Real-Time Data Pipeline Optimization for Enhanced Learner Engagement Analytics

As a leader in the Online Learning & MOOCs industry, our enterprise is committed to delivering exceptional learning experiences that are both engaging and effective. However, we face challenges with existing data infrastructure that limit our ability to analyze learner engagement in real-time. We are looking for a comprehensive data engineering solution that will enhance our current systems by implementing a data mesh architecture. This project involves developing real-time data pipelines using Apache Kafka to seamlessly integrate with platforms like Spark, Airflow, dbt, and Snowflake. The goal is to enable real-time analytics and data observability, ensuring that we have the insights needed to tailor our content and improve learner outcomes. Furthermore, the project will involve setting up MLOps frameworks to facilitate the deployment and monitoring of machine learning models that predict learner behavior and preferences. By achieving this, we aim to provide personalized learning journeys, increase learner retention, and boost overall satisfaction.