Below are instructions on how to configure  to point to S3.


NOTE: Please review the limitations before enabling. See Limitations of S3 Integration below.

Base Storage Layer


NOTE: Spark 2.3.0 jobs may fail on S3-based datasets due to a known incompatibility. For details, see

If you encounter this issue, please set spark.version to 2.1.0 in platform configuration. For more information, see Admin Settings Page.


Required AWS Account Permissions

For more information, see Required AWS Account Permissions.


Depending on your S3 environment, you can define:

Define base storage layer

The base storage layer is the default platform for storing results.

Required for:

The base storage layer for your is defined during initial installation and cannot be changed afterward.

If S3 is the base storage layer, you must also define the default storage bucket to use during initial installation, which cannot be changed at a later time. See Define default S3 write bucket below.

For more information on the various options for storage, see Storage Deployment Options.

For more information on setting the base storage layer, see Set Base Storage Layer.

Enable read access to S3

When read access is enabled,  can explore S3 buckets for creating datasets. 

NOTE: When read access is enabled, have automatic access to all buckets to which the specified S3 user has access. You may want to create a specific user account for S3 access.

NOTE: Data that is mirrored from one S3 bucket to another might inherit the permissions from the bucket where it is owned.


  1. Set the following property to enabled:

    Enable S3 Connectivity
  2. Save your changes.
  3. In the S3 configuration section, set enabled=true, which allows  to browse S3 buckets through the .
  4. Specify the AWS key and secret values for the user to access S3 storage.

Configure file storage protocols and locations

The  must be provided the list of protocols and locations for accessing S3. 


  1. Locate the following parameters and set their values according to the table below:

    "fileStorage.whitelist": ["s3"],
    "fileStorage.defaultBaseUris": ["s3:///"],

    A comma-separated list of protocols that are permitted to access S3.

    NOTE: The protocol identifier "s3" must be included in this list.


    For each supported protocol, this parameter must contain a top-level path to the location where files can be stored. These files include uploads, samples, and temporary storage used during job execution.

    NOTE: A separate base URI is required for each supported protocol. You may only have one base URI for each protocol.

    NOTE: For S3, three slashes at the end are required, as the third one is the end of the path value. This value is used as the base URI for all S3 connections created in .



    The above example is the most common example, as it is used as the base URI for all S3 connections that you create. Do not add a bucket value to the above URI.

  2. Save your changes and restart the platform.

Java VFS service

Use of SFTP connections requires the Java VFS service in the .

NOTE: This service is enabled by default.

For more information on configuring this service, see Configure Java VFS Service.

S3 access modes

The  supports the following modes for access S3. You must choose one access mode and then complete the related configuration steps.

NOTE: Avoid switching between user mode and system mode, which can disable user access to data. At install mode, you should choose your preferred mode.


System mode

(default) Access to S3 buckets is enabled and defined for all users of the platform. All users use the same AWS access key, secret, and default bucket.

System mode - read-only access

For read-only access, the key, secret, and default bucket must be specified in configuration.

NOTE: Please verify that the AWS account has all required permissions to access the S3 buckets in use. The account must have the ListAllMyBuckets ACL among its permissions.


  1. Locate the following parameters:

    aws.s3.keySet this value to the AWS key to use to access S3.
    aws.s3.secretSet this value to the secret corresponding to the AWS key provided.

    Set this value to the name of the S3 bucket from which users may read data.

    NOTE: Bucket names are not validated.

    NOTE: Additional buckets may be specified. See below.

  2. Save your changes.

User mode

Optionally, access to S3 can be defined on a per-user basis. This mode allows administrators to define access to specific buckets using various key/secret combinations as a means of controlling permissions.

NOTE: When this mode is enabled, individual users must have AWS configuration settings applied to their account, either by an administrator or by themselves. The global settings in this section do not apply in this mode.

To enable:

  1. Verify that the following setting has been set to enabled:

    Enable S3 Connectivity
  2. Please verify that the setting below has been configured:

    "aws.mode": "user",
  3. Additional configuration is required for per-user authentication. 
    1. You can choose to enable session tags to leverage your existing S3 permission scheme.

    2. For more information, see Configure AWS Per-User Authentication.

NOTE: If you have enabled user mode for S3 access, you must create and deploy an encryption key file. For more information, see Create Encryption Key File.

NOTE: If you have enabled user access mode, you can skip the following sections, which pertain to the system access mode, and jump to the Enable Redshift Connection section below.

System mode - additional configuration

The following sections apply only to system access mode.

Define default S3 write bucket

When S3 is defined as the base storage layer, write access to S3 is enabled. The  attempts to store outputs in the designated default S3 bucket. 

NOTE: This bucket must be set during initial installation. Modifying it at a later time is not recommended and can result in inaccessible data in the platform.

NOTE: Bucket names cannot have underscores in them. See


  1. Define S3 to be the base storage layer. See Set Base Storage Layer.
  2. Enable read access. See Enable read access.
  3. Specify a value for  which defines the S3 bucket where data is written. Do not include a protocol identifier. For example, if your bucket address is s3://MyOutputBucket, the value to specify is the following:


    NOTE: Bucket names are not validated.

    NOTE: Specify the top-level bucket name only. There should not be any backslashes in your entry.

NOTE: This bucket also appears as a read-access bucket if the specified S3 user has access.

