Real-Time Data Pipeline Implementation for Enhanced User Engagement in Online Learning

Our Online Learning platform is experiencing rapid growth, leading to an increased demand for personalized and engaging user experiences. To address this need, we are looking to implement a real-time data pipeline that allows us to process and analyze user interaction data instantaneously. The project involves designing a robust architecture using Apache Kafka for event streaming, Spark for real-time data processing, and Airflow for orchestrating data workflows. Additionally, the solution will utilize dbt for data transformation and integrate with Snowflake or BigQuery for storage and fast querying. The successful implementation will enhance our ability to deliver personalized content and insights to our users, thereby increasing engagement and retention. The project will also incorporate data observability and MLOps best practices to ensure data quality and streamline machine learning model deployment.