Master Azure Data Factory with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make Azure Data Factory powerful for automation & workflows workflows.
Azure Data Factory is the standalone, mature PaaS service available as an independent Azure resource, billed on a granular pay-per-use model. Microsoft Fabric Data Factory is a re-imagined version embedded inside the Microsoft Fabric SaaS platform, sharing capacity-based pricing with the rest of Fabric (Power BI, Synapse, OneLake) and introducing new experiences like Dataflow Gen2 and Fabric pipelines. They share many concepts and connectors but are separate products with different pricing, governance, and integration models. Microsoft continues to invest in both, but new strategic features increasingly debut in Fabric first.
ADF connects to on-premises and private-network data sources through the Self-Hosted Integration Runtime (SHIR), a lightweight agent installed on a Windows machine inside your network. The SHIR establishes outbound-only encrypted connections to the Azure Data Factory service, eliminating the need for inbound firewall rules or VPN tunnels. It supports clustering for high availability and load balancing across multiple nodes, and handles credential management locally so secrets never leave the network.
Yes, in two ways. First, ADF can natively rebuild SSIS workflows using its own pipeline and Mapping Data Flow capabilities, which is the recommended modernization path. Second, the SSIS Integration Runtime allows you to lift-and-shift existing SSIS packages into ADF with minimal changes, running them on managed Azure SSIS instances. This is unique to Azure and gives Microsoft-shop customers a gradual migration option rather than forcing a full rewrite.
ADF uses several separate consumption meters: pipeline orchestration (per activity run), data movement (per Data Integration Unit-hour for the Copy activity), data flow execution (per vCore-hour of the Spark cluster running Mapping Data Flows), SSIS Integration Runtime (per hour of provisioned compute), and inactive pipeline charges. Costs vary significantly based on workload patterns — a heavy data flow job can be far more expensive than a simple copy of the same data volume. Microsoft's pricing calculator and the cost analysis blade in Azure Cost Management are essential tools for forecasting.
Not in the true streaming sense. ADF supports event-based triggers that fire pipelines in response to blob storage or custom events, and it can process micro-batches on tight schedules (down to 1 minute via tumbling windows), but it is not a stream processing engine. For sub-second latency, complex event processing, or continuous ingestion of high-velocity event streams, Microsoft recommends pairing ADF with Azure Event Hubs, Azure Stream Analytics, or Databricks Structured Streaming.
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Tutorial updated March 2026