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Azure Data Factory Cookbook - Second Edition

You're reading from  Azure Data Factory Cookbook - Second Edition

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781803246598
Pages 532 pages
Edition 2nd Edition
Languages
Authors (4):
Dmitry Foshin Dmitry Foshin
Profile icon Dmitry Foshin
Tonya Chernyshova Tonya Chernyshova
Profile icon Tonya Chernyshova
Dmitry Anoshin Dmitry Anoshin
Profile icon Dmitry Anoshin
Xenia Ireton Xenia Ireton
Profile icon Xenia Ireton
View More author details

Table of Contents (15) Chapters

Preface 1. Getting Started with ADF 2. Orchestration and Control Flow 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

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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