Adding additional S3 buckets

When read access is enabled, all S3 buckets of which the specified user is the owner appear in the . You can also add additional S3 buckets from which to read.

NOTE: Additional buckets are accessible only if the specified S3 user has read privileges.

NOTE: Bucket names cannot have underscores in them.


  1. Locate the following parameter: aws.s3.extraBuckets:

    1. In the Admin Settings page, specify the extra buckets as a comma-separated string of additional S3 buckets that are available for storage. Do not put any quotes around the string. Whitespace between string values is ignored.

    2. In , specify the extraBuckets array as a comma-separated list of buckets as in the following: 

      "extraBuckets": ["MyExtraBucket01","MyExtraBucket02","MyExtraBucket03"]

      NOTE: Specify the top-level bucket name only. There should not be any backslashes in your entry.

      NOTE: Bucket names are not validated.

  2. Specify the extraBuckets array as a comma-separated list of buckets as in the following: 

    "extraBuckets": ["MyExtraBucket01","MyExtraBucket02","MyExtraBucket03"]
  3. These values are mapped to the following bucket addresses:


S3 Configuration

Configuration reference

Enable S3 ConnectivityWhen set to enabled, the S3 file browser is displayed in the GUI for locating files.

"aws.s3.key": "<AWS_KEY>",
"aws.s3.secret": "<AWS_SECRET>",

Set this value to the name of the S3 bucket to which you are writing.

  • When webapp.storageProtocol is set to s3, the output is delivered to

Access Key ID for the AWS account to use.

NOTE: This value cannot contain a slash (/).


Secret Access Key for the AWS account.


Add references to any additional S3 buckets to this comma-separated array of values.

The S3 user must have read access to these buckets.

Enable use of server-side encryption

You can configure the  to publish data on S3 when a server-side encryption policy is enabled. SSE-S3 and SSE-KMS methods are supported. For more information, see


To enable, please specify the following parameters.

Server-side encryption method

"aws.s3.serverSideEncryption": "none",

Set this value to the method of encryption used by the S3 server. Supported values:

NOTE: Lower case values are required.


Server-side KMS key identifier

When KMS encryption is enabled, you must specify the AWS KMS key ID to use for the server-side encryption.

"aws.s3.serverSideKmsKeyId": "",


The format for referencing this key is the following:


You can use an AWS alias in the following formats. The format of the AWS-managed alias is the following:


The format for a custom alias is the following:



<FSR> is the name of the alias for the entire key.

Save your changes and restart the platform.

Configure S3 filewriter

The following configuration can be applied through the Hadoop site-config.xml file. If your installation does not have a copy of this file, you can insert the properties listed in the steps below into to configure the behavior of the S3 filewriter.


  1. Locate the filewriter.hadoopConfig block, where you can insert the following Hadoop configuration properties:

    "filewriter": {
      max: 16,
      "hadoopConfig": {
        "fs.s3a.buffer.dir": "/tmp",
        "": "true"

    Specifies the temporary directory on the to use for buffering when uploading to S3. If is set to false, this parameter is unused.

    NOTE: This directory must be accessible to the Batch Job Runner process during job execution.

    Set to true to enable buffering in blocks.

    When set to false, buffering in blocks is disabled. For a given file, the entire object is buffered to the disk of the . Depending on the size and volume of your datasets, the node can run out of disk space.

  2. Save your changes and restart the platform.

Create Redshift Connection

For more information, see Amazon Redshift Connections.

Create Additional S3 Connections

Creating additional S3 connections is not required. After you define S3 as your base storage layer, you can create user-specific access to S3 buckets through  . For more information, see External S3 Connections.

Hadoop distribution-specific configuration


NOTE: If you are using Spark profiling through Hortonworks HDP on data stored in S3, additional configuration is required. See Configure for Hortonworks.

Additional Configuration for S3

The following parameters can be configured through the  to affect the integration with S3. You may or may not need to modify these values for your deployment.


This value should be the S3 endpoint DNS name value.

NOTE: Do not include the protocol identifier.

Example value:

If your S3 deployment is either of the following:

  • located in a region that does not support the default endpoint, or
  • v4-only signature is enabled in the region

Then, you can specify this setting to point to the S3 endpoint for Java/Spark services.

For more information on this location, see


Restart services. See Start and Stop the Platform.

Try running a simple job from the . For more information, see Verify Operations.


Profiling consistently fails for S3 sources of data

If you are executing visual profiles of datasets sourced from S3, you may see an error similar to the following in the batch-job-runner.log file:

01:19:52.297 [Job 3] ERROR com.trifacta.hadoopdata.joblaunch.server.BatchFileWriterWorker - BatchFileWriterException: Batch File Writer unknown error:
{jobId=3, why=bound must be positive}
01:19:52.298 [Job 3] INFO com.trifacta.hadoopdata.joblaunch.server.BatchFileWriterWorker - Notifying monitor for job 3 with status code FAILURE

This issue is caused by improperly configuring buffering when writing to S3 jobs. The specified local buffer cannot be accessed as part of the batch job running process, and the job fails to write results to S3.


You may do one  of the following:


Spark local directory has no space

During execution of a Spark job, you may encounter the following error:

org.apache.hadoop.util.DiskChecker$DiskErrorException: No space available in any of the local directories.