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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
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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

The Best Practices of Working with ADF

Welcome to the final chapter of Azure Data Factory Cookbook, where we delve into the best practices for working with Azure Data Factory (ADF) and Azure Synapse. Throughout this cookbook, we’ve explored a multitude of recipes and techniques to help you harness the power of ADF for your data integration and transformation needs. In this closing chapter, we’ll guide you through essential considerations, strategies, and practical recipes that will elevate your ADF projects to new heights of efficiency, security, and scalability.

We will cover the following list of recipes in this chapter:

  • Setting up roles and permissions with access levels for working with ADF
  • Setting up Meta ETL with ADF
  • Scaling your ADF project
  • Using ADF disaster recovery built-in features
  • Change data capture
  • Managing data factory costs with FinOps
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