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Azure Data Factory Cookbook

You're reading from   Azure Data Factory Cookbook Build and manage ETL and ELT pipelines with Microsoft Azure's serverless data integration service

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800565296
Length 382 pages
Edition 1st Edition
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Authors (4):
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Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
Roman Storchak Roman Storchak
Author Profile Icon Roman Storchak
Roman Storchak
Xenia Ireton Xenia Ireton
Author Profile Icon Xenia Ireton
Xenia Ireton
Dmitry Foshin Dmitry Foshin
Author Profile Icon Dmitry Foshin
Dmitry Foshin
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Toc

Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Getting Started with ADF 2. Chapter 2: Orchestration and Control Flow FREE CHAPTER 3. Chapter 3: Setting Up a Cloud Data Warehouse 4. Chapter 4: Working with Azure Data Lake 5. Chapter 5: Working with Big Data – HDInsight and Databricks 6. Chapter 6: Integration with MS SSIS 7. Chapter 7: Data Migration – Azure Data Factory and Other Cloud Services 8. Chapter 8: Working with Azure Services Integration 9. Chapter 9: Managing Deployment Processes with Azure DevOps 10. Chapter 10: Monitoring and Troubleshooting Data Pipelines 11. Other Books You May Enjoy

Chapter 8: Working with Azure Services Integration

The Azure ecosystem has a variety of different services. Most of them can be integrated and connected into Azure Data Factory (ADF). In this chapter, we will cover how to do integrations of the most commonly used Azure services into ADF. Also, you will understand how Azure services can be useful in designing Extract, Transform, Load (ETL) pipelines.

You will learn how to build a pipeline that executes a logic app upon completion and how to integrate ADF with the following Azure services: Azure Machine Learning (ML), Azure Web Apps, and Azure Functions.

We will cover the following recipes in this chapter:

  • Triggering your data processing with Logic Apps
  • Using the web activity to call an Azure logic app
  • Adding flexibility to your pipelines with Azure Functions
  • Automatically building ML models with speed and scale
  • Transforming and preparing your data via Azure Databricks
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