Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and orchestrate data workflows at scale. It enables you to move and transform data from various sources to any destination, such as Azure Data Lake Storage, Azure Blob Storage, Azure SQL Database, and more.

The architecture of Azure Data Factory is based on a modular design that enables you to create, manage, and monitor your data pipelines with ease. It is built on top of a distributed computing infrastructure, which means that you can easily scale up or down depending on your needs. Azure Data Factory also includes a visual interface that allows you to create and manage data pipelines through a drag-and-drop interface.

Azure Data Factory can be used in a variety of ways, including:

• Data integration: Azure Data Factory can be used to integrate data from various sources, such as on-premises systems, cloud storage services, and third-party data sources.

• Data migration: Azure Data Factory can be used to migrate data from one location to another, such as from on-premises to the cloud, or between different cloud providers.

• Data transformation: Azure Data Factory can be used to transform data using various transformation activities, such as mapping data between different formats or joining data from multiple sources.

One of the key benefits of Azure Data Factory is its ability to integrate with other Azure services, such as Azure Data Lake Storage, Azure Blob Storage, and Azure SQL Database. This allows you to build end-to-end data solutions using a range of Azure services, and to take advantage of the scalability, reliability, and security of the Azure cloud.

Azure Data Factory is an incredibly useful tool for businesses that need to move and transform data at scale. We particularly like its modular architecture, visual interface, and low-code environment which is complimented by its versatility.

You can see the books I used to practice using ADF here. WELL worth a read.