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How Azure Data Factory Pipelines Move Data From Source Systems to Production

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Azure Data Factory (ADF) is a cloud-based data integration service that orchestrates the movement and transformation of data across enterprise systems. A typical ADF workflow begins with a trigger that determines when a pipeline should run, followed by activities such as data extraction, loading to staging tables, and validation. ADF connects to source systems — including Oracle, MySQL, REST APIs, and cloud storage — through Integration Runtimes, with Self-hosted Integration Runtimes enabling access to data inside private networks. Pipelines can be scheduled, manually triggered, or event-driven, and often work alongside tools like Apache Airflow and dbt to complete end-to-end data transformations. Understanding each component's role — triggers, linked services, integration runtimes, and activities — is key to building and troubleshooting reliable data pipelines in ADF.

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How Azure Data Factory Pipelines Move Data From Source Systems to Production · ShortSingh