Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes-Reference-Cited by-同舟云学术

Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes

Published:2021-06-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 International Conference on Management of Data
language:
Short-container-title:

Author:

Song Jie¹,He Yeye²

Affiliation:

1. University of Michigan, Ann Arbor, MI, USA

2. Microsoft Research, Redmond, WA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3448016.3457250

Reference71 articles.

1. Amazon Deequ Library for Data Validation. https://github.com/awslabs/deequ. Amazon Deequ Library for Data Validation. https://github.com/awslabs/deequ.

2. Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes (Full version). https://arxiv.org/abs/2104.04659. Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes (Full version). https://arxiv.org/abs/2104.04659.

3. AWS Glue custom classifers. https://docs.aws.amazon.com/glue/latest/dg/custom-classifier.html. AWS Glue custom classifers. https://docs.aws.amazon.com/glue/latest/dg/custom-classifier.html.

4. Azure ML: Data Pipelines. https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines. Azure ML: Data Pipelines. https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines.

5. Azure Purview for data governance. https://azure.microsoft.com/en-us/services/purview/. Azure Purview for data governance. https://azure.microsoft.com/en-us/services/purview/.

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