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Part 1 – Unlock the Power of Azure Data Factory: A Guide to Boosting Your Data Ingestion Process

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Introduction

In the world of cloud architecture, Azure Data Factory (ADF) is a powerful tool for securely collecting, ingesting, and preparing data for health care industry solutions. This guide will walk you through the process of developing and deploying an ADF into multiple environments.

Part 1: Architecture and Scenario

This section will cover the basic architecture and scenario for setting up an ADF. It will discuss the creation of resources in Azure, including Azure Storage Containers and Azure Key Vaults. It will also explain how to create an Azure Data Factory with Key Vault access.

Part 2: Configure Azure Data Factory Source Control

This section will focus on configuring source control for your ADF. It will cover the process of constructing a data pipeline and publishing the concept for the ADF. It will also discuss configuring and deploying Azure resources for the ADF.

Part 3: The YAML Pipeline Structure

In this part, we will delve into the YAML pipeline structure for deploying the ADF. It will explain the publishing process and the parameterization of the ARM template. It will also provide a step-by-step guide for deploying the ADF using the ARM template.

Part 4: How to use Azure DevOps Pipeline Templates

This section will guide you through the process of using Azure DevOps pipeline templates for deploying the ADF. It will discuss the benefits of using templates and provide examples of how to implement them in your deployment process.

Part 5: How to deploy Azure Data Factory Linked Templates

The final part of the series will cover the deployment of Azure Data Factory linked templates. It will explain what linked templates are and how to deploy them in your ADF. It will also provide best practices and tips for optimizing the deployment process.

Don't forget to check out the full series in the Healthcare and Life Sciences Tech Community for a comprehensive guide to developing and deploying an Azure Data Factory into multiple environments.