# Data Lakes: A Survey of Functions and Systems

arXiv: https://arxiv.org/abs/2106.09592 · HTML: https://mlsanity.com/paper/2106.09592/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2021.

## Built on by (most cited first, 15 of 15)

- [Jellyfish: A Large Language Model for Data Preprocessing](https://mlsanity.com/paper/2312.01678/) (2023)
- [Column Type Annotation using ChatGPT](https://mlsanity.com/paper/2306.00745/) (2023)
- [LakeBench: Benchmarks for Data Discovery over Data Lakes](https://mlsanity.com/paper/2307.04217/) (2023)
- [Model Selection with Model Zoo via Graph Learning](https://mlsanity.com/paper/2404.03988/) (2024)
- [Multimodal Data Storage and Retrieval for Embodied AI: A Survey](https://mlsanity.com/paper/2508.13901/) (2025)
- [Leveraging Foundation Language Models (FLMs) for Automated Cohort Extraction from Large EHR Databases](https://mlsanity.com/paper/2412.11472/) (2024)
- [TabEmb: Joint Semantic-Structure Embedding for Table Annotation](https://mlsanity.com/paper/2604.18939/) (2026)
- [Semantic Data Management in Data Lakes](https://mlsanity.com/paper/2310.15373/) (2023)
- [SourceSplice: Source Selection for Machine Learning Tasks](https://mlsanity.com/paper/2507.22186/) (2025)
- [Conceptual Schema Inference for Tabular Datasets using Large Language Models](https://mlsanity.com/paper/2509.04632/) (2025)
- [ZTab: Domain-based Zero-shot Annotation for Table Columns](https://mlsanity.com/paper/2603.11436/) (2026)
- [PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries](https://mlsanity.com/paper/2605.18199/) (2026)
- [LayoutBench: Performance Benchmarking of Cloud Storage Layouts for Multimedia Data](https://mlsanity.com/paper/2607.28880/) (2026)
- [H2: A Dual Hybrid Semantic Data Lake Architecture for Medical Data Harmonization with Human-In-the-Loop verified, LLM Driven Metadata Annotation System](https://mlsanity.com/paper/2608.08056/) (2026)
- [High-Capacity Robust Medical Image Exfiltration via Neural Network Weight Replacement](https://mlsanity.com/paper/2609.31726/) (2026)

## Abstract (arXiv)

Data lakes are becoming increasingly prevalent for big data management and data analytics. In contrast to traditional 'schema-on-write' approaches such as data warehouses, data lakes are repositories storing raw data in its original formats and providing a common access interface. Despite the strong interest raised from both academia and industry, there is a large body of ambiguity regarding the definition, functions and available technologies for data lakes. A complete, coherent picture of data lake challenges and solutions is still missing. This survey reviews the development, architectures, and systems of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing approaches and systems based on their provided functions for data lakes, which makes this survey a useful technical reference for designing, implementing and deploying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey will motivate the future development of data lake research and practice.