Optimizing Real-Time Analytics for Payment Processing with Data Mesh Architecture

As a rapidly growing entity in the payment processing industry, we face challenges in managing and analyzing our vast data flow efficiently. We require a sophisticated data engineering solution that can handle real-time data processing and analytics to empower our operational decisions. The project involves designing a data mesh architecture, integrating key technologies such as Apache Kafka for event streaming, Spark for real-time data processing, and Airflow for workflow management. Additionally, the use of dbt, Snowflake, or BigQuery will be crucial for data warehousing and analytics. Our goal is to provide our stakeholders with instant access to detailed transaction analytics, enabling proactive business decisions and enhancing customer satisfaction. We anticipate that the implementation will significantly reduce time delays associated with data insights, creating a competitive edge in the market. The successful completion of this project will aid in minimizing transaction bottlenecks, improving fraud detection, and optimizing overall payment processing operations.