Move data by using Copy Activity


This article applies to version 1 of Data Factory. If you are using the current version of the Data Factory service, see Copy Activity in V2.


In Azure Data Factory, you can use Copy Activity to copy data between on-premises and cloud data stores. After the data is copied, it can be further transformed and analyzed. You can also use Copy Activity to publish transformation and analysis results for business intelligence (BI) and application consumption.

Role of Copy Activity

Copy Activity is powered by a secure, reliable, scalable, and globally available service. This article provides details on data movement in Data Factory and Copy Activity.

First, let's see how data migration occurs between two cloud data stores, and between an on-premises data store and a cloud data store.


To learn about activities in general, see Understanding pipelines and activities.

Copy data between two cloud data stores

When both source and sink data stores are in the cloud, Copy Activity goes through the following stages to copy data from the source to the sink. The service that powers Copy Activity:

  1. Reads data from the source data store.
  2. Performs serialization/deserialization, compression/decompression, column mapping, and type conversion. It does these operations based on the configurations of the input dataset, output dataset, and Copy Activity.
  3. Writes data to the destination data store.

The service automatically chooses the optimal region to perform the data movement. This region is usually the one closest to the sink data store.

Cloud-to-cloud copy

Copy data between an on-premises data store and a cloud data store

To securely move data between an on-premises data store and a cloud data store, install Data Management Gateway on your on-premises machine. Data Management Gateway is an agent that enables hybrid data movement and processing. You can install it on the same machine as the data store itself, or on a separate machine that has access to the data store.

In this scenario, Data Management Gateway performs the serialization/deserialization, compression/decompression, column mapping, and type conversion. Data does not flow through the Azure Data Factory service. Instead, Data Management Gateway directly writes the data to the destination store.

On-premises-to-cloud copy

See Move data between on-premises and cloud data stores for an introduction and walkthrough. See Data Management Gateway for detailed information about this agent.

You can also move data from/to supported data stores that are hosted on Azure IaaS virtual machines (VMs) by using Data Management Gateway. In this case, you can install Data Management Gateway on the same VM as the data store itself, or on a separate VM that has access to the data store.

Supported data stores and formats

Copy Activity in Data Factory copies data from a source data store to a sink data store. Data Factory supports the following data stores. Data from any source can be written to any sink. Click a data store to learn how to copy data to and from that store.


If you need to move data to/from a data store that Copy Activity doesn't support, use a custom activity in Data Factory with your own logic for copying/moving data. For details on creating and using a custom activity, see Use custom activities in an Azure Data Factory pipeline.

Category Data store Supported as a source Supported as a sink
Azure Azure Blob storage
  Azure Cosmos DB for NoSQL
  Azure Data Lake Storage Gen1
  Azure SQL Database
  Azure Synapse Analytics
  Azure Cognitive Search Index
  Azure Table storage
Databases Amazon Redshift
  SAP Business Warehouse*
  SQL Server*
NoSQL Cassandra*
File Amazon S3
  File System*
Others Generic HTTP
  Generic OData
  Generic ODBC*
  Web Table (table from HTML)


Data stores with * can be on-premises or on Azure IaaS, and require you to install Data Management Gateway on an on-premises/Azure IaaS machine.

Supported file formats

You can use Copy Activity to copy files as-is between two file-based data stores, you can skip the format section in both the input and output dataset definitions. The data is copied efficiently without any serialization/deserialization.

Copy Activity also reads from and writes to files in specified formats: Text, JSON, Avro, ORC, and Parquet, and compression codec GZip, Deflate, BZip2, and ZipDeflate are supported. See Supported file and compression formats with details.

For example, you can do the following copy activities:

  • Copy data in a SQL Server database and write to Azure Data Lake Store in ORC format.
  • Copy files in text (CSV) format from on-premises File System and write to Azure Blob in Avro format.
  • Copy zipped files from on-premises File System and decompress then land to Azure Data Lake Store.
  • Copy data in GZip compressed text (CSV) format from Azure Blob and write to Azure SQL Database.

Globally available data movement

Azure Data Factory is available only in the West US, East US, and North Europe regions. However, the service that powers Copy Activity is available globally in the following regions and geographies. The globally available topology ensures efficient data movement that usually avoids cross-region hops. See Services by region for availability of Data Factory and Data Movement in a region.

