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Compute environments supported by Azure Data Factory | Microsoft Docs
Learn about compute environments that you can use in Azure Data Factory pipelines (such as Azure HDInsight) to transform or process data.
data-factory
douglaslMS
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conceptual
07/31/2018
douglasl

Compute environments supported by Azure Data Factory

This article explains different compute environments that you can use to process or transform data. It also provides details about different configurations (on-demand vs. bring your own) supported by Data Factory when configuring linked services linking these compute environments to an Azure data factory.

The following table provides a list of compute environments supported by Data Factory and the activities that can run on them.

Compute environment activities
On-demand HDInsight cluster or your own HDInsight cluster Hive, Pig, Spark, MapReduce, Hadoop Streaming
Azure Batch Custom
Azure Machine Learning Machine Learning activities: Batch Execution and Update Resource
Azure Data Lake Analytics Data Lake Analytics U-SQL
Azure SQL, Azure SQL Data Warehouse, SQL Server Stored Procedure
Azure Databricks Notebook, Jar, Python

On-demand compute environment

In this type of configuration, the computing environment is fully managed by the Azure Data Factory service. It is automatically created by the Data Factory service before a job is submitted to process data and removed when the job is completed. You can create a linked service for the on-demand compute environment, configure it, and control granular settings for job execution, cluster management, and bootstrapping actions.

Note

The on-demand configuration is currently supported only for Azure HDInsight clusters.

Azure HDInsight on-demand linked service

The Azure Data Factory service can automatically create an on-demand HDInsight cluster to process data. The cluster is created in the same region as the storage account (linkedServiceName property in the JSON) associated with the cluster. The storage account must be a general-purpose standard Azure storage account.

Note the following important points about on-demand HDInsight linked service:

  • The on-demand HDInsight cluster is created under your Azure subscription. You are able to see the cluster in your Azure portal when the cluster is up and running.
  • The logs for jobs that are run on an on-demand HDInsight cluster are copied to the storage account associated with the HDInsight cluster. The clusterUserName, clusterPassword, clusterSshUserName, clusterSshPassword defined in your linked service definition are used to log in to the cluster for in-depth troubleshooting during the lifecycle of the cluster.
  • You are charged only for the time when the HDInsight cluster is up and running jobs.
  • Script Action is now supported with the Azure HDInsight on-demand linked service.

Important

It typically takes 20 minutes or more to provision an Azure HDInsight cluster on demand.

Example

The following JSON defines a Linux-based on-demand HDInsight linked service. The Data Factory service automatically creates a Linux-based HDInsight cluster to process the required activity.

{
  "name": "HDInsightOnDemandLinkedService",
  "properties": {
    "type": "HDInsightOnDemand",
    "typeProperties": {
      "clusterType": "hadoop",
      "clusterSize": 1,
      "timeToLive": "00:15:00",
      "hostSubscriptionId": "<subscription ID>",
      "servicePrincipalId": "<service principal ID>",
      "servicePrincipalKey": {
        "value": "<service principal key>",
        "type": "SecureString"
      },
      "tenant": "<tenent id>",
      "clusterResourceGroup": "<resource group name>",
      "version": "3.6",
      "osType": "Linux",
      "linkedServiceName": {
        "referenceName": "AzureStorageLinkedService",
        "type": "LinkedServiceReference"
      }
    },
    "connectVia": {
      "referenceName": "<name of Integration Runtime>",
      "type": "IntegrationRuntimeReference"
    }
  }
}

Important

The HDInsight cluster creates a default container in the blob storage you specified in the JSON (linkedServiceName). HDInsight does not delete this container when the cluster is deleted. This behavior is by design. With on-demand HDInsight linked service, a HDInsight cluster is created every time a slice needs to be processed unless there is an existing live cluster (timeToLive) and is deleted when the processing is done.

As more activity runs, you see many containers in your Azure blob storage. If you do not need them for troubleshooting of the jobs, you may want to delete them to reduce the storage cost. The names of these containers follow a pattern: adf**yourdatafactoryname**-**linkedservicename**-datetimestamp. Use tools such as Microsoft Storage Explorer to delete containers in your Azure blob storage.

