Book Image

Azure Data Engineering Cookbook

By : Ahmad Osama
Book Image

Azure Data Engineering Cookbook

By: Ahmad Osama

Overview of this book

Data engineering is one of the faster growing job areas as Data Engineers are the ones who ensure that the data is extracted, provisioned and the data is of the highest quality for data analysis. This book uses various Azure services to implement and maintain infrastructure to extract data from multiple sources, and then transform and load it for data analysis. It takes you through different techniques for performing big data engineering using Microsoft Azure Data services. It begins by showing you how Azure Blob storage can be used for storing large amounts of unstructured data and how to use it for orchestrating a data workflow. You'll then work with different Cosmos DB APIs and Azure SQL Database. Moving on, you'll discover how to provision an Azure Synapse database and find out how to ingest and analyze data in Azure Synapse. As you advance, you'll cover the design and implementation of batch processing solutions using Azure Data Factory, and understand how to manage, maintain, and secure Azure Data Factory pipelines. You’ll also design and implement batch processing solutions using Azure Databricks and then manage and secure Azure Databricks clusters and jobs. In the concluding chapters, you'll learn how to process streaming data using Azure Stream Analytics and Data Explorer. By the end of this Azure book, you'll have gained the knowledge you need to be able to orchestrate batch and real-time ETL workflows in Microsoft Azure.
Table of Contents (11 chapters)

Deploying Azure Data Factory pipelines using the Azure portal and ARM templates

In this recipe, we'll integrate the pipeline we created in the previous recipe with source control, and we'll learn how to deploy the pipeline to the test and production environment manually using the Azure portal and ARM templates.

Getting ready

To get started, you will need to do the following:

  1. Open the Azure portal at and log in to your Azure account.
  2. Create an Azure DevOps organization and project. We'll use this project to source control the Data Factory pipeline. Refer to the following link on how to create an organization and a project in Azure DevOps:

How to do it…

Let's start integrating the data factory with source control. There are two source control options available with Azure Data Factory – Azure...