Remove tag metrics-and-analytics
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Measure performance of AWS Glue Data Quality for ETL pipelines

AWS Big Data

AWS Glue Data Quality is built on DeeQu , an open source tool developed and used at Amazon to calculate data quality metrics and verify data quality constraints and changes in the data distribution so you can focus on describing how data should look instead of implementing algorithms.

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Use AWS Glue ETL to perform merge, partition evolution, and schema evolution on Apache Iceberg

AWS Big Data

As enterprises collect increasing amounts of data from various sources, the structure and organization of that data often need to change over time to meet evolving analytical needs. For example, an ecommerce company may add new customer demographic attributes or order status flags to enrich analytics.

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Sport analytics leverage AI and ML to improve the game

CIO Business Intelligence

Nearly 10 years ago, Bill James, a pioneer in sports analytics methodology, said if there’s one thing he wished more people understood about sabermetrics, pertaining to baseball, it’s that the data is not the point. Improving player safety in the NFL The NFL is leveraging AI and predictive analytics to improve player safety.

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A Beginner’s Guide To Inventory Metrics And Best Practices

datapine

At the same time, inventory metrics are needed to help managers and professionals in reaching established goals, optimizing processes, and increasing business value. We will finish by presenting a business dashboard that will show how those metrics work together when depicting an inventory data-story. What Are Inventory Metrics?

Metrics 286
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Automated data governance with AWS Glue Data Quality, sensitive data detection, and AWS Lake Formation

AWS Big Data

In this post, we showcase how to use AWS Glue with AWS Glue Data Quality , sensitive data detection transforms , and AWS Lake Formation tag-based access control to automate data governance. Data governance is the process of ensuring the integrity, availability, usability, and security of an organization’s data.

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What Is Rum data and why does it matter?

IBM Big Data Hub

Does that imply that there are “fake” user metrics as well? Plenty of analytics companies use synthetic data to analyze the performance of online applications and services. Real User Monitoring (RUM) data is information about how people interact with online applications and services. Are there alternatives to RUM data?

IT 71
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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

6) Data Quality Metrics Examples. quintillion bytes of data being produced on a daily basis and the wide range of online data analysis tools in the market, the use of data and analytics has never been more accessible. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.