Properties

Property Description Required
type The type property should be set to HDInsightOnDemand. Yes
clusterSize Number of worker/data nodes in the cluster. The HDInsight cluster is created with 2 head nodes along with the number of worker nodes you specify for this property. The nodes are of size Standard_D3 that has 4 cores, so a 4 worker node cluster takes 24 cores (4*4 = 16 cores for worker nodes, plus 2*4 = 8 cores for head nodes). See Set up clusters in HDInsight with Hadoop, Spark, Kafka, and more for details. Yes
linkedServiceName Azure Storage linked service to be used by the on-demand cluster for storing and processing data. The HDInsight cluster is created in the same region as this Azure Storage account. Azure HDInsight has limitation on the total number of cores you can use in each Azure region it supports. Make sure you have enough core quotas in that Azure region to meet the required clusterSize. For details, refer to Set up clusters in HDInsight with Hadoop, Spark, Kafka, and more

Currently, you cannot create an on-demand HDInsight cluster that uses an Azure Data Lake Store as the storage. If you want to store the result data from HDInsight processing in an Azure Data Lake Store, use a Copy Activity to copy the data from the Azure Blob Storage to the Azure Data Lake Store.

Yes
clusterResourceGroup The HDInsight cluster is created in this resource group. Yes
timetolive The allowed idle time for the on-demand HDInsight cluster. Specifies how long the on-demand HDInsight cluster stays alive after completion of an activity run if there are no other active jobs in the cluster. The minimal allowed value is 5 minutes (00:05:00).

For example, if an activity run takes 6 minutes and timetolive is set to 5 minutes, the cluster stays alive for 5 minutes after the 6 minutes of processing the activity run. If another activity run is executed with the 6-minutes window, it is processed by the same cluster.

Creating an on-demand HDInsight cluster is an expensive operation (could take a while), so use this setting as needed to improve performance of a data factory by reusing an on-demand HDInsight cluster.

If you set timetolive value to 0, the cluster is deleted as soon as the activity run completes. Whereas, if you set a high value, the cluster may stay idle for you to log on for some troubleshooting purpose but it could result in high costs. Therefore, it is important that you set the appropriate value based on your needs.

If the timetolive property value is appropriately set, multiple pipelines can share the instance of the on-demand HDInsight cluster.		 Yes
clusterType The type of the HDInsight cluster to be created. Allowed values are "hadoop" and "spark". If not specified, default value is hadoop. Enterprise Security Package enabled cluster is currently not supported No
version Version of the HDInsight cluster. If not specified, it's using the current HDInsight defined default version. No
hostSubscriptionId The Azure subscription ID used to create HDInsight cluster. If not specified, it uses the Subscription ID of your Azure login context. No
clusterNamePrefix The prefix of HDI cluster name, a timestamp will be automatically appended at the end of the cluster name No
sparkVersion The version of spark if the cluster type is "Spark" No
additionalLinkedServiceNames Specifies additional storage accounts for the HDInsight linked service so that the Data Factory service can register them on your behalf. These storage accounts must be in the same region as the HDInsight cluster, which is created in the same region as the storage account specified by linkedServiceName. No
osType Type of operating system. Allowed values are: Linux and Windows (for HDInsight 3.3 only). Default is Linux. No
hcatalogLinkedServiceName The name of Azure SQL linked service that point to the HCatalog database. The on-demand HDInsight cluster is created by using the Azure SQL database as the metastore. No
connectVia The Integration Runtime to be used to dispatch the activities to this HDInsight linked service. For on-demand HDInsight linked service, it only supports Azure Integration Runtime. If not specified, it uses the default Azure Integration Runtime. No
clusterUserName The username to access the cluster. No
clusterPassword The password in type of secure string to access the cluster. No
clusterSshUserName The username to SSH remotely connect to cluster’s node (for Linux). No
clusterSshPassword The password in type of secure string to SSH remotely connect cluster’s node (for Linux). No

Important

HDInsight supports multiple Hadoop cluster versions that can be deployed. Each version choice creates a specific version of the Hortonworks Data Platform (HDP) distribution and a set of components that are contained within that distribution. The list of supported HDInsight versions keeps being updated to provide latest Hadoop ecosystem components and fixes. Make sure you always refer to latest information of Supported HDInsight version and OS Type to ensure you are using supported version of HDInsight.

