Azure Data Factory Cookbook
eBook - ePub

Azure Data Factory Cookbook

Build and manage ETL and ELT pipelines with Microsoft Azure's serverless data integration service

Dmitry Anoshin, Dmitry Foshin, Roman Storchak, Xenia Ireton

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  1. 382 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Azure Data Factory Cookbook

Build and manage ETL and ELT pipelines with Microsoft Azure's serverless data integration service

Dmitry Anoshin, Dmitry Foshin, Roman Storchak, Xenia Ireton

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Table of contents

About This Book

Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory

Key Features

  • Learn how to load and transform data from various sources, both on-premises and on cloud
  • Use Azure Data Factory's visual environment to build and manage hybrid ETL pipelines
  • Discover how to prepare, transform, process, and enrich data to generate key insights

Book Description

Azure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You'll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you'll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you'll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. You'll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF.

By the end of this book, you'll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects.

What you will learn

  • Create an orchestration and transformation job in ADF
  • Develop, execute, and monitor data flows using Azure Synapse
  • Create big data pipelines using Azure Data Lake and ADF
  • Build a machine learning app with Apache Spark and ADF
  • Migrate on-premises SSIS jobs to ADF
  • Integrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure Functions
  • Run big data compute jobs within HDInsight and Azure Databricks
  • Copy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectors

Who this book is for

This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You'll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is expected.

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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 Platform services and meet main cloud data integration service - Azure Data Factory (ADF). We will login to the Azure and navigate to the Data Factories service in order to create the first data pipeline and run Copy activity. Then, we will do the same exercise but will use different methods of data factories management and control by using Python, PowerShell and Copy Data Tool.
If you don't have an Azure account, we will cover, how you can get a free Azure Account.
In this chapter, we will cover the following recipes:
  • Introduction to the Azure data platform
  • Creating and executing our first job in ADF
  • Creating an ADF pipeline by using the Copy Data tool
  • Create an ADF pipeline using Python
  • Creating a data factory using PowerShell
  • Using templates to create ADF pipelines

Introduction to the Azure data platform

The Azure data platform provides us with a number of data services for databases, data storage, and analytics. In Table 1.1, you can find a list of services and their purpose:
Table 1.1 – Azure data platform services
Table 1.1 – Azure data platform services
Using the Azure data platform services can help you build a modern analytics solution that is secure and scalable. The following diagram shows an example of a typical modern cloud analytics architecture:
Figure 1.1 – Modern analytics solution architecture
Figure 1.1 – Modern analytics solution architecture
You can find most of the Azure data platform services here. ADF is a core service for data movement and transformation.
Let's learn more about the reference architecture in Figure 1.1. It starts with source systems. We can collect data from files, databases, APIs, IoT, and so on. Then, we can use Event Hubs for streaming data and ADF for batch operations. ADF will push data into Azure Data Lake as a staging area, and then we can prepare data for analytics and reporting in Azure Synapse Analytics. Moreover, we can use Databricks for big data processing and machine learning models. Power BI is an ultimate data visualization service. Finally, we can push data into Azure Cosmos DB if we want to use data in business applications.

Getting ready

In this recipe, we will create a free Azure account, log in to the Azure portal, and locate ADF services. If you have an Azure account already, you can skip the creation of the account and log straight into the portal.

How to do it...

Open, then take the following steps:
  1. Click Start Free.
  2. You can sign in to your existing Microsoft account or create a new one. Let's create one as an example.
  3. Enter an email address in the format [email protected] and click Next.
  4. Enter a password of your choice.
  5. Verify your email by entering the code, and click Next.
  6. Fill in the information for your profile (Country, Name, and so on). It will also require your credit card information.
  7. After you have finished the account creation, it will bring you to the Microsoft Azure portal, as shown in the following screenshot:
    Figure 1.2 – Azure portal
    Figure 1.2 – Azure portal
  8. Now, we can explore the Azure portal and find Azure data services. Let's find Azure Synapse Analytics. In the search bar, enter Azure Synapse Analytics and choose Azure Synapse Analytics (formerly SQL DW). It will open the Synapse control panel, as shown in the following screenshot:
Figure 1.3 – Azure Synapse Analytics menu
Figure 1.3 – Azure Synapse Analytics menu
Here, we can launch a new instance of a Synapse data warehouse.
Let's find and create some data factories. In the next recipe, we will create a new data factory.
Before doing anything with ADF, though, let's review what we have covered about an Azure account.

How it works...

Now that we have created a free Azure account, it gives us the following benefits:
  • 12 months of free access to po...

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