May 16, 2019 · In this blog post, we introduce Deequ, an open source tool developed and used at Amazon. Deequ allows you to calculate data quality metrics on your dataset, define and verify data quality constraints, and be informed about changes in the data distribution.

Built on top of the open-source DeeQu framework, AWS Glue Data Quality provides a managed, serverless experience. AWS Glue Data Quality works with Data Quality Definition Language (DQDL), which is a domain specific language that you use to define data quality rules.

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Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. We are happy to receive feedback and contributions. Python users may also be interested in PyDeequ, a Python interface for Deequ. You can find PyDeequ on GitHub, readthedocs, and PyPI. Deequ depends on Java 8.

Dec 30, 2020 · In this post, we introduce PyDeequ, an open source Python wrapper over Deequ (an open source tool developed and used at Amazon). Deequ is written in Scala, whereas PyDeequ allows you to use its data quality and testing capabilities from Python and PySpark, the language of choice for many data scientists.

Data Quality Definition Language (DQDL) is a domain specific language that you use to define rules for AWS Glue Data Quality. This guide introduces key DQDL concepts to help you understand the language. It also provides a reference for …

Use over 25 out-of-the box AWS Glue Data Quality rules to validate your data and identify specific data that causes issues. Implement data quality checks that compare different data sets in disparate data sources in minutes with out-of-the-box rules.

Dec 29, 2020 · Dealing with large, real-world datasets requires a scalable and automated approach to data quality. Deequ is the tool of choice at Amazon when it comes to measuring the quality of large production datasets. It’s used to compute data quality metrics, suggest and verify constraints, and profile data.

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