Azure Data Factory Cookbook : Build and manage ETL and ELT pipelines with Microsoft Azure's serverless data integration service

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

Azure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps 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. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you'll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise 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. The book will take you through the common errors that you may encounter while working with ADF and show you 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.

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 Azure Data Lake and ADF
• Build a machine learning app with Apache Spark and ADF
• 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 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 expected.

Table of Contents

1. Getting Started with ADF
2. Orchestration and Control Flow
3. Setting up a Cloud Data Warehouse
4. Working with Azure Data Lake
5. Working with Big Data - HDInsight and Databricks
6. Integration with MS SSIS
7. Data Migration - Azure Data Factory and Other Cloud Services
8. Working with Azure Services Integration
9. Managing Deployment Processes with Azure DevOps
10. Monitoring and Troubleshooting Data Pipelines

Dmitry Anoshin is an expert in analytics with 10 years of experience. He started using Tableau as a primary BI tool in 2011 as a BI consultant at Teradata. He is certified in both Tableau Desktop and Tableau Server. He leads probably the biggest Tableau user community, with more than 2,000 active users. This community has two to three Tableau talks every month led by top Tableau experts, Tableau Zen Masters, Viz Champions, and more. In addition, Dmitry has previously written three books with Packt and reviewed more than seven books. Finally, he is an active speaker at data conferences and helps people to adopt cloud analytics.

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.

Roman Storchak is a PhD, and is a chief data officer whose main interest lies in building data-driven cultures through making analytics easy. He has led teams that have built ETL-heavy products in AdTech and retail and often uses Azure Stack, PowerBI, and Data Factory.

Xenia Ireton is a software engineer at Microsoft and has extensive knowledge in the field of data engineering, big data pipelines, data warehousing, and systems architecture.