Data Lakes: A Survey of Functions and Systems (Extended abstract)

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.

I. Introduction

In the past decade, various solutions and systems have been proposed to address the research challenges of data lakes. However, while ‘data lake’ is a current buzzword with a lot of hype surrounding it, there is a lot of ambiguity about its exact definition and functions. Moreover, most recent data lake proposals only target a specific research problem or certain types of source data. A coherent, complete picture of data lake problems and solutions is still missing.