ELT & data integration · Microsoft

Azure Data Factory

Microsoft's cloud ETL/ELT and orchestration service for building and scheduling data pipelines across Azure and hybrid sources.

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Azure Data Factory is Microsoft's managed data integration and orchestration service, combining a visual pipeline designer with code-first options for building, scheduling and monitoring data movement and transformation at scale. Mapping Data Flows provide a no-code, Spark-backed transformation engine, while pipelines can also invoke external compute such as Databricks notebooks, stored procedures or HDInsight for custom logic. It integrates a Self-Hosted Integration Runtime for moving on-premises data into Azure, making it a common choice for hybrid estates already standardized on Microsoft infrastructure. Billing is fully consumption-based across several independent meters (orchestration runs, data movement, data flow compute, and external activity execution), which makes costs granular but harder to estimate than flat-fee competitors.

At a glance

Vendor Microsoft
Pricing model Usage-based
Free tier Yes
Deployment Cloud
Open source No
Best for Organizations standardized on Azure needing pipeline orchestration across cloud and on-premises sources.

Pricing

Consumption pricing billed separately for pipeline orchestration (per 1,000 runs), data movement (per Data Integration Unit-hour), Data Flow execution (per vCore-hour) and external activity execution; first five low-frequency activities per month are free.

Plan Price Notes
Orchestration & execution Billed per 1,000 activity/trigger/debug runs Separate rates for low-frequency (≤ once daily) vs. high-frequency triggers; 20% discount above 100 monthly activities
Data movement Billed per Data Integration Unit-hour (DIU-hour) Rate varies by region and cloud vs. on-premises source
Data Flow execution Billed per vCore-hour General Purpose and Memory Optimized clusters, 8 vCore minimum, reserved-instance discounts available

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Visual pipeline designer and code-first authoring
  • Spark-backed Mapping Data Flows
  • Self-Hosted Integration Runtime for on-premises data
  • 90+ built-in connectors
  • Native orchestration of Databricks and HDInsight activities
  • Git-based CI/CD integration
  • Trigger-based and event-based scheduling

Integrations

Profile last reviewed September 21, 2026

Alternatives

AWS Glue Serverless AWS ETL service using Apache Spark under the hood, billed per Data Processing Unit-hour with no infrastructure to manage. Google Cloud Dataflow Fully managed, serverless service for running unified batch and streaming data pipelines built on Apache Beam. Matillion Cloud-native ELT platform with a low-code visual pipeline designer that pushes transformations down into the target warehouse. Talend Enterprise data integration and data quality suite, now sold as Qlik Talend Cloud after Qlik's acquisition of Talend.

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Terms to know

ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. ELT (extract, load, transform) A data integration pattern that loads raw data first and transforms it afterward using the warehouse's own compute. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Workflow orchestration Scheduling, sequencing and monitoring interdependent data tasks so each step runs only after the ones it depends on succeed.

Related guides

How to choose an ELT tool ELT tools differ less on connector count than on who operates them, how pricing scales, and whether transformation happens in the tool or downstream.