You can configure your instance of the  to integrate with Amazon Elastic MapReduce (EMR), a highly scalable Hadoop-based execution environment. 

NOTE: This section applies only to installations of where a license key file has been acquired from and applied to the platform.

Supported Versions

This section outlines how to create a new EMR cluster and integrate the  with it. The platform can be integrated with existing EMR clusters.

Supported Versions: EMR 5.6 - EMR 5.21

NOTE: EMR 5.20 - EMR 5.21 requires Spark 2.4. For more information, see Configure for Spark.


NOTE: Job cancellation is not supported on an EMR cluster.

Create EMR Cluster

Use the following section to set up your EMR cluster for use with the .

  • Via AWS EMR UI: This method is assumed in this documentation.
  • Via AWS command line interface: For this method, it is assumed that you know the required steps to perform the basic configuration. For custom configuration steps, additional documentation is provided below.

NOTE: It is recommended that you set up your cluster for exclusive use by the .

Cluster options

In the Amazon EMR console, click Create Cluster. Click Go to advanced options. Complete the sections listed below.

NOTE: Please be sure to read all of the cluster options before setting up your EMR cluster.

NOTE: Please perform your configuration through the Advanced Options workflow.

For more information on setting up your EMR cluster, see

Advanced Options

In the Advanced Options screen, please select the following:

  • Software Configuration:

    • Release: 5.15

    • Select:
      • Hadoop 2.8.3
      • Hue 3.12.0
      • Ganglia 3.7.2

        Tip: Although it is optional, Ganglia is recommended for monitoring cluster performance.

      • Spark 2.3.0

        NOTE: Additional configuration is required. You must apply the Spark version number in the spark.version property in Admin Settings. See Configure for Spark.

    • Deselect everything else.
  • Edit the software settings:
    • Copy and paste the following into Enter Configuration:

       "Classification": "capacity-scheduler",
       "Properties": {
       "yarn.scheduler.capacity.resource-calculator": "org.apache.hadoop.yarn.util.resource.DominantResourceCalculator"

  • Auto-terminate cluster after the last step is completed: Leave this option disabled.

Hardware configuration

NOTE: Please apply the sizing information for your EMR cluster that was recommended for you. If you have not done so, please contact your .

General Options

  • Cluster name: Provide a descriptive name.
  • Logging: Enable logging on the cluster. 
    • S3 folder: Please specify the S3 bucket and path to the logging folder.

      NOTE: Please verify that this location is read accessible to all users of the platform. See below for details.

  • Debugging: Enable.
  • Termination protection: Enable.
  • Tags:
    • No options required.
  • Additional Options:
    • EMRFS consistent view: You should enable this setting. Doing so may incur additional costs. For more information, see EMRFS consistent view is recommended below.
    • Custom AMI ID: None.
    • Bootstrap Actions:
      • If you are using a custom credential provider JAR, you must create a bootstrap action. 

        NOTE: This configuration must be completed before you create the EMR cluster. For more information, see Authentication below.

Security Options

  • EC2 key pair: Please select a key/pair to use if you wish to access EMR nodes via SSH. 
  • Permissions: Set to Custom to reduce the scope of permissions. For more information, see EMR cluster policies below.

    NOTE: Default permissions give access to everything in the cluster.

  • Encryption Options
    • No requirements.
  • EC2 Security Groups:

    • The selected security group for the master node on the cluster must allow TCP traffic from the on port 8088. For more information, see System Ports.

Create cluster and acquire cluster ID

If you performed all of the configuration, including the sections below, you can create the cluster.

NOTE: You must acquire your EMR cluster ID for use in configuration of the .

