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Azure Data Factory Cookbook

Azure Data Factory Cookbook

By : Dmitry Anoshin, Dmitry Foshin, Storchak, Xenia Ireton
4.2 (13)
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Azure Data Factory Cookbook

Azure Data Factory Cookbook

4.2 (13)
By: Dmitry Anoshin, Dmitry Foshin, 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)
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Copying data from Google BigQuery to Azure Data Lake Store

In this recipe, we will use Azure Data Factory to import a subset of a public fdic_banks.locations dataset from the Google BigQuery service (a cloud data warehouse) into an Azure Data Lake store. We will write the data into destination storage in Parquet format for convenience.

Getting ready

For this recipe, we assume that you have a Google Cloud account and a project, as well as an Azure account and a Data Lake storage account (ADLS Gen2). The following is a list of additional preparatory work:

  1. You need to enable the BigQuery API for your Google Cloud project. You can enable this API here: https://console.developers.google.com/apis/api/bigquery.googleapis.com/overview.
  2. You will require information for the Project ID, Client ID, Client Secret, and Refresh Token fields for the BigQuery API app. If you are not familiar on how to set up a Google Cloud app and obtain these tokens, you can find detailed instructions...

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