[!IMPORTANT] Currently, HDInsight linked services does not support HBase, Interactive Query (Hive LLAP), Storm and Enterprise Security Enabled (domain-joined) clusters.

additionalLinkedServiceNames JSON example

"additionalLinkedServiceNames": [{
    "referenceName": "MyStorageLinkedService2",
    "type": "LinkedServiceReference"          
}]

Service principal authentication

The On-Demand HDInsight linked service requires a service principal authentication to create HDInsight clusters on your behalf. To use service principal authentication, register an application entity in Azure Active Directory (Azure AD) and grant it the Contributor role of the subscription or the resource group in which the HDInsight cluster is created. For detailed steps, see Use portal to create an Azure Active Directory application and service principal that can access resources. Make note of the following values, which you use to define the linked service:

  • Application ID
  • Application key
  • Tenant ID

Use service principal authentication by specifying the following properties:

Property Description Required
servicePrincipalId Specify the application's client ID. Yes
servicePrincipalKey Specify the application's key. Yes
tenant Specify the tenant information (domain name or tenant ID) under which your application resides. You can retrieve it by hovering the mouse in the upper-right corner of the Azure portal. Yes

Advanced Properties

You can also specify the following properties for the granular configuration of the on-demand HDInsight cluster.

Property Description Required
coreConfiguration Specifies the core configuration parameters (as in core-site.xml) for the HDInsight cluster to be created. No
hBaseConfiguration Specifies the HBase configuration parameters (hbase-site.xml) for the HDInsight cluster. No
hdfsConfiguration Specifies the HDFS configuration parameters (hdfs-site.xml) for the HDInsight cluster. No
hiveConfiguration Specifies the hive configuration parameters (hive-site.xml) for the HDInsight cluster. No
mapReduceConfiguration Specifies the MapReduce configuration parameters (mapred-site.xml) for the HDInsight cluster. No
oozieConfiguration Specifies the Oozie configuration parameters (oozie-site.xml) for the HDInsight cluster. No
stormConfiguration Specifies the Storm configuration parameters (storm-site.xml) for the HDInsight cluster. No
yarnConfiguration Specifies the Yarn configuration parameters (yarn-site.xml) for the HDInsight cluster. No

Example – On-demand HDInsight cluster configuration with advanced properties

{
    "name": " HDInsightOnDemandLinkedService",
    "properties": {
      "type": "HDInsightOnDemand",
      "typeProperties": {
          "clusterSize": 16,
          "timeToLive": "01:30:00",
          "hostSubscriptionId": "<subscription ID>",
          "servicePrincipalId": "<service principal ID>",
          "servicePrincipalKey": {
            "value": "<service principal key>",
            "type": "SecureString"
          },
          "tenant": "<tenent id>",
          "clusterResourceGroup": "<resource group name>",
          "version": "3.6",
          "osType": "Linux",
          "linkedServiceName": {
              "referenceName": "AzureStorageLinkedService",
              "type": "LinkedServiceReference"
            },
            "coreConfiguration": {
                "templeton.mapper.memory.mb": "5000"
            },
            "hiveConfiguration": {
                "templeton.mapper.memory.mb": "5000"
            },
            "mapReduceConfiguration": {
                "mapreduce.reduce.java.opts": "-Xmx4000m",
                "mapreduce.map.java.opts": "-Xmx4000m",
                "mapreduce.map.memory.mb": "5000",
                "mapreduce.reduce.memory.mb": "5000",
                "mapreduce.job.reduce.slowstart.completedmaps": "0.8"
            },
            "yarnConfiguration": {
                "yarn.app.mapreduce.am.resource.mb": "5000",
                "mapreduce.map.memory.mb": "5000"
            },
            "additionalLinkedServiceNames": [{
                "referenceName": "MyStorageLinkedService2",
                "type": "LinkedServiceReference"          
            }]
        }
    },
      "connectVia": {
      "referenceName": "<name of Integration Runtime>",
      "type": "IntegrationRuntimeReference"
    }
}

