This section describes how you interact through the  with your S3 environment.

• Simple Storage Service (S3) is an online data storage service provided by Amazon, which provides low-latency access through web services. For more information, see https://aws.amazon.com/s3/.

Uses of S3

The  can use S3 for the following tasks for reading and writing data:

1. Enabled S3 Integration: The  has been configured to integrate with your S3 instance. For more information, see Enable S3 Access.
2. Creating Datasets from S3 Files: You can read in source data stored in S3. An imported dataset may be a single S3 file or a folder of identically structured files. See Reading from Sources in S3 below.
3. Reading Datasets: When creating a dataset, you can pull your data from a source in S3. See Creating Datasets below.
4. Writing Job Results: After a job has been executed, you can write the results back to S3. See Writing Job Results below.

In the , S3 is accessed through the S3 browser. See S3 Browser.

Before You Begin Using S3

• Read/Write Access: If you are using system-wide permissions, your administrator must configure access parameters for S3 locations. If you are using per-user permissions, this requirement does not apply. See Enable S3 Access. 

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

• Your 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

Your administrator can grant access on a per-user basis or for the entire .

The  utilizes an S3 key and secret to access your S3 instance. These keys must enable read/write access to the appropriate directories in the S3 instance. 

For more information, see Enable S3 Access.

Storing Data in S3

Your administrator should provide raw data or locations and access for storing raw data within S3. All  should have a clear understanding of the folder structure within S3 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.
• The  stores the results of each job in a separate folder in S3.

Reading from Sources in S3

You can create an imported dataset from one or more files stored in S3.

When you select a folder in S3 to create your dataset, you select all files in the folder to be included. Notes:

• This option selects all files in all sub-folders and bundles them into a single dataset. If your sub-folders contain separate datasets, you should be more specific in your folder selection.
• All files used in a single imported 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 S3, the following file types are ignored:

• *_SUCCESS and *_FAILED files, which may be present if the folder has been populated by Hadoop.

Creating Datasets

When creating a dataset, you can choose to read data in from a source stored from S3 or local file. 

• S3 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 S3 above.
• In the Import Data page, click the S3 tab. See Import Data Page.

Writing Job Results

When you run a job, you can specify the S3 bucket and file path where the generated results are written. By default, the output is generated in your default bucket and default output home directory.

• Each set of job results must be stored in a separate folder within your S3 output home directory. 
• For more information on your output home directory, see User Profile Page.

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.