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
Other Books You May Enjoy
14
Index

Managing Data Factory costs with FinOps

Data Factory is a crucial service for data processing in Azure, but managing its costs effectively is essential to avoid unexpected expenses.

FinOps is a set of practices and principles that help organizations manage their cloud costs efficiently. It involves collaboration between finance, IT, and business teams to optimize cloud spending, allocate costs accurately, and drive accountability. The goal of FinOps is to strike a balance between cost optimization and enabling cloud innovation.

Examples of applying FinOps principles to ADF include:

  • Resource Right-sizing:: Analyze the compute resources used by your Data Factory pipelines and adjust them based on actual workload requirements. For instance, if certain pipelines consistently underutilize resources, consider downsizing the compute instances to save costs.
  • Schedule Optimization: Leverage Data Factory’s scheduling capabilities to run pipelines during off-peak...