Specify cluster roles

The following cluster roles and their permissions are required. For more information on the specifics of these policies, see EMR cluster policies

  • EMR Role: 
    • Read/write access to log bucket
    • Read access to resource bucket
  • EC2 instance profile:
    • If using instance mode: 
      • EC2 profile should have read/write access for all users. 
      • EC2 profile should have same permissions as EC2 Edge node role. 
    • Read/write access to log bucket
    • Read access to resource bucket
  • Auto-scaling role:
    • Read/write access to log bucket
    • Read access to resource bucket
    • Standard auto-scaling permissions


You can use one of two methods for authenticating the EMR cluster:

  • Role-based IAM authentication (recommended): This method leverages your IAM roles on the EC2 instance. 
  • Custom credential provider JAR file: This method utilizes a JAR file provided with the platform. This JAR file must be deployed to all nodes on the EMR cluster through a bootstrap action script.

Role-based IAM authentication

You can leverage your IAM roles to provide role-based authentication to the S3 buckets.

NOTE: The IAM role that is assigned to the EMR cluster and to the EC2 instances on the cluster must have access to the data of all users on S3.

For more information, see Configure for EC2 Role-Based Authentication.

Specify the custom credential provider JAR file

If you are not using IAM roles for access, you can manage access using either of the following:

  • AWS key and secret values specified in 
  • AWS user mode

In either scenario, you must use the custom credential provider JAR provided in the installation. This JAR file must be available to all nodes of the EMR cluster.

After you have installed the platform and configured the S3 buckets, please complete the following steps to deploy this JAR file.

NOTE: These steps must be completed before you create the EMR cluster.

NOTE: This section applies if you are using the default credential provider mechanism for AWS and are not using the IAM instance-based role authentication mechanism.


  1. From the installation of the , retrieve the following file:


  2. Upload this JAR file to an S3 bucket location where the EMR cluster can access it:

    1. Via AWS Console S3 UI: See
    2. Via AWS command line:

      aws s3 cp trifacta-aws-emr-credential-provider.jar s3://<YOUR-BUCKET>/

  3. Create a bootstrap action script named The contents must be the following:

    sudo aws s3 cp s3://<YOUR-BUCKET>/trifacta-aws-emr-credential-provider.jar  /usr/share/aws/emr/emrfs/auxlib/

  4. This script must be uploaded into S3 in a location that can be accessed from the EMR cluster. Retain the full path to this location.
  5. Add bootstrap action to EMR cluster configuration.
    1. Via AWS Console S3 UI: Create the bootstrap action to point to the script you uploaded on S3.

    2. Via AWS command line: 
      1. Upload the file to the accessible S3 bucket.
      2. In the command line cluster creation script, add a custom bootstrap action, such as the following:

        --bootstrap-actions '[
        {"Path":"s3://<YOUR-BUCKET>/","Name":"Custom action"}

When the EMR cluster is launched with the above custom bootstrap action, the cluster does one of the following:

  • Interacts with S3 using the credentials specified in
  • if aws.mode = user, then the credentials registered by the user are used.

For more information about AWSCredentialsProvider for EMRFS please see:

EMRFS consistent view is recommended

Although it is not required, you should enable the consistent view feature for EMRFS on your cluster.

During job execution, including profiling jobs, on EMR, the  writes files in rapid succession, and these files are quickly read back from storage for further processing. However, Amazon S3 does not provide a guarantee of a consistent file listing until a later time.

To ensure that the  does not begin reading back an incomplete set of files, you should enable EMRFS consistent view. 

NOTE: If EMRFS consistent view is enabled, additional policies must be added for users and the EMR cluster. Details are below.

NOTE: If EMRFS consistent view is not enabled, profiling jobs may not get a consistent set of files at the time of execution. Jobs can fail or generate inconsistent results.

For more information on EMRFS consistent view, see


Amazon's DynamoDB is automatically enabled to store metadata for EMRFS consistent view.

NOTE: DynamoDB incurs costs while it is in use. For more information, see

NOTE: DynamoDB does not automatically purge metadata after a job completes. You should configure periodic purges of the database during off-peak hours.