Node sizes

You can specify the sizes of head, data, and zookeeper nodes using the following properties:

Property Description Required
headNodeSize Specifies the size of the head node. The default value is: Standard_D3. See the Specifying node sizes section for details. No
dataNodeSize Specifies the size of the data node. The default value is: Standard_D3. No
zookeeperNodeSize Specifies the size of the Zoo Keeper node. The default value is: Standard_D3. No

Specifying node sizes

See the Sizes of Virtual Machines article for string values you need to specify for the properties mentioned in the previous section. The values need to conform to the CMDLETs & APIS referenced in the article. As you can see in the article, the data node of Large (default) size has 7-GB memory, which may not be good enough for your scenario.

If you want to create D4 sized head nodes and worker nodes, specify Standard_D4 as the value for headNodeSize and dataNodeSize properties.

"headNodeSize": "Standard_D4",    
"dataNodeSize": "Standard_D4",

If you specify a wrong value for these properties, you may receive the following error: Failed to create cluster. Exception: Unable to complete the cluster create operation. Operation failed with code '400'. Cluster left behind state: 'Error'. Message: 'PreClusterCreationValidationFailure'. When you receive this error, ensure that you are using the CMDLET & APIS name from the table in the Sizes of Virtual Machines article.

Bring your own compute environment

In this type of configuration, users can register an already existing computing environment as a linked service in Data Factory. The computing environment is managed by the user and the Data Factory service uses it to execute the activities.

This type of configuration is supported for the following compute environments:

  • Azure HDInsight
  • Azure Batch
  • Azure Machine Learning
  • Azure Data Lake Analytics
  • Azure SQL DB, Azure SQL DW, SQL Server

Azure HDInsight linked service

You can create an Azure HDInsight linked service to register your own HDInsight cluster with Data Factory.

Example

{
    "name": "HDInsightLinkedService",
    "properties": {
      "type": "HDInsight",
      "typeProperties": {
        "clusterUri": " https://<hdinsightclustername>.azurehdinsight.net/",
        "userName": "username",
        "password": {
            "value": "passwordvalue",
            "type": "SecureString"
          },
        "linkedServiceName": {
              "referenceName": "AzureStorageLinkedService",
              "type": "LinkedServiceReference"
        }
      },
      "connectVia": {
        "referenceName": "<name of Integration Runtime>",
        "type": "IntegrationRuntimeReference"
      }
    }
  }

Properties

Property Description Required
type The type property should be set to HDInsight. Yes
clusterUri The URI of the HDInsight cluster. Yes
username Specify the name of the user to be used to connect to an existing HDInsight cluster. Yes
password Specify password for the user account. Yes
linkedServiceName Name of the Azure Storage linked service that refers to the Azure blob storage used by the HDInsight cluster.

Currently, you cannot specify an Azure Data Lake Store linked service for this property. If the HDInsight cluster has access to the Data Lake Store, you may access data in the Azure Data Lake Store from Hive/Pig scripts.

Yes
connectVia The Integration Runtime to be used to dispatch the activities to this linked service. You can use Azure Integration Runtime or Self-hosted Integration Runtime. If not specified, it uses the default Azure Integration Runtime. No

Important

HDInsight supports multiple Hadoop cluster versions that can be deployed. Each version choice creates a specific version of the Hortonworks Data Platform (HDP) distribution and a set of components that are contained within that distribution. The list of supported HDInsight versions keeps being updated to provide latest Hadoop ecosystem components and fixes. Make sure you always refer to latest information of Supported HDInsight version and OS Type to ensure you are using supported version of HDInsight.

[!IMPORTANT] Currently, HDInsight linked services does not support HBase, Interactive Query (Hive LLAP), Storm and Enterprise Security Enabled (domain-joined) clusters.

