LakeCompass: An End-to-End System for Table Maintenance, Search and Analysis in Data Lakes

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Abstract

Searching tables from poorly maintained data lakes has long been recognized as a formidable challenge in the realm of data management. There are three pivotal tasks: keyword-based, joinable and unionable table search, which form the backbone of tasks that aim to make sense of diverse datasets, such as machine learning. In this demo, we propose LakeCompass, an end-to-end prototype system that maintains abundant tabular data, supports all above search tasks with high efficacy, and well serves downstream ML modeling. To be specific, LakeCompass manages numerous real tables over which diverse types of indexes are built to support efficient search based on different user requirements. Particularly, LakeCompass could automatically integrate these discovered tables to improve the downstream model performance in an iterative approach. Finally, we provide both Python APIs and Web interface to facilitate flexible user interaction.