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Azure Databricks Cookbook

You're reading from   Azure Databricks Cookbook Accelerate and scale real-time analytics solutions using the Apache Spark-based analytics service

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781789809718
Length 452 pages
Edition 1st Edition
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Authors (2):
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Vinod Jaiswal Vinod Jaiswal
Author Profile Icon Vinod Jaiswal
Vinod Jaiswal
Phani Raj Phani Raj
Author Profile Icon Phani Raj
Phani Raj
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Toc

Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Creating an Azure Databricks Service 2. Chapter 2: Reading and Writing Data from and to Various Azure Services and File Formats FREE CHAPTER 3. Chapter 3: Understanding Spark Query Execution 4. Chapter 4: Working with Streaming Data 5. Chapter 5: Integrating with Azure Key Vault, App Configuration, and Log Analytics 6. Chapter 6: Exploring Delta Lake in Azure Databricks 7. Chapter 7: Implementing Near-Real-Time Analytics and Building a Modern Data Warehouse 8. Chapter 8: Databricks SQL 9. Chapter 9: DevOps Integrations and Implementing CI/CD for Azure Databricks 10. Chapter 10: Understanding Security and Monitoring in Azure Databricks 11. Other Books You May Enjoy

Deploying notebooks to multiple environments

The Azure DevOps CI/CD process can be used to deploy Azure resources and artifacts to various environments from the same release pipelines. Also, we can set the deployment sequence specifically to the needs of a project or application. For example, you can deploy notebooks to the test environment first. If the deployment to the test environment succeeds, then deploy them to UAT, and later, upon approval of the changes, they can be deployed to the production environment. In this recipe, you will learn how to use variable groups and mapping groups in a specific environment and use the values in variables at runtime to deploy Azure Databricks notebooks to different environments.

In variable groups, we create a set of variables that hold values that can be used in the release pipeline for all the stages or scope it to one specific stage. For example, if we have two stages in the release pipeline that are deploying to different environments...

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