Azure Batch linked service

You can create an Azure Batch linked service to register a Batch pool of virtual machines (VMs) to a data factory. You can run Custom activity using Azure Batch.

See following topics if you are new to Azure Batch service:

Example

{
    "name": "AzureBatchLinkedService",
    "properties": {
      "type": "AzureBatch",
      "typeProperties": {
        "accountName": "batchaccount",
        "accessKey": {
          "type": "SecureString",
          "value": "access key"
        },
        "batchUri": "https://batchaccount.region.batch.azure.com",
        "poolName": "poolname",
        "linkedServiceName": {
          "referenceName": "StorageLinkedService",
          "type": "LinkedServiceReference"
        }
      },
      "connectVia": {
        "referenceName": "<name of Integration Runtime>",
        "type": "IntegrationRuntimeReference"
      }
    }
  }

Properties

Property Description Required
type The type property should be set to AzureBatch. Yes
accountName Name of the Azure Batch account. Yes
accessKey Access key for the Azure Batch account. Yes
batchUri URL to your Azure Batch account, in format of https://batchaccountname.region.batch.azure.com. Yes
poolName Name of the pool of virtual machines. Yes
linkedServiceName Name of the Azure Storage linked service associated with this Azure Batch linked service. This linked service is used for staging files required to run the activity. Yes
connectVia The Integration Runtime to be used to dispatch the activities to this linked service. You can use Azure Integration Runtime or Self-hosted Integration Runtime. If not specified, it uses the default Azure Integration Runtime. No

Azure Machine Learning linked service

You create an Azure Machine Learning linked service to register a Machine Learning batch scoring endpoint to a data factory.

Example

{
    "name": "AzureMLLinkedService",
    "properties": {
      "type": "AzureML",
      "typeProperties": {
        "mlEndpoint": "https://[batch scoring endpoint]/jobs",
        "apiKey": {
            "type": "SecureString",
            "value": "access key"
        }
     },
     "connectVia": {
        "referenceName": "<name of Integration Runtime>",
        "type": "IntegrationRuntimeReference"
      }
    }
}

Properties

Property Description Required
Type The type property should be set to: AzureML. Yes
mlEndpoint The batch scoring URL. Yes
apiKey The published workspace model’s API. Yes
updateResourceEndpoint The Update Resource URL for an Azure ML Web Service endpoint used to update the predictive Web Service with trained model file No
servicePrincipalId Specify the application's client ID. Required if updateResourceEndpoint is specified
servicePrincipalKey Specify the application's key. Required if updateResourceEndpoint is specified
tenant Specify the tenant information (domain name or tenant ID) under which your application resides. You can retrieve it by hovering the mouse in the upper-right corner of the Azure portal. Required if updateResourceEndpoint is specified
connectVia The Integration Runtime to be used to dispatch the activities to this linked service. You can use Azure Integration Runtime or Self-hosted Integration Runtime. If not specified, it uses the default Azure Integration Runtime. No

Azure Data Lake Analytics linked service

You create an Azure Data Lake Analytics linked service to link an Azure Data Lake Analytics compute service to an Azure data factory. The Data Lake Analytics U-SQL activity in the pipeline refers to this linked service.

Example

{
    "name": "AzureDataLakeAnalyticsLinkedService",
    "properties": {
        "type": "AzureDataLakeAnalytics",
        "typeProperties": {
            "accountName": "adftestaccount",
            "dataLakeAnalyticsUri": "azuredatalakeanalytics URI",
            "servicePrincipalId": "service principal id",
            "servicePrincipalKey": {
                "value": "service principal key",
                "type": "SecureString"
            },
            "tenant": "tenant ID",
            "subscriptionId": "<optional, subscription id of ADLA>",
            "resourceGroupName": "<optional, resource group name of ADLA>"
        },
        "connectVia": {
            "referenceName": "<name of Integration Runtime>",
            "type": "IntegrationRuntimeReference"
        }
    }
}

