We present a learning analytics study on a Master-level practical database course designed to equip students with hands-on experience in the assessment of four database systems for specific use cases. The course is based on a React web application to monitor task performance, including success rates, executability, processing time, and perceived difficulty. Learning analytics conducted across two semesters reveals trends in student success and challenges, such as superior performance in Schema Evolution tasks with PostgreSQL and increased difficulty in Network Analysis tasks across all databases. While Cassandra’s lack of join capabilities introduces additional learning complexities, Neo4J demonstrates constantly higher executability and ease of syntax.