Introduction to Azure Data Factory

Modern data engineering often involves moving and transforming vast amounts of data from various sources to destinations, ensuring it's ready for analytics or business applications. This is where Azure Data Factory (ADF) comes in - a powerful, cloud-based data integration service designed to simplify and automate these tasks.

Azure Data Factory (ADF) is a Platform as a Service (PaaS) offering from Microsoft that enables you to create, manage, and monitor ETL/ELT data pipelines. These pipelines orchestrate workflows to:

• Extract data from diverse sources like on-premises databases, APIs, and cloud storage;
• Transform the data by cleansing, aggregating, or enriching it;
• Load it into destinations such as Azure SQL Database, Data Lakes, or Power BI.

Key Components of Azure Data Factory

• Pipelines: the core unit in ADF where you define the sequence of tasks (activities) to process your data;
• Datasets: represent your data structure, such as a file in Blob Storage or a table in a database;
• Linked Services: define the connection details to your data sources and destinations (e.g., credentials, endpoints).

Why Use Azure Data Factory?

• Seamless data movement: connects to over 90 data sources, both cloud-based and on-premises;
• No-Code/Low-Code development: build pipelines visually using a drag-and-drop interface or define workflows in code;
• Scalability: automatically scales to handle large datasets and high-frequency data transfers;
• Cost-Effective: pay-as-you-go pricing with no upfront costs.