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Measure performance of AWS Glue Data Quality for ETL pipelines

AWS Big Data

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

AWS Big Data

Today, we are pleased to announce that Amazon DataZone is now able to present data quality information for data assets. This information empowers end-users to make informed decisions as to whether or not to use specific assets. Amazon DataZone data assets can be updated at varying frequencies.

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Get started with AWS Glue Data Quality dynamic rules for ETL pipelines

AWS Big Data

They establish data quality rules to ensure the extracted data is of high quality for accurate business decisions. These rules assess the data based on fixed criteria reflecting current business states. We are excited to talk about how to use dynamic rules , a new capability of AWS Glue Data Quality.

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Visualize data quality scores and metrics generated by AWS Glue Data Quality

AWS Big Data

AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues.

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Getinge’s digital transformation shows scaling and adapting in equal measure

CIO Business Intelligence

There’s been good progress but, admittedly, there’s still a lot of work to do, says Nilsson, since it’s difficult to take ownership in a large organization and streamline data management. There’s also a lot of information about what other companies are doing, and for us it’s important to translate it into our own unique ideas,” he says.

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The Gold Standard – The Key to Information Extraction and Data Quality Control

Ontotext

Originally, the Gold Standard was a monetary system that required countries to fix the value of their currencies to a certain amount of gold, aiming to replace the unreliable human control with a fixed measurement that could be used by everyone. Simply put, we need to be able to measure and evaluate our results against clearly set criteria.