# Querying metadata tables with Amazon analytics services
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You can query your S3 managed metadata tables with Amazon analytics services such as Amazon Athena, Amazon Redshift, and Amazon EMR.

Before you can run queries, you must first [integrate the Amazon managed S3 table buckets](s3-tables-integrating-aws.md) in your Amazon Web Services account and Region with Amazon analytics services.

## Querying metadata tables with Amazon Athena
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After you [integrate your Amazon managed S3 table buckets](https://docs.amazonaws.cn/AmazonS3/latest/userguide/s3-tables-integrating-aws.html) with Amazon analytics services, you can start querying your metadata tables in Athena. In your queries, do the following: 
+ Specify your catalog as `s3tablescatalog/aws-s3` and your database as `b_general_purpose_bucket_name` (which is typically the namespace for your metadata tables). 
+ Make sure to surround your metadata table namespace names in quotation marks (`"`) or backticks (```), otherwise the query might not work.

For more information, see [Querying Amazon S3 tables with Athena](https://docs.amazonaws.cn/AmazonS3/latest/userguide/s3-tables-integrating-athena.html).

You can also run queries in Athena from the Amazon S3 console. 

### Using the S3 console and Amazon Athena
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The following procedure uses the Amazon S3 console to access the Athena query editor so that you can query a table with Amazon Athena. 

**To query a metadata table**

1. Sign in to the Amazon Web Services Management Console and open the Amazon S3 console at [https://console.amazonaws.cn/s3/](https://console.amazonaws.cn/s3/).

1. In the left navigation pane, choose **General purpose buckets**.

1. On the **General purpose buckets** tab, choose the bucket that contains the metadata configuration for the metadata table that you want to query.

1. On the bucket details page, choose the **Metadata** tab. 

1. Choose **Query table with Athena**, and then choose one of the sample queries for journal or inventory tables.

1. The Amazon Athena console opens and the Athena query editor appears with a sample query loaded for you. Modify this query as needed for your use case.

   In the query editor, the **Catalog** field should be populated with **s3tablescatalog/aws-s3**. The **Database** field should be populated with the namespace where your table is stored (for example, **b\$1*general-purpose-bucket-name***). 
**Note**  
If you don't see these values in the **Catalog** and **Database** fields, make sure that you've integrated your Amazon managed table bucket with Amazon analytics services in this Region. For more information, see [Integrating Amazon S3 Tables with Amazon analytics services](s3-tables-integrating-aws.md). 

1. To run the query, choose **Run**.
**Note**  
If you receive the error "Insufficient permissions to execute the query. Principal does not have any privilege on specified resource" when you try to run a query in Athena, you must be granted the necessary Lake Formation permissions on the table. For more information, see [Granting Lake Formation permission on a table or database](grant-permissions-tables.md#grant-lf-table).  
Also make sure that you have the appropriate Amazon Identity and Access Management (IAM) permissions to query metadata tables. For more information, see [Permissions for querying metadata tables](metadata-tables-bucket-query-permissions.md).
If you receive the error "Iceberg cannot access the requested resource" when you try to run the query, go to the Amazon Lake Formation console and make sure that you've granted yourself permissions on the table bucket catalog and database (namespace) that you created. Don't specify a table when granting these permissions. For more information, see [Granting Lake Formation permission on a table or database](grant-permissions-tables.md#grant-lf-table). 

## Querying metadata tables with Amazon Redshift
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After you [integrate your Amazon managed S3 table buckets](https://docs.amazonaws.cn/AmazonS3/latest/userguide/s3-tables-integrating-aws.html) with Amazon analytics services, do the following:
+ [Create a resource link](https://docs.amazonaws.cn/AmazonS3/latest/userguide/s3-tables-integrating-aws.html#database-link-tables) to your metadata table namespace (typically `b_general_purpose_bucket_name`). 
+ Make sure to surround your metadata table namespace names in quotation marks (`"`) or backticks (```), otherwise the query might not work. 

After that's done, you can start querying your metadata tables in the Amazon Redshift console. For more information, see [Accessing Amazon S3 tables with Amazon Redshift](https://docs.amazonaws.cn/AmazonS3/latest/userguide/s3-tables-integrating-redshift.html).

## Querying metadata tables with Amazon EMR
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To query your metadata tables by using Amazon EMR, you create an Amazon EMR cluster configured for Apache Iceberg and connect to your metadata tables using Apache Spark. You can set this up by integrating your Amazon managed S3 table buckets with Amazon analytics services or using the open-source Amazon S3 Tables Catalog for Iceberg client catalog.

**Note**  
When using Apache Spark on Amazon EMR or other third-party engines to query your metadata tables, we recommend that you use the Amazon S3 Tables Iceberg REST endpoint. Your query might not run successfully if you don't use this endpoint. For more information, see [Accessing tables using the Amazon S3 Tables Iceberg REST endpoint](s3-tables-integrating-open-source.md).

 For more information, see [Accessing Amazon S3 tables with Amazon EMR](https://docs.amazonaws.cn/AmazonS3/latest/userguide/s3-tables-integrating-emr.html).