Copy data between cloud data stores

When both source and sink data stores are in the cloud, Data Factory uses a service deployment in the region that is closest to the sink in the same geography to move the data. Refer to the following table for mapping:

Geography of the destination data stores Region of the destination data store Region used for data movement
United States East US East US
  East US 2 East US 2
  Central US Central US
  North Central US North Central US
  South Central US South Central US
  West Central US West Central US
  West US West US
  West US 2 West US 2
Canada Canada East Canada Central
  Canada Central Canada Central
Brazil Brazil South Brazil South
Europe North Europe North Europe
  West Europe West Europe
United Kingdom UK West UK South
  UK South UK South
Asia Pacific Southeast Asia Southeast Asia
  East Asia Southeast Asia
Australia Australia East Australia East
  Australia Southeast Australia Southeast
India Central India Central India
  West India Central India
  South India Central India
Japan Japan East Japan East
  Japan West Japan East
Korea Korea Central Korea Central
  Korea South Korea Central

Alternatively, you can explicitly indicate the region of Data Factory service to be used to perform the copy by specifying executionLocation property under Copy Activity typeProperties. Supported values for this property are listed in above Region used for data movement column. Note your data goes through that region over the wire during copy. For example, to copy between Azure stores in Korea, you can specify "executionLocation": "Japan East" to route through Japan region (see sample JSON as reference).


If the region of the destination data store is not in preceding list or undetectable, by default Copy Activity fails instead of going through an alternative region, unless executionLocation is specified. The supported region list will be expanded over time.

Copy data between an on-premises data store and a cloud data store

When data is being copied between on-premises (or Azure virtual machines/IaaS) and cloud stores, Data Management Gateway performs data movement on an on-premises machine or virtual machine. The data does not flow through the service in the cloud, unless you use the staged copy capability. In this case, data flows through the staging Azure Blob storage before it is written into the sink data store.

Create a pipeline with Copy Activity

You can create a pipeline with Copy Activity in a couple of ways:

By using the Copy Wizard

The Data Factory Copy Wizard helps you to create a pipeline with Copy Activity. This pipeline allows you to copy data from supported sources to destinations without writing JSON definitions for linked services, datasets, and pipelines. See Data Factory Copy Wizard for details about the wizard.

By using JSON scripts

You can use Data Factory Editor in Visual Studio, or Azure PowerShell to create a JSON definition for a pipeline (by using Copy Activity). Then, you can deploy it to create the pipeline in Data Factory. See Tutorial: Use Copy Activity in an Azure Data Factory pipeline for a tutorial with step-by-step instructions.

JSON properties (such as name, description, input and output tables, and policies) are available for all types of activities. Properties that are available in the typeProperties section of the activity vary with each activity type.

For Copy Activity, the typeProperties section varies depending on the types of sources and sinks. Click a source/sink in the Supported sources and sinks section to learn about type properties that Copy Activity supports for that data store.

Here's a sample JSON definition:

  "name": "ADFTutorialPipeline",
  "properties": {
    "description": "Copy data from Azure blob to Azure SQL table",
    "activities": [
        "name": "CopyFromBlobToSQL",
        "type": "Copy",
        "inputs": [
            "name": "InputBlobTable"
        "outputs": [
            "name": "OutputSQLTable"
        "typeProperties": {
          "source": {
            "type": "BlobSource"
          "sink": {
            "type": "SqlSink"
          "executionLocation": "Japan East"          
        "Policy": {
          "concurrency": 1,
          "executionPriorityOrder": "NewestFirst",
          "retry": 0,
          "timeout": "01:00:00"
    "start": "2016-07-12T00:00:00Z",
    "end": "2016-07-13T00:00:00Z"

The schedule that is defined in the output dataset determines when the activity runs (for example: daily, frequency as day, and interval as 1). The activity copies data from an input dataset (source) to an output dataset (sink).

You can specify more than one input dataset to Copy Activity. They are used to verify the dependencies before the activity is run. However, only the data from the first dataset is copied to the destination dataset. For more information, see Scheduling and execution.

Performance and tuning

See the Copy Activity performance and tuning guide, which describes key factors that affect the performance of data movement (Copy Activity) in Azure Data Factory. It also lists the observed performance during internal testing and discusses various ways to optimize the performance of Copy Activity.

Fault tolerance

By default, copy activity will stop copying data and return failure when encounter incompatible data between source and sink; while you can explicitly configure to skip and log the incompatible rows and only copy those compatible data to make the copy succeeded. See the Copy Activity fault tolerance on more details.

Security considerations

See the Security considerations, which describes security infrastructure that data movement services in Azure Data Factory use to secure your data.

Scheduling and sequential copy

See Scheduling and execution for detailed information about how scheduling and execution works in Data Factory. It is possible to run multiple copy operations one after another in a sequential/ordered manner. See the Copy sequentially section.

Type conversions

Different data stores have different native type systems. Copy Activity performs automatic type conversions from source types to sink types with the following two-step approach:

  1. Convert from native source types to a .NET type.
  2. Convert from a .NET type to a native sink type.

The mapping from a native type system to a .NET type for a data store is in the respective data store article. (Click the specific link in the Supported data stores table). You can use these mappings to determine appropriate types while creating your tables, so that Copy Activity performs the right conversions.

Next steps