Real-Time Data Pipeline for Optimized Ride Matching

Our startup is on a mission to revolutionize the ride sharing experience by leveraging advanced data engineering techniques. We aim to build a real-time data pipeline that integrates seamlessly with our existing infrastructure to enhance our ride matching capabilities. The project requires setting up a data mesh architecture that efficiently handles event streams from riders and drivers. Utilizing technologies such as Apache Kafka for event streaming, and Spark for real-time analytics, the pipeline will process location data, traffic conditions, and user preferences to optimize ride matches. Additionally, MLOps practices will be employed to ensure continuous improvement of our algorithms. The successful completion of this project will lead to reduced wait times, increased ride acceptance rates, and improved customer satisfaction. We need an expert to design, implement, and maintain this system within a 4-6 week timeframe, with a budget that reflects the critical nature of this initiative.