Real-Time Data Infrastructure for Enhanced Learning Path Optimization

As a rapidly growing company in the professional development industry, we are committed to enhancing our platform's ability to deliver personalized learning experiences. To achieve this, we need a real-time data infrastructure that can ingest, process, and analyze data from multiple sources effectively. The project involves setting up a scalable architecture using Apache Kafka for event streaming, Spark for real-time data processing, and Airflow for orchestrating data workflows. Additionally, dbt will be employed for data transformation, while Snowflake or BigQuery will serve as the cloud data warehouse. The infrastructure should also integrate with Databricks to implement MLOps practices, ensuring that machine learning models can be deployed and monitored efficiently. By implementing a data mesh approach, we aim to decentralize data ownership and improve data quality across teams. This project is crucial for providing personalized recommendations to our users, thereby enhancing their learning paths and overall experience.