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Important—The price in R$ is merely a reference; this is an international transaction and the final price is subject to exchange rates and the inclusion of IOF taxes. An eNF will not be issued.
US government entities are eligible to purchase Azure Government services from a licensing solution provider with no upfront financial commitment, or directly through a pay-as-you-go online subscription.
Important—The price in R$ is merely a reference; this is an international transaction and the final price is subject to exchange rates and the inclusion of IOF taxes. An eNF will not be issued.
Pricing for Data Pipeline is calculated based on:
- Pipeline orchestration and execution
- Data flow execution and debugging
- Number of Data Factory operations such as create pipelines and pipeline monitoring
Data Factory Pipeline Orchestration and Execution
Pipelines are control flows of discrete steps referred to as activities. You pay for data pipeline orchestration by activity run and activity execution by integration runtime hours. The integration runtime, which is serverless in Azure and self-hosted in hybrid scenarios, provides the compute resources used to execute the activities in a pipeline. Integration runtime charges are prorated by the minute and rounded up.
For example, the Azure Data Factory copy activity can move data across various data stores in a secure, reliable, performant, and scalable way. As data volume or throughput needs grow, the integration runtime can scale out to meet those needs.
| Type | Azure Integration Runtime Price | Azure Managed VNET Integration Runtime Price | Self-Hosted Integration Runtime Price |
|---|---|---|---|
| Orchestration1 | $- per 1,000 runs | $- per 1,000 runs | $- per 1,000 runs |
| Data movement Activity2 | $-/DIU-hour | $-/DIU-hour | $-/hour |
| Pipeline Activity3 | $-/hour |
$-/hour (Up to 50 concurrent pipeline activities) |
$-/hour |
| External Pipeline Activity4 | $-/hour |
$-/hour (Up to 800 concurrent pipeline activities) |
$-/hour |
Data Flow Execution and Debugging
Data Flows are visually-designed components inside of Data Factory that enable data transformations at scale. You pay for the Data Flow cluster execution and debugging time per vCore-hour. The minimum cluster size to run a Data Flow is 8 vCores. Execution and debugging charges are prorated by the minute and rounded up. Change Data Capture artifacts are billed at General Purpose rates for 4-vCore clusters during public preview of CDC.
Change Data Capture (CDC) objects execute on the same data flow compute infrastructure using a single node 4 vCore machine. The same Data Flow Reserved Instance pricing discount also applies to CDC resources.
| Type | Price | One Year Reserved (% Savings) |
Three Year Reserved (% Savings) |
|---|---|---|---|
| General Purpose | $- per vCore-hour | $- per vCore-hour | $- per vCore-hour |
| Memory Optimised | $- per vCore-hour | $- per vCore-hour | $- per vCore-hour |
Azure Data Factory workflow orchestration manager
| Size | Workflow Capacity | Scheduler vCPU | Worker vCPU | Web Server vCPU | Price Per Hour |
|---|---|---|---|---|---|
| Small (D2v4) | Up to 50 DAGs | 2 | 2 | 2 | $- |
| Large (D4v4) | Up to 1,000 DAGs | 4 | 4 | 4 | $- |
| Additional node | Worker vCPU | Price Per Hour |
|---|---|---|
| Small (D2v4) | 2 | $- |
| Large (D4v4) | 4 | $- |
Data Factory Operations
| Type | Price | Examples |
|---|---|---|
| Read/Write* | $- per 50,000 modified/referenced entities | Read/write of entities in Azure Data Factory* |
| Monitoring | $- per 50,000 run records retrieved | Monitoring of pipeline, activity, trigger, and debug runs** |
The pricing for Data Factory usage is calculated based on the following factors:
- The frequency of activities (high or low). A low frequency activity does not execute more than once in a day (for example, daily, weekly, monthly); a high-frequency activity executes more than once in a day (for example, hourly, every 15 mins). See Orchestration of activities section below for details.
- Where the activities run (cloud or on-premises). See Data Movement section below.
- Whether a pipeline is active or not. See Inactive Pipelines section below.
- Whether you are re-running an activity. See Re-running activities section below.
Orchestration of activities
| Low frequency | High frequency | |
|---|---|---|
|
Activites running in the cloud (examples: copy activity moving data from an Azure blob to an Azure SQL database; hive activity running hive script on an Azure HDInsight cluster). |
$- per activity per month | $- per activity per month |
|
Activities running on-premises and involving a self-hosted Integration Runtime (examples: copy activity moving data from an on-premises SQL Server database to Azure blob; stored procedure activity running a stored procedure in an on-premises SQL Server database). |
$- per activity per month | $- per activity per month |
Data Movement
Azure Data Factory can copy data between various data stores in a secure, reliable, performant and scalable way. As your volume of data or data movement throughput needs grow, Azure Data Factory can scale out to meet those needs. Refer to the Copy Activity Performance Guide to learn about leveraging data movement units to boost your data movement performance.
| Data Movement between Cloud data stores | $- per hour |
| Data Movement when an on-premises store is involved | $- per hour |
Inactive Pipelines
You must specify an active data processing period using a date/time range (start and end times) for each pipeline you deploy to the Azure Data Factory. The pipeline is considered as active for the specified period even if its activities are not actually running. It is considered as inactive at all other times.
