Remove Dashboards Remove Metadata Remove Metrics
article thumbnail

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. It involves: Reviewing data in detail Comparing and contrasting the data to its own metadata Running statistical models Data quality reports.

article thumbnail

Deploy Amazon QuickSight dashboards to monitor AWS Glue ETL job metrics and set alarms

AWS Big Data

In this post, we explore how to combine AWS Glue usage information and metrics with centralized reporting and visualization using QuickSight. You have metrics available per job run within the AWS Glue console, but they don’t cover all available AWS Glue job metrics, and the visuals aren’t as interactive compared to the QuickSight dashboard.

Metrics 100
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

In this blog post, we’ll discuss how the metadata layer of Apache Iceberg can be used to make data lakes more efficient. You will learn about an open-source solution that can collect important metrics from the Iceberg metadata layer. It supports two types of reports: one for commits and one for scans.

Metadata 118
article thumbnail

Use Amazon Kinesis Data Streams to deliver real-time data to Amazon OpenSearch Service domains with Amazon OpenSearch Ingestion

AWS Big Data

For example, you can use metadata about the Kinesis data stream name to index by data stream ( ${getMetadata("kinesis_stream_name") ), or you can use document fields to index data depending on the CloudWatch log group or other document data ( ${path/to/field/in/document} ).

Metadata 108
article thumbnail

How REA Group approaches Amazon MSK cluster capacity planning

AWS Big Data

Solution overview The MSK clusters in Hydro are configured with a PER_TOPIC_PER_BROKER level of monitoring, which provides metrics at the broker and topic levels. These metrics help us determine the attributes of the cluster usage effectively. We then match these attributes to the relevant MSK metrics available.

Metrics 78
article thumbnail

How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

In the following section, two use cases demonstrate how the data mesh is established with Amazon DataZone to better facilitate machine learning for an IoT-based digital twin and BI dashboards and reporting using Tableau. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog.

IoT 100
article thumbnail

Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight

AWS Big Data

In Part 2 of this series, we discussed how to enable AWS Glue job observability metrics and integrate them with Grafana for real-time monitoring. Grafana provides powerful customizable dashboards to view pipeline health. QuickSight makes it straightforward for business users to visualize data in interactive dashboards and reports.

Metrics 118