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Azure Data Factory Cookbook

You're reading from   Azure Data Factory Cookbook Build ETL, Hybrid ETL, and ELT pipelines using ADF, Synapse Analytics, Fabric and Databricks

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781803246598
Length 532 pages
Edition 2nd Edition
Tools
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Authors (4):
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Tonya Chernyshova Tonya Chernyshova
Author Profile Icon Tonya Chernyshova
Tonya Chernyshova
Xenia Ireton Xenia Ireton
Author Profile Icon Xenia Ireton
Xenia Ireton
Dmitry Foshin Dmitry Foshin
Author Profile Icon Dmitry Foshin
Dmitry Foshin
Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
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Toc

Table of Contents (15) Chapters Close

Preface 1. Getting Started with ADF 2. Orchestration and Control Flow FREE CHAPTER 3. Setting Up Synapse Analytics 4. Working with Data Lake and Spark Pools 5. Working with Big Data and Databricks 6. Data Migration – Azure Data Factory and Other Cloud Services 7. Extending Azure Data Factory with Logic Apps and Azure Functions 8. Microsoft Fabric and Power BI, Azure ML, and Cognitive Services 9. Managing Deployment Processes with Azure DevOps 10. Monitoring and Troubleshooting Data Pipelines 11. Working with Azure Data Explorer 12. The Best Practices of Working with ADF 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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