Auto-Tag: Tagging-Data-By-Example in Data Lakes

TL;DR

Auto-Tag is a lightweight, interactive approach that automatically tags custom data types in enterprise data lakes using minimal user input and an offline index, enhancing data governance and search capabilities.

Contribution

It introduces Auto-Tag, a novel corpus-driven method that infers data patterns from a single example to accurately and efficiently tag custom data types in large-scale data lakes.

Findings

Auto-Tag achieves high accuracy in data-type tagging.

The approach is efficient and suitable for large enterprise data lakes.

Auto-Tag integrates with Azure Purview for practical deployment.

Abstract

As data lakes become increasingly popular in large enterprises today, there is a growing need to tag or classify data assets (e.g., files and databases) in data lakes with additional metadata (e.g., semantic column-types), as the inferred metadata can enable a range of downstream applications like data governance (e.g., GDPR compliance), and dataset search. Given the sheer size of today's enterprise data lakes with petabytes of data and millions of data assets, it is imperative that data assets can be ``auto-tagged'', using lightweight inference algorithms and minimal user input. In this work, we develop Auto-Tag, a corpus-driven approach that automates data-tagging of \textit{custom} data types in enterprise data lakes. Using Auto-Tag, users only need to provide \textit{one} example column to demonstrate the desired data-type to tag. Leveraging an index structure built offline using a lightweight scan of the data lake, which is analogous to pre-training in machine learning, Auto-Tag can infer suitable data patterns to best ``describe'' the underlying ``domain'' of the given column at an interactive speed, which can then be used to tag additional data of the same ``type'' in data lakes. The Auto-Tag approach can adapt to custom data-types, and is shown to be both accurate and efficient. Part of Auto-Tag ships as a ``custom-classification'' feature in a cloud-based data governance and catalog solution \textit{Azure Purview}.