# On data lake architectures and metadata management

arXiv: https://arxiv.org/abs/2107.11152 · HTML: https://mlsanity.com/paper/2107.11152/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2021.

## Built on by (most cited first, 8 of 8)

- [LakeMLB: Data Lake Machine Learning Benchmark](https://mlsanity.com/paper/2602.10441/) (2026)
- [Model Lakes](https://mlsanity.com/paper/2403.02327/) (2024)
- [Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted](https://mlsanity.com/paper/2506.05520/) (2025)
- [Multimodal Data Storage and Retrieval for Embodied AI: A Survey](https://mlsanity.com/paper/2508.13901/) (2025)
- [Exploiting Formal Concept Analysis for Data Modeling in Data Lakes](https://mlsanity.com/paper/2408.13265/) (2024)
- [Towards Edge-Based Data Lake Architecture for Intelligent Transportation System](https://mlsanity.com/paper/2409.02808/) (2024)
- [An End to End Edge to Cloud Data and Analytics Strategy](https://mlsanity.com/paper/2509.12296/) (2025)
- [Secure, Scalable and Privacy Aware Data Strategy in Cloud](https://mlsanity.com/paper/2509.13627/) (2025)

## Abstract (arXiv)

Over the past two decades, we have witnessed an exponential increase of data production in the world. So-called big data generally come from transactional systems, and even more so from the Internet of Things and social media. They are mainly characterized by volume, velocity, variety and veracity issues. Big data-related issues strongly challenge traditional data management and analysis systems. The concept of data lake was introduced to address them. A data lake is a large, raw data repository that stores and manages all company data bearing any format. However, the data lake concept remains ambiguous or fuzzy for many researchers and practitioners, who often confuse it with the Hadoop technology. Thus, we provide in this paper a comprehensive state of the art of the different approaches to data lake design. We particularly focus on data lake architectures and metadata management, which are key issues in successful data lakes. We also discuss the pros and cons of data lakes and their design alternatives.