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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

Provisioning Azure Data Factory

To get started with Azure Data Factory, you need to run an Azure Data Factory account. An Azure Data Factory account is comprised of the following key components:

  • Linked services: A component that maintains the connection credentials to data sources. An example of this is a connection to a SQL database/text file.
  • Dataset: The data that’s obtained after connecting to the data source using a linked service. An example of this is a group of tables or files connected via a linked service.
  • Activity: A task that will process the dataset. An example of this is a copy activity that moves the data from a flat file to a database.
  • Data flow: These are specific tasks that perform data transformations on datasets. An example of this is pivoting or sorting a dataset while it is being moved from source to destination. This can be done by a data flow transformation task.
  • Integration runtime: This is the Azure Data Factory engine that...
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