Remove 2000 Remove Dashboards Remove Optimization
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Write queries faster with Amazon Q generative SQL for Amazon Redshift

AWS Big Data

With Amazon Q, you can spend less time worrying about the nuances of SQL syntax and optimizations, allowing you to concentrate your efforts on extracting invaluable business insights from your data. Refer to Easy analytics and cost-optimization with Amazon Redshift Serverless to get started. For this post, we use Redshift Serverless.

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Amazon EMR Serverless observability, Part 1: Monitor Amazon EMR Serverless workers in near real time using Amazon CloudWatch

AWS Big Data

For example, underutilization of vCPUs or memory can reveal resource wastage, allowing you to optimize worker sizes to achieve potential cost savings. Solution overview To further enhance this observability experience, we have created a solution that gathers all these metrics on a single CloudWatch dashboard for an EMR Serverless application.

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Introducing Amazon MWAA larger environment sizes

AWS Big Data

xlarge 8 vCPUs / 24 GB 4 vCPUs / 12 GB 40 tasks (default) Up to 2000 mw1.2xlarge 16 vCPUs / 48 GB 8 vCPUs / 24 GB 80 tasks (default) Up to 4000 With the introduction of these larger environments, your Amazon Aurora metadata database will now use larger, memory-optimized instances powered by AWS Graviton2.

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Becoming an Intelligent, Sustainable Enterprise with SAP

Timo Elliott

With a goal to optimize end-to-end processes and accelerate the organization’s digital journey, they looked for more efficient ways to execute all the manual and time-consuming financial forecasting process across their decentralized R&D business units. Create a sustainable world together. Trusted by customers.

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How to Build a Performant Data Warehouse in Redshift

Sisense

This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. Amazon describes the dense storage nodes (DS2) as optimized for large data workloads and use hard disk drives (HDD) for storage. Sort & Dist Keys.

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The Very Best Digital Metrics For 15 Different Companies!

Occam's Razor

There are Search people and Content people and Landing Page Optimizers and Cart fixers and Attribution Specialists and more. The goal is for each company’s Google Data Studio to not look like a CDP (customized data puke), but to be a focused strategic dashboard with an emphasis on IABI. If you want to play along.

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Unintentional data

The Unofficial Google Data Science Blog

More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Every data scientist surely has a story of identifying important issues by monitoring metrics on dashboards without having any particular hypothesis about what they are looking for.