Solve real-world data problems and create data-driven workflows with ease using Azure Data Factory
Build an ADF pipeline that operates on pre-built ML model and Azure AI
Get up and running with Fabric Data Explorer and extend ADF with Logic Apps and Azure functions
Description
This new edition of the Azure Data Factory book, 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. There are updated and new recipes throughout the book based on developments happening in Azure Synapse, Deployment with Azure DevOps, and Azure Purview. The current edition also runs you through Fabric Data Factory, Data Explorer, and some industry-grade best practices with specific chapters on each.
You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discover 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. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out the solutions to them. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF.
By the end of this book, you’ll be able to use ADF with its latest advancements as the main ETL and orchestration tool for your data warehouse projects.
Who is this book 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.
What you will learn
Build and Manage data pipelines with ease using the latest version of ADF
Configure, load data, and operate data flows with Azure Synapse
Get up and running with Fabric Data Factory
Working with Azure Data Factory and Azure Purview
Create big data pipelines using Databricks and Delta tables
Integrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure Functions
Learn industry-grade best practices for using Azure Data Factory
I loved the book as it brings many screenshots and recipe-like instructions with the steps to do any task.The book brings information from creating the first job, going through big data, Databricks , and deployment.
Amazon Verified review
ArdenMar 03, 2024
5
Azure, data bricks, and fabric everything rolled in one! Everything u can ask for in one thing excellent resource must read!
Amazon Verified review
Souvik RoyMar 01, 2024
5
The book is great to start with for people who are transitioning into data engineering and cloud computing. I am using it in my academics to understand the essence of data engineering and trying to implement it in real-world projects. I would recommend people who want to study data engineering from scratch and also Azure details are quite good for beginners.
Amazon Verified review
Vivek VermaMay 10, 2024
5
This book covers a lot of topics. Given the title of the book was about ADF, I was really suprised to see the breadth of topics that were covered. The subtitle is A data engineer's guide to building and managing ETL and ELT pipelines with data integration. And that gives a clue to the fact that the coverage is much, much broader than ADF.I was surprised to see so much coverage of pipelines in other places like Synapse Analytics, Fabric, etc. but more surprised to see coverage of HDInsight and big data concepts. I can't remember the last time I saw anyone using HDInsight. I always thought it was seriously over-hyped while it was being promoted, and still think the same way.It made more sense to see a bunch of coverage of Databricks, delta tables and integrating ADF with Azure Machine Learning, Azure Logic Apps, Azure Functions and more. They are relatively common areas of integration for ADF, along with migrating on-premises SSIS packages to ADF.
Amazon Verified review
Steven FernandesMar 02, 2024
5
This guide is a valuable resource for data engineers and architects exploring Azure's data processing capabilities, covering Azure Data Factory, Synapse, Databricks, Delta tables, and more. It offers insights into big data handling, migration of SSIS jobs to ADF, and integration with Azure services like ML and Logic Apps. However, its effectiveness is slightly marred by a confusing table of contents with sections like "Getting Read," "How to Do It...," and "How It Works...". A clearer, more intuitive organization would enhance its utility, making it easier for readers to navigate and locate specific information. Despite this, the book remains an indispensable tool for professionals aiming to build comprehensive data solutions in Azure.
Dmitry Foshin is a business intelligence team leader, whose main goals are delivering business insights to the management team through data engineering, analytics, and visualization. He has led and executed complex full-stack BI solutions (from ETL processes to building DWH and reporting) using Azure technologies, Data Lake, Data Factory, Data Bricks, MS Office 365, PowerBI, and Tableau. He has also successfully launched numerous data analytics projects – both on-premises and cloud – that help achieve corporate goals in international FMCG companies, banking, and manufacturing industries.
Xenia Ireton is a Senior Software Engineer at Microsoft. She has extensive knowledge in building distributed services, data pipelines and data warehouses.
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.
Tonya Chernyshova is an experienced Data Engineer with over 10 years in the field, including time at Amazon. Specializing in Data Modeling, Automation, Cloud Computing (AWS and Azure), and Data Visualization, she has a strong track record of delivering scalable, maintainable data products. Her expertise drives data-driven insights and business growth, showcasing her proficiency in leveraging cloud technologies to enhance data capabilities.
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