Book Image

Azure Data Factory Cookbook

By : Dmitry Anoshin, Dmitry Foshin, Roman Storchak, Xenia Ireton
Book Image

Azure Data Factory Cookbook

By: Dmitry Anoshin, Dmitry Foshin, Roman Storchak, Xenia Ireton

Overview of this book

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.
Table of Contents (12 chapters)

Creating and executing our first job in ADF

ADF allows us to create workflows for transforming and orchestrating data movement. You may think of ADF as an ETL (short for Extract, Transform, Load) tool for the Azure cloud and the Azure data platform. ADF is Software as a Service (SaaS). This means that we don't need to deploy any hardware or software. We pay for what we use. Often, ADF is referred to as a code-free ETL as a service. The key operations of ADF are listed here:

  • Ingest: Allows us to collect data and load it into Azure data platform storage or any other target location. ADF has 90+ data connectors.
  • Control flow: Allows us to design code-free extracting and loading.
  • Data flow: Allows us to design code-free data transformations.
  • Schedule: Allows us to schedule ETL jobs.
  • Monitor: Allows us to monitor ETL jobs.

We have learned about the key operations in ADF. Next, we should try them.

Getting ready

In this recipe, we will continue on...