Compare Azure Data Factory versions 1 and 2

This article compares version 2 (V2) with version 1 (V1) of Azure Data Factory. For an introduction to V1 of the service, see Data Factory version 1 - introduction, and for V2, see Data Factory version 2 - introduction.

Feature comparison

The following table compares features of V1 and V2.

Feature V1 V2
Datasets A named view of data that references the data you want to use in your activities as inputs and outputs. Datasets identify data within different data stores, such as tables, files, folders, and documents. For example, an Azure Blob dataset specifies the blob container and folder in Blob storage from which the activity should read the data.

Availability defines the processing window slicing model for the dataset (for example, hourly, daily, etc.).
Datasets are the same in V2. However, you do not need to define availability schedules for datasets. You can define a trigger resource that can schedule pipelines from a clock scheduler paradigm. For more information, see Triggers and Datasets.
Linked services Linked services are much like connection strings, which define the connection information needed for Data Factory to connect to external resources. Same as Data Factory V1, but with a new connectVia property to utilize the Data Factory V2 Integration Runtime compute environment. For more information, see Integration runtimes and Linked service properties for Azure Blob Storage.
Pipelines A data factory can have one or more pipelines. A pipeline is a logical grouping of activities that together perform a task. You used startTime, endTime, isPaused to schedule and run pipelines. Pipelines are still groups of activities to be performed on data. However, the scheduling of activities in the pipeline has been separated into new trigger resources. You can think of pipelines in Data Factory V2 more as “workflow units” that you schedule separately via triggers.

Pipelines do not have “windows” of time execution in Data Factory V2. The Data Factory V1 concepts of startTime, endTime, and isPaused are no longer present in Data Factory V2. For more information, see Pipeline execution and triggers and Pipelines and activities.
Activities Activities define actions to perform on your data within a pipeline. Data movement (copy activity) and data transformation activities (such as Hive, Pig, and MapReduce) are supported. In Data Factory V2, activities still are defined actions within a pipeline. V2 introduces new control flow activities. You use these activities in control flow (looping & branching). Data movement and data transformation activities that were supported in V1 are supported in V2. You can define transformation activities without using datasets in V2.
Hybrid data movement and activity dispatch Previously known as Data Management Gateway supported moving data between on-premises and cloud. Now, it's called Integration Runtime. Data Management Gateway is now referred to as Self-hosted Integration Runtime. It provides the same capability as in V1.

The Azure-SSIS Integration Runtime in V2 also supports deploying and running SQL Server Integration Services (SSIS) packages in the cloud. For details, see Integration runtime article.
Parameters NA Parameters are key-value pairs of read-only configuration settings that are defined in pipelines. You can pass arguments for the parameters when you are manually executing the pipeline. If you are using a scheduler trigger, the trigger can pass values for the parameters too. Activities within the pipeline consume the parameter values.
Expressions Data Factory V1 allows you to use functions and system variables in data selection queries and activity/dataset properties. In Data Factory V2, you can use expressions anywhere in a JSON string value. For more information, see Expressions and Functions in V2.
Pipeline runs NA A single instance of a pipeline execution. For example, say you have a pipeline that executes at 8am, 9am, and 10am. There would be three separate runs of pipeline (pipeline runs) in this case. Each pipeline run has a unique pipeline run ID, which is a GUID that uniquely defines that particular pipeline run. Pipeline runs are typically instantiated by passing arguments to parameters defined in the pipelines.
Activity Runs NA An instance of an activity execution within a pipeline.
Trigger runs NA An instance of a trigger execution. For more information, see Triggers.
Scheduling Scheduling is based on pipeline start/end times, and dataset availability. Scheduler trigger or execution via external scheduler. For more information, see Pipeline execution and triggers.

The following sections provide more information capabilities of version 2:

Control flow

To support diverse integration flows and patterns in the modern data warehouse, Data Factory V2 has enabled a new flexible data pipeline model that is no longer tied with time-series data. A few common flows that are now enabled that were previously not possible are:

Chaining activities

In version 1, you had to configure output of an activity as input of another activity to chain them. In V2, you can chain activities in a sequence within a pipeline. You can use the dependsOn property in an activity definition to chain it with an upstream activity. For more information and an example, see Pipelines and activities and Branching and chaining activities articles.

Branching activities

Now, in V2, you can branch activities within a pipeline. The If-condition activity provides the same functionality that an if statement provides in programming languages. It evaluates a set of activities when the condition evaluates to true and another set of activities when the condition evaluates to false. For an example of branching activities, see Branching and chaining activities tutorial.


You can define parameters at the pipeline level and pass arguments while you're invoking the pipeline on-demand or from a trigger. Activities can consume the arguments that are passed to the pipeline. For more information, see Pipelines and triggers.

Custom state passing

Activity outputs including state can be consumed by a subsequent activity in the pipeline. For example, in the JSON definition of an activity, you can access the output of the previous activity by using the following syntax: @activity('NameofPreviousActivity').output.value. This feature allows you to build workflows where values can pass through activities.

