Mapping Data Flows in Azure Data Factory: Building Scalable ETL Projects in the Microsoft Cloud

Mapping Data Flows in Azure Data Factory: Building Scalable ETL Projects in the Microsoft Cloud
by Mark Kromer
Publisher Finelybook 出版社:Apress; 1st ed. edition (August 26, 2022)
Language 语言:English
pages 页数:212 pages
ISBN-10 书号:1484286111
ISBN-13 书号:9781484286111

Book Description
Build scalable ETL data pipelines in the cloud using Azure Data Factory’s Mapping Data Flows. Each chapter of this book addresses different aspects of an end-to-end data pipeline that includes repeatable design patterns based on best practices using ADF’s code-free data transformation design tools. The book shows data engineers how to take raw business data at cloud scale and turn that data into business value by organizing and transforming the data for use in data science projects and analytics systems.
The book begins with an introduction to Azure Data Factory followed by an introduction to its Mapping Data Flows feature set. Subsequent chapters show how to build your first pipeline and corresponding data flow, implement common design patterns, and operationalize your result. By the end of the book, you will be able to apply what you’ve learned to your complex data integration and ETL projects in Azure. These projects will enable cloud-scale big analytics and data loading and transformation best practices for data warehouses.

What You Will Learn
Build scalable ETL jobs in Azure without writing code
Transform big data for data quality and data modeling requirements
Understand the different aspects of Azure Data Factory ETL pipelines from datasets and Linked Services to Mapping Data Flows
Apply best practices for designing and managing complex ETL data pipelines in Azure Data Factory
Add cloud-based ETL patterns to your set of data engineering skills
Build repeatable code-free ETL design patterns

Who This Book Is For
Data engineers who are new to building complex data transformation pipelines in the cloud with Azure; and data engineers who need ETL solutions that scale to match swiftly growing volumes of data

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