Additionally, we aim to integrate data observability tools to enforce data accuracy and reliability. This will not only improve our processing capabilities but also provide us with actionable insights, facilitating better decision-making and compliance with industry standards.