AWS Big Data

MARCH 12, 2024

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.

Data Quality 93

Data Quality Measurement Testing Visualization 93

datapine

JANUARY 6, 2022

In fact, a Digital Universe study found that the total data supply in 2012 was 2.8 Yet, before any serious data interpretation inquiry can begin, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding scales of measurement. trillion gigabytes!

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

IBM Big Data Hub

AUGUST 9, 2023

Even a simple web application can benefit from observability by implementing basic logging and metrics. In such scenarios, observability becomes crucial to trace requests across different services, measure latency and pinpoint performance bottlenecks. As their systems grow in complexity, they face new challenges and potential failures.

Metrics 68

Metrics Interactive Software Data-driven 68

AWS Big Data

APRIL 3, 2024

Many organizations already use AWS Glue Data Quality to define and enforce data quality rules on their data, validate data against predefined rules , track data quality metrics, and monitor data quality over time using artificial intelligence (AI). The metrics are saved in Amazon S3 to have a persistent output.

Data Quality 93

Data Quality Visualization Metadata Metrics 93

Cloudera

AUGUST 6, 2018

Fox Foundation is testing a watch-type wearable device in Australia to continuously monitor the symptoms of patients with Parkinson’s disease. This is important because unlike diabetes or high blood pressure we don’t yet have clear metrics for Parkinson’s. For example, the Michael J.

Machine Learning 50

Machine Learning Internet of Things Cost-Benefit Data-driven 50

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. In fact, Hainmueller (2012) show that entropy balancing is equivalent to estimating the weights as a log-linear model of the covariate functions $c_j(X)$.

Statistics 44

Statistics Optimization Modeling Experimentation 44