- 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 Alteryx Analytics Cloud can use S3 for the following tasks:
Enabled S3 Integration: The Alteryx Analytics Cloud has been configured to integrate with your S3 instance.
- 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.
- Reading Datasets: When creating a dataset, you can pull your data from a source in S3. See Creating Datasets below.
Writing Results: After a job has been executed, you can write the results back to S3.
In the Designer Cloud application , S3 is accessed through the S3 browser. See S3 Browser.
NOTE: When the Alteryx Analytics Cloud 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 S3
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.
/trifacta/uploadsfor reading and writing data. This directory is used by the Designer Cloud application .
Your administrator should provide a writeable home output directory for you. This directory location is available through your user profile. See Storage Config Page.
Your administrator can grant access on a per-user basis or for the entire Alteryx Analytics Cloud .
The Alteryx Analytics Cloud 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.
NOTE: If you disable or revoke your S3 access key, you must update the S3 keys for each user or for the entire system.
Storing Data in S3
Your administrator should provide raw data or locations and access for storing raw data within S3. All Trifacta users should have a clear understanding of the folder structure within S3 where each individual can read from and write results.
- Users should know where shared data is located and where personal data can be saved without interfering with or confusing other users.
- The Designer Cloud application stores the results of each job in a separate folder in S3.
NOTE: The Alteryx Analytics Cloud
does not modify source data in S3. Source data stored in S3 is read without modification from source locations, and source data uploaded to the Alteryx Analytics Cloud
is stored in
Reading from Sources in S3
You can create an imported dataset from one or more files stored in S3.
NOTE: Import of glaciered objects is not supported.
You can parameterize your input paths to import source files as part of the same imported dataset. For more information, see Overview of Parameterization.
When you select a folder in S3 to create your dataset, you select all files in the folder to be included.
- 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:
*_FAILEDfiles, which may be present if the folder has been populated by the running environment.
NOTE: If you have a folder and file with the same name in S3, search only retrieves the file. You can still navigate to locate the folder.
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/uploadswhere they remain and are not changed.
Data may be individual files or all of the files in a folder. In the Import Data page, click the S3 tab. See Import Data Page.
Tip: Users can create secondary connections to specific S3 buckets. For more information, see External S3 Connections.
Full execution on Snowflake :
For S3 data sources that are written to Snowflake , you may be able to execute the job in Snowflake .
- You must enable the Full execution for S3 file option and configure the Snowflake flow optimizations. For more information, see Flow Optimization Settings Dialog.
- Additional configuration and limitations may apply. For more information, see Snowflake Running Environment.
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 results must be stored in a separate folder within your S3 output home directory.
- For more information on your output home directory, see Storage Config Page.
append action is not supported when publishing to S3.
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