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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
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14
Index

Ingesting data into Delta Lake using Mapping Data Flows

In the realm of data management, Atomicity, Consistency, Isolation, Durability (ACID) is a foundational set of principles ensuring the reliability and integrity of database transactions. Let’s break down the significance of each component:

  • Atomicity: Guarantees that a transaction is treated as a single, indivisible unit. It either executes in its entirety, or not at all. This ensures that even if a system failure occurs mid-transaction, the database remains in a consistent state.
  • Consistency: Enforces that a transaction brings the database from one valid state to another. Inconsistent states are avoided, providing a reliable and predictable environment for data operations.
  • Isolation: Ensures that transactions operate independently of each other, preventing interference. Isolation safeguards against concurrent transactions affecting each other’s outcomes, maintaining data integrity.
  • Durability...
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