Properties

Property Description Required
type The type property should be set to: AzureDataLakeAnalytics. Yes
accountName Azure Data Lake Analytics Account Name. Yes
dataLakeAnalyticsUri Azure Data Lake Analytics URI. No
subscriptionId Azure subscription id No
resourceGroupName Azure resource group name No
servicePrincipalId Specify the application's client ID. Yes
servicePrincipalKey Specify the application's key. Yes
tenant Specify the tenant information (domain name or tenant ID) under which your application resides. You can retrieve it by hovering the mouse in the upper-right corner of the Azure portal. Yes
connectVia The Integration Runtime to be used to dispatch the activities to this linked service. You can use Azure Integration Runtime or Self-hosted Integration Runtime. If not specified, it uses the default Azure Integration Runtime. No

Azure Databricks linked service

You can create Azure Databricks linked service to register Databricks workspace that you will use to run the Databricks workloads(notebooks).

Example - Using new job cluster in Databricks

{
    "name": "AzureDatabricks_LS",
    "properties": {
        "type": "AzureDatabricks",
        "typeProperties": {
            "domain": "https://eastus.azuredatabricks.net",
            "newClusterNodeType": "Standard_D3_v2",
            "newClusterNumOfWorker": "1:10",
            "newClusterVersion": "4.0.x-scala2.11",
            "accessToken": {
                "type": "SecureString",
                "value": "dapif33c9c721144c3a790b35000b57f7124f"
            }
        }
    }
}

Example - Using existing Interactive cluster in Databricks

{
    "name": " AzureDataBricksLinedService",
    "properties": {
      "type": " AzureDatabricks",
      "typeProperties": {
        "domain": "https://westeurope.azuredatabricks.net",
        "accessToken": {
            "type": "SecureString", 
            "value": "dapif33c9c72344c3a790b35000b57f7124f"
          },
        "existingClusterId": "{clusterId}"
        }
}

Properties

Property Description Required
name Name of the Linked Service Yes
type The type property should be set to: AzureDatabricks. Yes
domain Specify the Azure Region accordingly based on the region of the Databricks workspace. Example: https://eastus.azuredatabricks.net Yes
accessToken Access token is required for Data Factory to authenticate to Azure Databricks. Access token needs to be generated from the databricks workspace. More detailed steps to find the access token can be found here Yes
existingClusterId Cluster ID of an existing cluster to run all jobs on this. This should be an already created Interactive Cluster. You may need to manually restart the cluster if it stops responding. Databricks suggest running jobs on new clusters for greater reliability. You can find the Cluster ID of an Interactive Cluster on Databricks workspace -> Clusters -> Interactive Cluster Name -> Configuration -> Tags. More details No
newClusterVersion The Spark version of the cluster. It will create a job cluster in databricks. No
newClusterNumOfWorker Number of worker nodes that this cluster should have. A cluster has one Spark Driver and num_workers Executors for a total of num_workers + 1 Spark nodes. A string formatted Int32, like “1” means numOfWorker is 1 or “1:10” means auto-scale from 1 as min and 10 as max. No
newClusterNodeType This field encodes, through a single value, the resources available to each of the Spark nodes in this cluster. For example, the Spark nodes can be provisioned and optimized for memory or compute intensive workloads This field is required for new cluster No
newClusterSparkConf a set of optional, user-specified Spark configuration key-value pairs. Users can also pass in a string of extra JVM options to the driver and the executors via spark.driver.extraJavaOptions and spark.executor.extraJavaOptions respectively. No

Azure SQL Database linked service

You create an Azure SQL linked service and use it with the Stored Procedure Activity to invoke a stored procedure from a Data Factory pipeline. See Azure SQL Connector article for details about this linked service.

Azure SQL Data Warehouse linked service

You create an Azure SQL Data Warehouse linked service and use it with the Stored Procedure Activity to invoke a stored procedure from a Data Factory pipeline. See Azure SQL Data Warehouse Connector article for details about this linked service.

SQL Server linked service

You create a SQL Server linked service and use it with the Stored Procedure Activity to invoke a stored procedure from a Data Factory pipeline. See SQL Server connector article for details about this linked service.

Next steps

For a list of the transformation activities supported by Azure Data Factory, see Transform data.