Azure Data Factory by Example Practical Implementation for Data Engineers 1st Edition by Richard Swinbank ISBN 1484270282  978-1484270288

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ISBN 10: 1484270282
ISBN 13: 978-1484270288
Author: Richard Swinbank

Data engineers who need to hit the ground running will use this book to build skills in Azure Data Factory v2 (ADF). The tutorial-first approach to ADF taken in this book gets you working from the first chapter, explaining key ideas naturally as you encounter them. From creating your first data factory to building complex, metadata-driven nested pipelines, the book guides you through essential concepts in Microsoft’s cloud-based ETL/ELT platform. It introduces components indispensable for the movement and transformation of data in the cloud. Then it demonstrates the tools necessary to orchestrate, monitor, and manage those components.
The hands-on introduction to ADF found in this book is equally well-suited to data engineers embracing their first ETL/ELT toolset as it is to seasoned veterans of Microsoft’s SQL Server Integration Services (SSIS). The example-driven approach leads you through ADF pipeline construction from the ground up, introducing important ideas and making learning natural and engaging. SSIS users will find concepts with familiar parallels, while ADF-first readers will quickly master those concepts through the book’s steady building up of knowledge in successive chapters. Summaries of key concepts at the end of each chapter provide a ready reference that you can return to again and again.

What You Will Learn

• Create pipelines, activities, datasets, and linked services
• Build reusable components using variables, parameters, and expressions
• Move data into and around Azure services automatically
• Transform data natively using ADF data flows and Power Query data wrangling
• Master flow-of-control and triggers for tightly orchestrated pipeline execution
• Publish and monitor pipelines easily and with confidence

Who This Book Is For
Data engineers and ETL developers taking their first steps in Azure Data Factory, SQL Server Integration Services users making the transition toward doing ETL in Microsoft’s Azure cloud, and SQL Server database administrators involved in data warehousing and ETL operations

Azure Data Factory by Example: Practical Implementation for Data Engineers 1st Table of contents:

• Preface

  • Overview of Azure Data Factory (ADF) and its relevance to modern data engineering.
  • How to use this book effectively for hands-on learning.

• Chapter 1: Introduction to Azure Data Factory

  • What is Azure Data Factory?
  • Key components: Pipelines, Datasets, Linked Services, and Activities.
  • Overview of ADF architecture and its role in cloud-based ETL processes.

• Chapter 2: Setting Up Your Azure Data Factory Environment

  • Creating an Azure subscription and setting up an ADF instance.
  • Navigating the Azure portal and using ADF Studio.
  • Configuring linked services for data sources.

• Chapter 3: Building Your First Data Pipeline

  • Introduction to pipelines and their role in ADF.
  • Step-by-step guide to building a simple data pipeline.
  • Running, debugging, and monitoring your first pipeline.

• Chapter 4: Working with Datasets and Linked Services

  • Understanding datasets and their relationship with data sources.
  • Creating and managing datasets in ADF.
  • Configuring linked services for connections to on-premises and cloud data sources.

• Chapter 5: Data Movement and Transformation

  • Data flow activities in ADF: Copy Activity, Data Flow, and more.
  • Handling data movement between various sources (Azure Blob Storage, SQL Database, etc.).
  • Implementing basic data transformations using ADF.

• Chapter 6: Advanced Data Transformation with Mapping Data Flows

  • Creating complex data transformations with Mapping Data Flows.
  • Using transformation techniques like filters, joins, and aggregates.
  • Best practices for designing scalable data flows.

• Chapter 7: Scheduling and Orchestrating Data Pipelines

  • Setting up triggers to schedule pipeline execution.
  • Managing pipeline dependencies and failure handling.
  • Using triggers for incremental loads and event-based executions.

• Chapter 8: Monitoring, Logging, and Debugging Pipelines

  • Monitoring pipeline runs and checking activity logs.
  • Implementing alerts and notifications for pipeline failures or successes.
  • Debugging common issues in ADF pipelines.

• Chapter 9: Integrating Azure Data Factory with Other Azure Services

  • Using ADF with Azure Databricks, Azure SQL Data Warehouse, and Azure Synapse Analytics.
  • Integrating ADF with Azure Machine Learning for advanced analytics workflows.
  • Leveraging ADF for data lake management and data warehousing.

• Chapter 10: Securing Your Data and Managing Access

  • Securing data in transit and at rest within ADF.
  • Setting up Azure Active Directory (AAD) authentication and role-based access control (RBAC).
  • Best practices for securing data workflows and managing permissions.

• Chapter 11: Optimizing ADF Pipelines for Performance and Cost Efficiency

  • Performance tuning for ADF pipelines and data flows.
  • Managing data throughput, partitioning, and parallelism.
  • Cost optimization strategies in ADF.

• Chapter 12: Real-World Examples and Case Studies

  • Case study 1: Building an end-to-end ETL pipeline for data migration.
  • Case study 2: Real-time data integration using ADF and Azure Stream Analytics.
  • Case study 3: Using ADF for building a data lake solution.

• Chapter 13: Advanced Topics in Azure Data Factory

  • Handling complex data sources and formats (e.g., JSON, Parquet, Avro).
  • Data integration patterns and orchestration strategies.
  • Working with ADF in hybrid and multi-cloud environments.

• Chapter 14: Future Trends and the Evolution of Azure Data Factory

  • The future of data engineering and cloud-based ETL.
  • Upcoming features and enhancements in Azure Data Factory.
  • The role of ADF in the broader Azure ecosystem and data management.

• Appendices

  • A. ADF CLI and PowerShell Commands for Automation
  • B. Azure Data Factory REST API and Programmatic Access
  • C. Additional Resources and Documentation

• Glossary

  • Definitions of key terms used throughout the book.

• Index

  • A comprehensive index for quick reference to topics and features covered in the book.

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