Book Image

Azure Data Factory Cookbook - Second Edition

By : Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton
4 (1)
Book Image

Azure Data Factory Cookbook - Second Edition

4 (1)
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)
13
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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...