  The rise of big data has revolutionized data exploitation practices and led
to the emergence of new concepts. Among them, data lakes have emerged as large
heterogeneous data repositories that can be analyzed by various methods. An
efficient data lake requires a metadata system that addresses the many problems
arising when dealing with big data. In consequence, the study of data lake
metadata models is currently an active research topic and many proposals have
been made in this regard. However, existing metadata models are either tailored
for a specific use case or insufficiently generic to manage different types of
data lakes, including our previous model MEDAL. In this paper, we generalize
MEDAL's concepts in a new metadata model called goldMEDAL. Moreover, we compare
goldMEDAL with the most recent state-of-the-art metadata models aiming at
genericity and show that we can reproduce these metadata models with
goldMEDAL's concepts. As a proof of concept, we also illustrate that goldMEDAL
allows the design of various data lakes by presenting three different use
cases.
