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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 1: Getting Started with ADF

Microsoft Azure is a public cloud vendor. It offers different services for modern organizations. The Azure cloud has several key components, such as compute, storage, databases, and networks. They serve as building blocks for any organization that wants to reap the benefits of cloud computing. There are many benefits to using the cloud, including utilities, metrics, elasticity, and security. Many organizations across the world already benefit from cloud deployment and have fully moved to the Azure cloud. They deploy business applications and run their business on the cloud. As a result, their data is stored in cloud storage and cloud applications.

Microsoft Azure offers a cloud analytics stack that helps us to build modern analytics solutions, extract data from on-premises and the cloud, and use data for decision-making progress, searching patterns in data, and deploying machine learning applications.

In this chapter we will meet Azure Data...

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