Enable output job manifest

When EMRFS consistent view is enabled on the cluster, the platform must be configured to use it. During job execution, the platform can use consistent view to create a manifest file of all files generated during job execution. When the job results are published to an external target, this manifest file ensures proper publication.


  1. Locate the following parameter and set it to true:

    "feature.enableJobOutputManifest": true,

  2. Save your changes and restart the platform.

Set up S3 Buckets

Bucket setup

You must set up S3 buckets for read and write access. 

NOTE: Within the , you must enable use of S3 as the default storage layer. This configuration is described later.

For more information, see Enable S3 Access.

Set up EMR resources buckets

On the EMR cluster, all users of the platform must have access to the following locations:

LocationDescriptionRequired Access
EMR Resources bucket and path

The S3 bucket and path where resources can be stored by the for execution of Spark jobs on the cluster.

The locations are configured separately in the .

EMR Logs bucket and path

The S3 bucket and path where logs are written for cluster job execution.  


These locations are configured on the  later.

Access Policies

EC2 instance profile

 require the following policies to run jobs on the EMR cluster:

    "Statement": [
            "Effect": "Allow",
            "Action": [
            "Resource": [
            "Effect": "Allow",
            "Action": [
            "Resource": [


EMR roles

The following policies should be assigned to the EMR roles listed below for read/write access:

            "Effect": "Allow",
            "Action": [
            "Resource": [

EMRFS consistent view policies

If EMRFS consistent view is enabled, the following policy must be added for users and the EMR cluster permissions:

  "Version": "2012-10-17",
  "Statement": [
      "Action": [
      "Effect": "Allow",
      "Resource": [

Configure  for EMR

Please complete the following sections to configure the  to communicate with the EMR cluster.

Change admin password

As soon as you have installed the software, you should login to the application and change the admin password. The initial admin password is the instanceId for the EC2 instance. For more information, see Change Password.

Verify S3 as base storage layer

EMR integrations requires use of S3 as the base storage layer.

NOTE: The base storage layer must be set during initial installation and set up of the .

See Set Base Storage Layer.

Set up S3 integration

To integrate with S3, additional configuration is required. See Enable S3 Access.

Enable EMR integration

After you have configured S3 to be the base storage layer, you must enable EMR integration.


  1. Search for the following setting:

    "webapp.runInEMR": false,

  2. Set the above value to true
  3. Set the following value to false:

    "webapp.runInHadoop": false,

  4. Verify the following property values:

    "webapp.runInTrifactaServer": true,
    "webapp.runInEMR": true,
    "webapp.runInHadoop": false,
    "webapp.runInDataflow": false,

Apply EMR cluster ID

The  must be aware of the EMR cluster to which to connection. 


  1. Under External Service Settings, enter your AWS EMR Cluster ID. Click the Save button below the textbox.

For more information, see Admin Settings Page.

Extract IP address of master node in private sub-net

If you have deployed your EMR cluster on a private sub-net that is accessible outside of AWS, you must enable this property, which permits the extraction of the IP address of the master cluster node through DNS.

NOTE: This feature must be enabled if your EMR is accessible outside of AWS on a private network.


  1. Set the following property to true:

    "emr.extractIPFromDNS": false,

  2. Save your changes and restart the platform.

EMR Authentication for the 

Depending on the authentication method you used, you must set the following properties.

Authentication methodProperties and values

Use default credential provider for all including EMR.

NOTE: This method requires the deployment of a custom credential provider JAR.


Use default credential provider for all . However, EC2 role-based IAM authentication is used for EMR.


EC2 role-based IAM authentication for all


Configure Spark for EMR

For EMR, you can configure a set of Spark-related properties to manage the integration and its performance.

Configure Spark version

Depending on the version of EMR with which you are integrating, the must be modified to use the appropriate version of Spark to connect to EMR. For more information, see Configure for Spark.

Specify YARN queue for Spark jobs

Through the Admin Settings page, you can specify the YARN queue to which to submit your Spark jobs. All Spark jobs from the  are submitted to this queue.


