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Azure Data Factory Cookbook

Azure Data Factory Cookbook

By : Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton
4.9 (29)
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Azure Data Factory Cookbook

Azure Data Factory Cookbook

4.9 (29)
By: Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton

Overview of this book

This new edition of the Azure Data Factory book, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF. There are updated and new recipes throughout the book based on developments happening in Azure Synapse, Deployment with Azure DevOps, and Azure Purview. The current edition also runs you through Fabric Data Factory, Data Explorer, and some industry-grade best practices with specific chapters on each. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out the solutions to them. 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 with its latest advancements as the main ETL and orchestration tool for your data warehouse projects.
Table of Contents (15 chapters)
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13
Other Books You May Enjoy
14
Index

Setting up Meta ETL with ADF

When faced with the task of copying vast amounts of objects, such as thousands of tables, or loading data from a diverse range of sources, an effective approach is to leverage a control table that contains a list of object names along with their required copy behaviors. By employing parameterized pipelines, these object names and behaviors can be read from the control table and applied to the jobs accordingly. “Copy behaviors” refer to the specific actions or configurations associated with copying each object. These behaviors can include parameters such as source and destination locations, data transformation requirements, scheduling preferences, error-handling strategies, and any other settings relevant to the copying process.

Unlike traditional methods that require redeploying pipelines whenever the objects list needs modification (e.g., adding or removing objects), utilizing a control table allows for swift and straightforward updates...

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