# Coining goldMEDAL: A New Contribution to Data Lake Generic Metadata Modeling

arXiv: https://arxiv.org/abs/2103.13155 · HTML: https://mlsanity.com/paper/2103.13155/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Semantic Data Management in Data Lakes](https://mlsanity.com/paper/2310.15373/) (2023)

## Abstract (arXiv)

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.