Using ADLS

This section describes how you interact through the Designer Cloud Powered by Trifacta® platform with your ADLS environment.

• ADLS is a scalable file storage system for use across all of the nodes (servers) of an HDI cluster. Many interactions with ADLS are similar with desktop interactions with files and folders. However, what looks like a "file" or "folder" in ADLS may be spread across multiple nodes in the cluster. For more information, see https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-overview.

Uses of ADLS

The Designer Cloud Powered by Trifacta platform 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 Designer Cloud application, ADLS is accessed through the ADLS browser. See ADLS Browser.

Before You Begin Using ADLS

• Read/Write Access: Your HDI administrator must configure read/write permissions to locations in ADLS. Please see the ADLS documentation.

  Avoid using /trifacta/uploads for reading and writing data. This directory is used by the Designer Cloud application.

• Your HDI administrator should provide a place or mechanism for raw data to be uploaded to your HDI datastore. 

• Your HDI administrator should provide a writeable home output directory for you. This directory location is available through your user profile. See User Profile Page.

Secure Access

Depending on the security features you've enabled, the technical methods by which Alteryx users 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 Alteryx users should have a clear understanding of the folder structure within ADLS where each individual can read from and write their job results. 

• Users should know where shared data is located and where personal data can be saved without interfering with or confusing other users.

Reading from Sources in ADLS

You can create a dataset from one or more files stored in ADLS. When you select a folder in ADLS to create your dataset, you select all files in the folder to be included.

Notes:

• This option selects all files in all sub-folders. If your sub-folders contain separate datasets, you should be more specific in your folder selection.
• All files used in a single dataset must be of the same format and have the same structure. For example, you cannot mix and match CSV and JSON files if you are reading from a single directory. 
• When a folder is selected from ADLS, the following file types are ignored:
  
  • *_SUCCESS and *_FAILED files, which may be present if the folder has been populated by HDI.
  • If you have stored files in ADLS that begin with an underscore (_), these files cannot be read during batch transformation and are ignored. Please rename these files through ADLS so that they do not begin with an underscore. 

Creating Datasets

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

• ADLS sources are not moved or changed.
• Local file sources are uploaded to /trifacta/uploads where they remain and are not changed.

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.

• The ADLS location is available through the Export Results window for the job in the Jobs page. See Export Results Window.
• Each set of job results must be stored in a separate folder within your ADLS output home directory. 
• For more information on your output home directory, see User Profile Page.

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.

• If user mode is enabled, results are written to ADLS through the ADLS account configured for your use. Depending on the permissions of your ADLS account, you may be the only person who can access these results.

• If user mode is not enabled, then each Alteryx user writes results to ADLS using a shared account. Depending on the permissions of that account, your results may be visible to all platform users.

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.