Real-Time Data Pipeline Development for Personalized Language Learning

We are a growing language learning platform looking to deepen user engagement and learning outcomes through personalized experiences. To achieve this, we require a robust real-time data pipeline that captures, processes, and analyzes user interaction data. This project involves designing and implementing a data infrastructure using Apache Kafka for event streaming, Apache Spark for processing, and Snowflake or BigQuery for storage and analytics. The solution must be scalable to handle increasing user data and integrate seamlessly with our existing platform. By leveraging data mesh principles and MLOps practices, the pipeline should enable continuous learning improvements through feedback loops. Experience in data observability is essential to ensure data quality and reliability. The goal is to deliver insights that drive personalized content recommendations, improving user satisfaction and retention.