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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics

AWS Big Data

Today we are pleased to announce a new class of Amazon CloudWatch metrics reported with your pipelines built on top of AWS Glue for Apache Spark jobs. The new metrics provide aggregate and fine-grained insights into the health and operations of your job runs and the data being processed. workerUtilization showed 1.0

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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.

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Deriving Value from Data Lakes with AI

Sisense

AI and ML are the only ways to derive value from massive data lakes, cloud-native data warehouses, and other huge stores of information. There just aren’t enough AI and data science practitioners to go around to tackle this lofty goal. Apply that metric to any other business-critical function.

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

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

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Data Lakes: What Are They and Who Needs Them?

Jet Global

To address the flood of data and the needs of enterprise businesses to store, sort, and analyze that data, a new storage solution has evolved: the data lake. What’s in a Data Lake? Data warehouses do a great job of standardizing data from disparate sources for analysis. Taking a Dip.

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Analyzing the business-case approach Perdue Farms takes to derive value from data

CIO Business Intelligence

The data can also help us enrich our commodity products. How are you populating your data lake? We’ve decided to take a practical approach, led by Kyle Benning, who runs our data function. Then our analytics team, an IT group, makes sure we build the data lake in the right sequence.

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5 ways to maximize your cloud investment

CIO Business Intelligence

Optimizing cloud investments requires close collaboration with the rest of the business to understand current and future needs, building effective FinOps teams, partnering with providers, and ongoing monitoring of key performance metrics. We need hard metrics because we’re running 800 instances of cloud computers.