Complete Master Class on Pydeequ & AWS Glue Data Quality for ETL Pipelines

You generally write unit tests for your code, but do you also test your data? Incoming data quality can make or break your application. Incorrect, missing, or malformed data can have a large impact on production systems. Examples of data quality issues include the following:
Missing values can lead to failures in the production system that require non-null values (NullPointerException)
Changes in the distribution of data can lead to unexpected outputs of machine learning (ML) models
Aggregations of incorrect data can lead to misguided business decisions

In this video, we will be exploring 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.

Code:
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https://github.com/SatadruMukherjee/D...
https://github.com/SatadruMukherjee/D...

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Your Queries:
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Using PyDeequ with AWS Glue