| 书目名称 | Mapping Data Flows in Azure Data Factory |
| 副标题 | Building Scalable ET |
| 编辑 | Mark Kromer |
| 视频video | |
| 概述 | Shows how to build scalable, cloud-first ETL solutions in Azure.Enables you to perform data transformations without writing code.Covers reusable design patterns and best practices for the cloud |
| 图书封面 |  |
| 描述 | 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 |
| 出版日期 | Book 2022 |
| 关键词 | Mapping Data Flows; Azure Data Factory; Microsoft Azure; Azure Data Factory Cookbook; ETL Pipelines; Data |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4842-8612-8 |
| isbn_softcover | 978-1-4842-8611-1 |
| isbn_ebook | 978-1-4842-8612-8 |
| copyright | Mark Kromer 2022 |
发表于 2025-3-21 17:58:31
