# Creating a configured table – Amazon S3 data source
<a name="create-config-table-s3"></a>

In this procedure, the [member](glossary.md#glossary-member) does the following tasks: 
+  Configures an existing AWS Glue table for use in AWS Clean Rooms. (This step can be done before or after joining a collaboration, unless using Cryptographic Computing for Clean Rooms.)
**Note**  
AWS Clean Rooms supports AWS Glue tables. For more information about getting your data in AWS Glue, see [Step 3: Upload your data table to Amazon S3](prepare-data-S3.md#upload-to-s3). 
+ Names the [configured table](glossary.md#glossary-configured-table) and chooses which columns to use in the collaboration.

The following procedure assumes that:
+ The collaboration member has already [uploaded their data tables to Amazon S3](prepare-data-S3.md#upload-to-s3) and [created an AWS Glue table](prepare-data-S3.md#create-glue-crawler).
**Note**  
The **Results destination in Amazon S3** can't be within the same S3 bucket as any data source.
+ (Optional) For [encrypted](glossary.md#glossary-encryption) data tables only, the collaboration member has already [prepared encrypted data tables](prepare-encrypted-data.md) using the C3R encryption client.

You can use the statistic generation provided by AWS Glue to compute column-level statistics for AWS Glue Data Catalog tables. After AWS Glue generates statistics for tables in the Data Catalog, Amazon Redshift Spectrum automatically uses those statistics to optimize the query plan. For more information about computing column-level statistics using AWS Glue, see [Optimizing query performance using column statistics](https://docs.aws.amazon.com/glue/latest/dg/column-statistics.html) in the *AWS Glue User Guide*. For more information about AWS Glue, see the *[AWS Glue Developer Guide](https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html)*.

**To create a configured table – Amazon S3 data source**

1. Sign in to the AWS Management Console and open the AWS Clean Rooms console at [https://console.aws.amazon.com/cleanrooms](https://console.aws.amazon.com/cleanrooms/home).

1. In the left navigation pane, choose **Tables**.

1. In the upper right corner, choose **Configure new table**.

1. For **Data source**, under **AWS data sources**, choose **Amazon S3**. 

1. Under **Amazon S3 table**: 

   1. Select the **Region** where the S3 table is hosted.

      By default, the current Region (such as N. Virginia us-east-1) is selected. 
**Warning**  
When your Amazon S3 data source is in a different Region than your processing location, data processing may occur temporarily outside the source Region. Before proceeding, verify that cross-Region data movement complies with your data sovereignty requirements, regulatory compliance policies, and data governance standards. 

      For more information about Regions, see [Regions and Endpoints](https://docs.aws.amazon.com/general/latest/gr/rande.html) in the *AWS General Reference*. 

   1. Choose the **Database** from the dropdown list.

   1. Choose the **Table** that you want to configure from the dropdown list.
**Note**  
To verify that this is the correct table, do either one of the following:  
Choose **View in AWS Glue**.
Turn on **View schema from AWS Glue** to view the schema.
**Important**  
For AWS Glue tables where the data is in CSV format, the column names and order in the Glue schema must exactly match the CSV data. If they don't align, the allowed columns list for the configured table might not be enforced properly.

1. For **Columns and analysis methods allowed in collaborations**, 

   1. For **Which columns do you want to allow in collaborations?**
      + Choose **All columns** to allow all columns to be queried in the collaboration.
      + Choose **Custom list** to allow one or more columns from the **Specify allowed columns** dropdown list to be queried in the collaboration.

   1. For **Allowed analysis methods**,

      1. Choose **Direct query** to allow SQL queries to be run directly on this table

      1. Choose **Direct job** to allow PySpark jobs to be run directly on this table.  
**Example**  

   For example, if you want to allow collaboration members to run both direct SQL queries and PySpark jobs on all columns, then choose **All columns**, **Direct query**, and **Direct job**.

1. For **Configured table details**, 

   1. Enter a **Name** for the configured table.

      You can use the default name or rename this table.

   1. Enter a **Description** of the table. 

      The description helps differentiate between other configured tables with similar names.

1. If you want to enable **Tags** for the configured table resource, choose **Add new tag** and then enter the **Key** and **Value** pair. 

1. Choose **Configure new table**. 

Now that you have created a configured table, you are ready to: 
+ [Add an analysis rule to the configured table](add-analysis-rule.md)
+ [Associate the configured table to a collaboration](associate-configured-table.md)