Create Dataset with Parameters

This section provides an overview on how to parameterize relational sources and files while importing data into the Alteryx Analytics Cloud.

From File System

When browsing for data on your default storage layer, you can choose to parameterize elements of the path. You can select path elements through the Import Data page, apply one of the supported parameter types, and then create the dataset with parameters.

Anmerkung

When you import a file, the data is not stored in the Alteryx Analytics Cloud . What you create is an imported dataset that is simply a reference to the data source. The Alteryx Analytics Cloud never stores or modifies source data.

When you create a dataset with parameters in the Alteryx Analytics Cloud, you can replace segments of the input path with parameters. Suppose you have the following files that you'd like to capture through a parameterized dataset:

//source/user/me/datasets/month01/2017-01-31-file.csv
//source/user/me/datasets/month02/2017-02-28-file.csv
//source/user/me/datasets/month03/2017-03-31-file.csv
//source/user/me/datasets/month04/2017-04-30-file.csv
//source/user/me/datasets/month05/2017-05-31-file.csv
//source/user/me/datasets/month06/2017-06-30-file.csv
//source/user/me/datasets/month07/2017-07-31-file.csv
//source/user/me/datasets/month08/2017-08-31-file.csv
//source/user/me/datasets/month09/2017-09-30-file.csv
//source/user/me/datasets/month10/2017-10-31-file.csv
//source/user/me/datasets/month11/2017-11-30-file.csv
//source/user/me/datasets/month12/2017-12-31-file.csv

A parameterized reference to all of these files would look something like:

//source/user/me/datasets/month##/YYYY-MM-DD-file.csv

Through the application, you can specify the parameters to match all values for:

## - You can use a wildcard or (better) a pattern to replace these values.
YYYY-MM-DD - A formatted Datetime parameter can replace these values.

For more information, see Parameterize Files for Import.

Parameterize bucket names

You can create environment parameters for your bucket names.

From Relational Sources

You can create datasets from a relational source by applying parameters to the custom SQL that pulls the data from the source. During the import of database tables through relational connections, you can apply parameters to the SQL query to define the imported dataset. In some scenarios, you may need to define the table to import using a variable parameter or to parameterize the time value associated with a table name. Using parameters, you can define the tables, columns, and conditions of the query to bring in data from a relational database.

For more information, see Parameterize Tables for Import.

Edit Parameter

After you have created your dataset with parameters, you can edit the parameter as needed.

Steps:

In the left nav bar, select Library for Data.
In the Library for Data page, locate the dataset. From its context menu, select either of the following:
1. Files: Select Edit parameters. In the Edit Dataset with Parameters, click the parameter to modify its definition.
2. Tables: Click Edit Custom SQL. You can modify the SQL statement in the Custom SQL window, including any parameters. For more information, see Create Dataset with SQL.

Apply Parameter Overrides

After you have created a parameterized dataset, you can apply overrides to the default value. These override values can be applied in the following order of precedence.

Job: When you choose to execute a job, you can set a new value for the parameter, which is applied for the specified job only.

Default: The default value for the parameter is used if no override is applied.

Delete Parameter