Azure Data Factory Cookbook – Second Edition 版次: A data engineer’s guide to building and managing ETL and ELT pipelines with data integration
Author: Dmitry Foshin (Author), Tonya Chernyshova (Author), Dmitry Anoshin (Author)
Publisher finelybook 出版社: Packt Publishing
Edition 版次: 2nd ed.
Publication Date 出版日期: 2024-02-28
Language 语言: English
Print Length 页数: 532 pages
ISBN-10: 1803246596
ISBN-13: 9781803246598
Book Description
Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory
Key Features:
- Learn how to load and transform data from various sources, both on-premises and on cloud
- Use Azure Data Factory’s visual environment to build and manage hybrid ETL pipelines
- Discover how to prepare, transform, process, and enrich data to generate key insights
Book Description
This new edition of the Azure Data Factory Cookbook, 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.
You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discovering 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. As you advance, you’ll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. You’ll familiarize yourself with the common errors that you may encounter while working with ADF and find out how to use the Azure portal to monitor pipelines. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF.
Two new chapters covering Azure Data Explorer and key best practices have been added, along with new recipes throughout.
By the end of this book, you’ll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects.
What You Will Learn:
- Create an orchestration and transformation job in ADF
- Develop, execute, and monitor data flows using Azure Synapse
- Create big data pipelines using Databricks and Delta tables
- Work with big data in Azure Data Lake using Spark Pool
- Migrate on-premises SSIS jobs to ADF
- Integrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure Functions
- Run big data compute jobs within HDInsight and Azure Databricks
- Copy data from AWS S3 and google Cloud Storage to Azure Storage using ADF’s built-in connectors
Who this book is for:
This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone else who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft’s Azure Data Factory. You’ll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is a prerequisite.
About the Author
Tonya Chernyshova is an Experienced Data Engineer with a proven track record of successfully delivering scalable, maintainable, and impactful data products. She’s Hhighly proficient in Data Modeling, Automation, Cloud Computing, and Data Visualization, consistently driving data-driven insights and business growth.
Dmitry Anoshin is a data-centric technologist and a recognized expert in building and implementing big data and analytics solutions. He has a successful track record when it comes to implementing business and digital intelligence projects in numerous industries, including retail, finance, marketing, and e-commerce. Dmitry possesses in-depth knowledge of digital/business intelligence, ETL, data warehousing, and big data technologies. He has extensive experience in the data integration process and is proficient in using various data warehousing methodologies. Dmitry has constantly exceeded project expectations when he has worked in the financial, machine tool, and retail industries. He has completed a number of multinational full BI/DI solution life cycle implementation projects. With expertise in data modeling, Dmitry also has a background and business experience in multiple relation databases, OLAP systems, and NoSQL databases. He is also an active speaker at data conferences and helps people to adopt cloud analytics.
