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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. In this post, we explore how to connect QuickSight to Amazon CloudWatch metrics and build graphs to uncover trends in AWS Glue job observability metrics.

Metrics 107
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AI recommendations for descriptions in Amazon DataZone for enhanced business data cataloging and discovery is now generally available

AWS Big Data

Without the right metadata and documentation, data consumers overlook valuable datasets relevant to their use case or spend more time going back and forth with data producers to understand the data and its relevance for their use case—or worse, misuse the data for a purpose it was not intended for.

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How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

AWS Big Data

To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. We used the same AWS Glue jobs to further transform and load the data into the required S3 bucket and a portion of extracted metadata into DynamoDB.

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Data Insights for Everyone — The Semantic Layer to the Rescue

Rocket-Powered Data Science

They realized that the search results would probably not provide an answer to my question, but the results would simply list websites that included my words on the page or in the metadata tags: “Texas”, “Cows”, “How”, etc. The BI team may be focused on KPIs, forecasts, trends, and decision-support insights.

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Bringing an AI Product to Market

O'Reilly on Data

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 361
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Blending Art and Science: Using Data to Forecast and Manage Your Sales Pipeline

Sisense

Analytics and sales should partner to forecast new business revenue and manage pipeline, because sales teams that have an analyst dedicated to their data and trends, drive insights that optimize workflows and decision making. After creating the daily snapshot, then calculate the metadata such as: how many times is that opportunity pushed?

Sales 91
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How to Manage Risk with Modern Data Architectures

Cloudera

Up your liquidity risk management game Historically, technological limitations made it difficult for financial institutions to accurately forecast and manage liquidity risk. Financial institutions can use ML and AI to: Support liquidity monitoring and forecasting in real time. Enhance counterparty risk assessment.