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Azure Data Factory Cookbook

You're reading from   Azure Data Factory Cookbook Build ETL, Hybrid ETL, and ELT pipelines using ADF, Synapse Analytics, Fabric and Databricks

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781803246598
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Tonya Chernyshova Tonya Chernyshova
Author Profile Icon Tonya Chernyshova
Tonya Chernyshova
Xenia Ireton Xenia Ireton
Author Profile Icon Xenia Ireton
Xenia Ireton
Dmitry Foshin Dmitry Foshin
Author Profile Icon Dmitry Foshin
Dmitry Foshin
Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
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Toc

Table of Contents (15) Chapters Close

Preface 1. Getting Started with ADF 2. Orchestration and Control Flow FREE CHAPTER 3. Setting Up Synapse Analytics 4. Working with Data Lake and Spark Pools 5. Working with Big Data and Databricks 6. Data Migration – Azure Data Factory and Other Cloud Services 7. Extending Azure Data Factory with Logic Apps and Azure Functions 8. Microsoft Fabric and Power BI, Azure ML, and Cognitive Services 9. Managing Deployment Processes with Azure DevOps 10. Monitoring and Troubleshooting Data Pipelines 11. Working with Azure Data Explorer 12. The Best Practices of Working with ADF 13. Other Books You May Enjoy
14. Index

Setting up roles and permissions with access levels in ADF

ADF is built on principles of collaboration, and to work effectively you will need to grant access privileges to other users and teams. By its very nature, ADF relies on integration with other services, therefore entities such as users, service principles, and managed identities will require access to resources within your ADF instance. User access management is a pivotal feature of ADF.

Similar to many Azure services, ADF relies on Role-Based Access Control (RBAC). RBAC enables fine-grained definitions of roles that can be granted, or assigned, to users, groups, service principals, or managed identities. These role assignments determine who can perform specific actions, such as viewing or making changes to pipelines, datasets, linked services, and other components, and ultimately govern access to your data workflows.

Imagine a scenario where a company is using ADF to orchestrate their data pipelines, which involves...

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