  Over the past decade, the data lake concept has emerged as an alternative to
data warehouses for storing and analyzing big data. A data lake allows storing
data without any predefined schema. Therefore, data querying and analysis
depend on a metadata system that must be efficient and comprehensive. However,
metadata management in data lakes remains a current issue and the criteria for
evaluating its effectiveness are more or less nonexistent.In this paper, we
introduce MEDAL, a generic, graph-based model for metadata management in data
lakes. We also propose evaluation criteria for data lake metadata systems
through a list of expected features. Eventually, we show that our approach is
more comprehensive than existing metadata systems.
