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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. Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. The data quality analysis metrics of complete and accurate data are imperative to this step. Table of Contents. 2) Why Do You Need DQM?

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Take Complete Charge Of Customer Satisfaction Metrics – Customer Effort Score, NPS & Customer Satisfaction Score

datapine

Read here how these metrics can drive your customers’ satisfaction up! Customer satisfaction metrics evaluate how the products or services supplied by a company meet or surpass a customer’s expectations. Some examples for triggering event data include time since signup for a product, or complete user onboarding.

Metrics 134
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Amazon DocumentDB zero-ETL integration with Amazon OpenSearch Service is now available

AWS Big Data

Enable change streams on the Amazon DocumentDB collections Amazon DocumentDB change stream events comprise a time-ordered sequence of data changes due to inserts, updates, and deletes on your data. We use these change stream events to transmit data changes from the Amazon DocumentDB cluster to the OpenSearch Service domain.

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How To Improve Your Facility Management With Healthcare Reports

datapine

Like many of today’s most important industries, digital data, metrics and KPIs (key performance indicators) are a part of a bright and prosperous future – and a comprehensive healthcare report has the power to deliver in each of these critical areas. Cutting down unnecessary costs.

Reporting 198
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Monitor Apache Spark applications on Amazon EMR with Amazon Cloudwatch

AWS Big Data

In this post, we demonstrate how to publish detailed Spark metrics from Amazon EMR to Amazon CloudWatch. By default, Amazon EMR sends basic metrics to CloudWatch to track the activity and health of a cluster. Solution overview This solution includes Spark configuration to send metrics to a custom sink.

Metrics 93
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How to achieve Kubernetes observability: Principles and best practices

IBM Big Data Hub

Observability comprises a range of processes and metrics that help teams gain actionable insights into a system’s internal state by examining system outputs. The primary data classes used—known as the three pillars of observability—are logs, metrics and traces.

Metrics 73
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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

It requires an Amazon Simple Queue Service (Amazon S3) queue that receives S3 Event Notifications. You can configure S3 buckets to raise an event any time an object is stored or modified within the bucket to be processed. For a list of supported metrics, refer to Monitoring pipeline metrics.

Data Lake 112