An inactive pipeline is charged at $- per month.
Pipelines that are inactive for an entire month are billed at the applicable "inactive pipeline" rate for the month. Pipelines that are inactive for a portion of a month are billed for their inactive periods on a prorated basis for the number of hours they are inactive in that month. For example, if a pipeline has a starting date and time of January 1, 2016 at 12:00 AM and an ending date and time of January 20, 2016 at 12:00 AM, the pipeline is considered active for those 20 days and inactive for 11 days. The charge for inactive pipeline ($-) is prorated for 11 days.
If a pipeline does not have an active data processing period (a start and end time) specified, it is considered inactive.
Re-running activities
Activities can be re-run if needed (for example, if the data source was unavailable during the scheduled run). The cost of re-running activities varies based on the location where the activity is run. The cost of re-running activities in the cloud is $- per 1,000 re-runs. The cost of re-running activities on-premises is $- per 1,000 re-runs.
Example
Suppose you have a data pipeline with the following two activities that run once a day (low-frequency):
- A Copy activity that copies data from an on-premises SQL Server database to an Azure blob.
- A Hive activity that runs a hive script on an Azure HDInsight cluster.
Assume that it takes 2 hours in a day to move data from on-premises SQL Server database to Azure blob storage. The following table shows costs associated with this pipeline:
| First activity (copying data from on-premises to Azure) | |
| Data Movement Cost (per month) | 30 days per month |
| 2 hours per day | |
| $- | |
| $- | |
| Orchestration of Activities Cost (per month) | $- |
| Subtotal (per month) | $- |
| Second activity (a Hive script running on Azure HDInsight) | |
| Data Movement Cost (per month) | $- |
| Orchestration of Activities Cost (per month) | $- |
| Subtotal (per month) | $- |
| Total activities (per month) | $- |
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Azure Data Factory
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Frequently asked questions
Azure Data Factory V2
-
Read/write operations include create, read, update, and delete Azure Data Factory entities. Entities include datasets, linked services, pipelines, integration runtime, and triggers.
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Monitoring operations include get and list for pipeline, activity, trigger, and debug runs.
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An activity is a step within a pipeline. The execution of each activity is called a run.
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An integration runtime is the compute infrastructure used by Azure Data Factory to provide the following data integration capabilities across different network environments:
- Data movement: Transfer of data between data stores in public and private (on-premise or virtual private) networks, providing support for built-in connectors, format conversion, column mapping, and performant and scalable data transfer.
- Activity despatch: Despatching and monitoring of transformation activities running on a variety of compute services, such as Azure HDInsight, Azure Machine Learning, Azure SQL Database, SQL Server, and others.
- SQL Server Integration Services package execution: Native execution of SQL Server Integration Service packages in a managed Azure compute environment.
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A trigger is a unit of processing that determines when a pipeline execution needs to be initiated. A trigger run is the execution of a trigger, which may produce an activity run if the conditions are satisfied.
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A debug run is a test run that a user can perform during iterative development to ensure the steps in the pipeline are working as intended before changes are published to the data factory.
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An inactive pipeline is one that’s not associated with a trigger and that has zero runs within a month. A charge is incurred after one month of zero runs.
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Pipeline execution activities (Azure integration runtime data movement, pipeline activities, external and self-hosted integration runtime data movement, pipeline activities, and external) are billed at the hourly rate shown above. Pipeline execution charges are prorated by the minute and rounded up.
For example: If you run an operation that takes 2 minutes and 20 seconds, you will be billed for 3 minutes.
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Find scenario-based pricing examples on the Azure Data Factory Documentation page.
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Check out guidance on how to plan and manage ADF costs on the Azure Data Factory domination page.
Azure Data Factory V1
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Activities define the actions to perform on your data. Each activity takes zero or more datasets as inputs and produces one or more datasets as output. An activity is a unit of orchestration in Azure Data Factory.
For example, you may use a Copy activity to orchestrate copying data from one dataset to another. Similarly, you may use a Hive activity to run a Hive query on an Azure HDInsight cluster to transform or analyse your data. Azure Data Factory provides a wide range of data transformation and data movement activities. You may also choose to create a customised .NET activity to run your own code.
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A pipeline is a logical grouping of activities. Pipelines can be active for a user-specified period of time (start and end times). Pipelines are inactive at all other times.
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Yes. If the Activity uses Azure services such as HDInsight, those services are billed separately at their per service rates.
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There are two sets of costs incurred when you perform a data copy. First, the compute resources that are used for performing the copy are represented by the data movement metre. There are cloud and on-premises versions of the data movement metre, and on-premises data movement is less expensive because a portion of the compute associated with the copy is performed by your own on-premises resources. Data movement charges are prorated by the minute and rounded up. (For example, a data copy taking 41 minutes 23 seconds of compute time will result in a charge for 42 minutes).
Second, you may incur data transfer charges, which will show up as a separate outbound data transfer line item on your note. Outbound data transfer charges are applied when data goes out of Azure data centres. See Data Transfers Pricing Details for more information.
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