Looping containers

The ForEach activity defines a repeating control flow in your pipeline. This activity is used to iterate over a collection and executes specified activities in a loop. The loop implementation of this activity is similar to Foreach looping structure in programming languages.

The Until activity provides the same functionality that a do-until looping structure provides in programming languages. It executes a set of activities in a loop until the condition associated with the activity evaluates to true. You can specify a timeout value for the until activity in Data Factory.

Trigger-based flows

Pipelines can be triggered by on-demand or wall-clock time. The pipelines and triggers article has the detailed information about triggers.

Invoke a pipeline from another pipeline

The Execute Pipeline activity allows a Data Factory pipeline to invoke another pipeline.

Delta flows

A key use case in ETL patterns is “delta loads”, or only loading data that changed since the last iteration of a pipeline. New capabilities in version 2 such as lookup activity, flexible scheduling, and control flow enable this use case in a natural way. For a tutorial with step-by-step instructions, see Tutorial: Incremental copy.

Other control flow activities

Here are a few more control flow activities supported by Data Factory V2:

Control activity Description
ForEachActivity ForEach Activity defines a repeating control flow in your pipeline. This activity is used to iterate over a collection and executes specified activities in a loop. The loop implementation of this activity is similar to Foreach looping structure in programming languages.
WebActivity Web Activity can be used to call a custom REST endpoint from a Data Factory pipeline. You can pass datasets and linked services to be consumed and accessed by the activity.
Lookup Activity Lookup Activity can be used to read or look up a record/ table name/ value from any external source. This output can further be referenced by succeeding activities.
Get Metadata Activity GetMetadata activity can be used to retrieve metadata of any data in Azure Data Factory.
Wait Activity The pipeline waits (pauses) for the specified period of time.

Deploy SSIS packages to Azure

If you want to move your SQL Server Integration Services (SSIS) workloads to cloud, create a data factory V2, and provision an Azure-SSIS Integration Runtime (IR). The Azure-SSIS IR is a fully managed cluster of Azure VMs (nodes) that are dedicated to running your SSIS packages in the cloud. Once you provision Azure-SSIS IR, you can use the same tools that you have been using to deploy SSIS packages to an on-premises SSIS environment. For example, you can use SQL Server Data Tools (SSDT) or SQL Server Management Studio (SSMS) to deploy SSIS packages to this runtime on Azure. For step-by-step instructions, see the tutorial Deploy SQL Server Integration Services packages to Azure.

Flexible scheduling

In Data Factory V2, you do not need to define dataset availability schedules. You can define a trigger resource that can schedule pipelines from a clock scheduler paradigm. You can also pass parameters to pipelines from a trigger for a flexible scheduling / execution model. Pipelines do not have “windows” of time execution in Data Factory V2. The Data Factory V1 concepts of startTime, endTime, and isPaused are no longer present in Data Factory V2. Here is how to build and then schedule a pipeline in Data Factory V2: Pipeline execution and triggers.

Support for more data stores

V2 supports copy data to/from more data stores than V1. For a list of supported data stores, see the following articles:

Support for on-demand Spark cluster

V2 supports creation of an on-demand HDInsight Spark cluster. To create an on-demand Spark cluster, specify the cluster type as Spark in your on-demand HDInsight linked service definition. Then, you can configure the Spark activity in your pipeline to use this linked service. At runtime, when the activity is executed, the Data Factory service automatically creates the Spark cluster for you. For more information, see the following articles:

Custom activities

In V1, you implement (Custom) DotNet Activity code by creating a .Net Class Library project with a class that implements the Execute method of the IDotNetActivity interface. Therefore, your custom code needs to be written in .Net Framework 4.5.2 and be executed on Windows-based Azure Batch Pool nodes.

In V2 Custom Activity, you are not required to implement a .Net interface. You can now directly run commands, scripts, and run your own custom code complied as an executable.

For more information, see Difference between custom activity in V1 and V2.


Version 2 of Data Factory provides a richer set of SDKs that can be used to author, manage, and monitor pipelines.

  • .NET SDK: The .NET SDK is updated for V2.
  • PowerShell: The PowerShell cmdlets are updated for V2. The V2 cmdlets have DataFactoryV2 in the name. For example: Get-AzureRmDataFactoryV2.
  • Python SDK: This SDK is new to V2.
  • REST API: The REST API is updated for V2.

The SDKs that are updated for V2 are not backward-compatible with version 1 clients.

Authoring experience

Data Factory V1 allows you to author pipelines by using Data Factory Editor in the Azure portal. Currently, Data Factory V2 supports only programmatic way (.NET SDK, REST API, PowerShell, Python, etc.) to create data factories. There is no user interface support yet. Data Factory V1 also supports SDK, REST, and PowerShell authoring support.

Monitoring experience

In V2, you can also monitor data factories by using Azure Monitor. The new PowerShell cmdlets support monitoring of integration runtimes. Both V1 and V2 support visual monitoring via monitoring application that can be launched from the Azure portal.

Next steps

Learn how to create a data factory by following step-by-step instructions in the following Quickstarts: PowerShell, .NET, Python, REST API.