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Azure Data Engineering Cookbook

You're reading from   Azure Data Engineering Cookbook Get well versed in various data engineering techniques in Azure using this recipe-based guide

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781803246789
Length 608 pages
Edition 2nd Edition
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Authors (3):
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Ahmad Osama Ahmad Osama
Author Profile Icon Ahmad Osama
Ahmad Osama
Nagaraj Venkatesan Nagaraj Venkatesan
Author Profile Icon Nagaraj Venkatesan
Nagaraj Venkatesan
Luca Zanna Luca Zanna
Author Profile Icon Luca Zanna
Luca Zanna
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Toc

Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Creating and Managing Data in Azure Data Lake 2. Chapter 2: Securing and Monitoring Data in Azure Data Lake FREE CHAPTER 3. Chapter 3: Building Data Ingestion Pipelines Using Azure Data Factory 4. Chapter 4: Azure Data Factory Integration Runtime 5. Chapter 5: Configuring and Securing Azure SQL Database 6. Chapter 6: Implementing High Availability and Monitoring in Azure SQL Database 7. Chapter 7: Processing Data Using Azure Databricks 8. Chapter 8: Processing Data Using Azure Synapse Analytics 9. Chapter 9: Transforming Data Using Azure Synapse Dataflows 10. Chapter 10: Building the Serving Layer in Azure Synapse SQL Pool 11. Chapter 11: Monitoring Synapse SQL and Spark Pools 12. Chapter 12: Optimizing and Maintaining Synapse SQL and Spark Pools 13. Chapter 13: Monitoring and Maintaining Azure Data Engineering Pipelines 14. Index 15. Other Books You May Enjoy

Creating workload groups for advanced workload management

Workload groups in Synapse dedicated pools allow you to define resource pools with custom resource allocation percentages compared to the predefined percentages offered by resource classes. Additionally, workload groups offer the flexibility to set minimum and maximum resource percentages for the entire pool and also for each request. Setting a minimum resource percentage for a workload group ensures there will always be a percentage of resource reserved for the queries mapped to the resource group. In this recipe, we will define a custom resource group with minimum and maximum resource percentages for the pool and for each request, and we will also classify it in such a way that the resource allocation changes depending on the time of query execution.

Getting ready

Create a Synapse Analytics workspace as explained in the Provisioning an Azure Synapse Analytics workspace recipe of Chapter 8, Processing Data Using Azure...

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