Real-Time Data Pipeline for Enhanced User Matching in the Sharing Economy

As a budding startup in the sharing economy, we aim to revolutionize how users find and engage with service providers. Our goal is to build a real-time data infrastructure that enhances the speed and accuracy of our user-service matching algorithms. This project involves creating a data pipeline capable of processing and analyzing large volumes of streaming data to deliver insights swiftly. By utilizing technologies such as Apache Kafka for event streaming, Spark for real-time data processing, and dbt for data transformations, the project will redesign our existing data architecture into a more agile and responsive system. Our current setup has limitations in handling the dynamic nature of user interactions, which affects the matching accuracy and response times. With the integration of data observability tools and MLOps, we aim to not only improve our current performance but also establish a foundation for future scalability. This project is crucial for maintaining competitive advantage and delighting our users with exceptional service delivery.