  1. In platform configuration, locate the following:


  2. Specify the name of the queue. 
  3. Save your changes.

Allocation properties

The following properties must be passed from the  to Spark for proper execution on the EMR cluster. 

NOTE: Do not modify these properties through the Admin Settings page. These properties must be added as extra properties through the Spark configuration block. Ignore any references in to these properties and their settings.

"spark": { 
  "props": { 
    "spark.dynamicAllocation.enabled": "true",
    "spark.shuffle.service.enabled": "true", 
    "spark.executor.instances": "0", 
    "spark.executor.memory": "2048M", 
    "spark.executor.cores": "2",
    "spark.driver.maxResultSize": "0"

Enable dynamic allocation on the Spark cluster, which allows Spark to dynamically adjust the number of executors.true
Enable Spark shuffle service, which manages the shuffle data for jobs, instead of the executors.true
Default count of executor instances.See Sizing Guidelines.
Default memory allocation of executor instances.See Sizing Guidelines.
Default count of executor cores.See Sizing Guidelines.
spark.driver.maxResultSizeEnable serialized results of unlimited size by setting this parameter to zero (0).0

Default Hadoop job results format

For smaller datasets, the platform recommends using the  running environment.

For larger datasets, if the size information is unavailable, the platform recommends by default that you run the job on the Hadoop cluster. For these jobs, the default publishing action for the job is specified to run on the Hadoop cluster, generating the output format defined by this parameter. Publishing actions, including output format, can always be changed as part of the job specification. 

As needed, you can change this default format. 

"webapp.defaultHadoopFileFormat": "csv",

Accepted values: csvjsonavropqt

For more information, see Run Job Page.

Additional configuration for EMR

You can set the following parameters as needed:



S3 bucket name where , libraries, and other resources can be stored that are required for Spark execution.


S3 path within the bucket where resources can be stored for job execution on the EMR cluster.

NOTE: Do not include leading or trailing slashes for the path value.


This value defines the user for the to use for connecting to the cluster.

NOTE: Do not modify this value.

aws.emr.maxLogPollingRetriesNConfigure maximum number of retries when polling for log files from EMR after job success or failure. Minimum value is 5.

Defines the number of days that temporary files in the /trifacta/tempfiles directory on EMR HDFS are permitted to age.

By default, this value is set to 0, which means that cleanup is disabled.

If needed, you can set this to a positive integer value. During each job run, the platform scans this directory for temp files older than the specified number of days and removes any that are found. This cleanup provides an additional level of system hygiene.

Before enabling this secondary cleanup process, please execute the following command to clear the tempfiles directory:

hdfs dfs -rm -r -skipTrash /trifacta/tempfiles

Optional Configuration

Configure for Redshift

For more information on configuring the platform to integrate with Redshift, see Create Redshift Connections.

Switch EMR Cluster

If needed, you can switch to a different EMR cluster through the application. For example, if the original cluster suffers a prolonged outage, you can switch clusters by entering the cluster ID of a new cluster. For more information, see Admin Settings Page.

Configure Batch Job Runner

Batch Job Runner manages jobs executed on the EMR cluster. You can modify aspects of how jobs are executed and how logs are collected. For more information, see Configure Batch Job Runner.

Modify Job Tag Prefix

In environments where the EMR cluster is shared with other job-executing applications, you can review and specify the job tag prefix, which is prepended to job identifiers to avoid conflicts with other applications.


  1. Locate the following and modify if needed:

    "aws.emr.jobTagPrefix": "TRIFACTA_JOB_",

  2. Save your changes and restart the platform.


  1. Load a dataset from the EMR cluster.
  2. Perform a few simple steps on the dataset.
  3. Click Run Job in the Transformer page. 
  4. When specifying the job: 
    1. Click the Profile Results checkbox.
    2. Select Spark.
  5. When the job completes, verify that the results have been written to the appropriate location.