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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

Leveraging ADF scalability: Performance tuning of an ADF pipeline

Due to its serverless architecture, ADF is inherently scalable, dynamically adjusting its resource allocation to meet workload demands without the need for users to manage physical servers. This flexible architecture offers users various techniques to enhance the performance of their data pipelines.

One approach for improving performance involves harnessing the power of parallelism, such as incorporating a ForEach activity into your pipelines. The ForEach activity allows for the parallel processing of data by iterating over a collection of items, executing a specified set of activities for each item in parallel. This can significantly reduce overall execution time, especially when dealing with large datasets or when multiple independent tasks can be processed concurrently.

For example, suppose you have a pipeline that needs to process data from multiple files stored in Azure Blob Storage. By using a ForEach...

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