This section describes how you interact through the  with your ADLS environment.

Uses of ADLS

The  can use ADLS for the following reading and writing tasks:

  1. Creating Datasets from ADLS Files: You can read in from a data source stored in ADLS. A source may be a single ADLS file or a folder of identically structured files. See Reading from Sources in ADLS below.
  2. Reading Datasets: When creating a dataset, you can pull your data from another dataset defined in ADLS. See Creating Datasets below.
  3. Writing Job Results: After a job has been executed, you can write the results back to ADLS. See Writing Job Results below.

In the , ADLS is accessed through the ADLS browser. See ADLS Browser.

NOTE: When the executes a job on a dataset, the source data is untouched. Results are written to a new location, so that no data is disturbed by the process.

Before You Begin Using ADLS

Secure Access

Depending on the security features you've enabled, the technical methods by which  access ADLS may vary. For more information, see Enable ADLS Access.

Storing Data in ADLS

Your HDI administrator should provide raw data or locations and access for storing raw data within ADLS. All  should have a clear understanding of the folder structure within ADLS where each individual can read from and write their job results. 

NOTE: The does not modify source data in ADLS. Sources stored in ADLS are read without modification from their source locations, and sources that are uploaded to the platform are stored in /trifacta/uploads.

Reading from Sources in ADLS

You can create a dataset from one or more files stored in ADLS.


You can parameterize your input paths to import source files as part of the same imported dataset. For more information, see Overview of Parameterization

Folder selection:

NOTE: Avoid including spaces in the paths to your ADLS sources. Spaces in the path value can cause errors during execution on Databricks.

When you select a folder in ADLS to create your dataset, you select all files in the folder to be included. Notes:

Creating Datasets

When creating a dataset, you can choose to read data in from a source stored from ADLS or from a local file.

Data may be individual files or all of the files in a folder. For more information, see Reading from Sources in ADLS above.

In the Import Data page, click the ADLS tab. See Import Data Page.

Writing Job Results

When your job results are generated, they can be stored back in ADLS for you at the location defined for your user account.

If your deployment is using ADLS, do not use the trifacta/uploads directory. This directory is used for storing uploads and metadata, which may be used by multiple users. Manipulating files outside of the can destroy other users' data. Please use the tools provided through the interface for managing uploads from ADLS.

Users can specify a default output home directory and, during job execution, an output directory for the current job.

Access to results:

Depending on how the platform is integrated with ADLS, other users may or may not be able to access your job results.

Creating a new dataset from results

As part of writing job results, you can choose to create a new dataset, so that you can chain together data wrangling tasks.

NOTE: When you create a new dataset as part of your job results, the file or files are written to the designated output location for your user account. Depending on how your HDI permissions are configured, this location may not be accessible to other users.