Azure Data Factory Documentation

Welcome to the official documentation for Azure Data Factory, a cloud-based ETL and data integration service that allows you to create data-driven workflows for orchestrating data movement and transforming data.

What is Azure Data Factory?

Azure Data Factory (ADF) is a fully managed, serverless data integration service. ADF allows you to ingest data from various sources, transform it using compute services like Azure Databricks or HDInsight, and load it into data stores like Azure Synapse Analytics or Azure SQL Database. It empowers you to build complex ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) solutions without provisioning or managing infrastructure.

Key Capabilities

• Data Movement: Copy data from a wide array of on-premises and cloud data stores.
• Data Transformation: Transform data using robust compute services.
• Orchestration: Create and schedule data-driven workflows (pipelines) that automate data movement and transformation tasks.
• Monitoring: Monitor pipeline runs, activity executions, and data flow performance through an intuitive interface.
• Hybrid Integration: Connect to and move data between on-premises data stores and cloud services.

Getting Started

Dive into the core concepts of Azure Data Factory:

• Pipelines: Logical groupings of activities that together perform a task.
• Activities: Represent processing steps within a pipeline.
• Datasets: Represent data within the data stores, as input or output of an activity.
• Linked Services: Define the connection information needed for Data Factory to connect to external resources.
• Integration Runtimes: The compute infrastructure used by ADF to perform various data integration capabilities.

Explore Tutorials and Examples

Learn by doing with our comprehensive tutorials, covering common data integration scenarios.

Need Help?

If you encounter any issues, consult our troubleshooting guide or visit the Azure support page.