# Working with data sources in Amazon Quick Sight
Working with data sources

Use a data source to access an external data store. Amazon S3 data sources save the manifest file information. In contrast, Salesforce and database data sources save connection information like credentials. In such cases, you can easily create multiple datasets from the data store without having to re-enter information. Connection information isn't saved for text or Microsoft Excel files. 

**Topics**
+ [

# Creating a data source
](create-a-data-source.md)
+ [

# Editing a data source
](edit-a-data-source.md)
+ [

# Deleting a data source
](delete-a-data-source.md)

# Creating a data source



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|    Intended audience:  Amazon Quick authors  | 

As an analysis author in Amazon Quick, you don't need to understand anything about the infrastructure that you use to connect to your data. You set up a new data source only once. 

After a data source is set up, you can access it from its tile in the Quick console. You can use it to create one or more datasets. After a dataset is set up, you can also access the dataset from its tile. By abstracting away the technical details, Amazon Quick Sight simplifies data connections. 

**Note**  
You don't need to store connection settings for files that you plan to upload manually. For more information about file uploads, see [Creating datasets](creating-data-sets.md).

Before you begin adding a new data-source connection profile to Amazon Quick, first collect the information that you need to connect to the data source. In some cases, you might plan to copy and paste settings from a file. If so, make sure that the file doesn't contain formatting characters (list bullets or numbers) or blank space characters (spaces, tabs). Also make sure that the file doesn't contain nontext "gremlin" characters such as non-ASCII, null (ASCII 0), and control characters. 

The following list includes the information to collect the most commonly used settings:
+ The data source to connect to.

  Make sure that you know which source that you need to connect to for reporting. This source might be different than the source that stores, processes, or provides access to the data. 

  For example, let's say that you're a new analyst in a large company. You want to analyze data from your ordering system, which you know uses Oracle. However, you can't directly query the online transaction processing (OLTP) data. A subset of data is extracted and stored in a bucket on Amazon S3, but you don't have access to that either. Your new co-workers explain that they use AWS Glue crawlers to read the files and AWS Lake Formation to access them. With more research, you learn that you need to use an Amazon Athena query as your data source in Amazon Quick Sight. The point here is that it isn't always obvious which type of data source to choose.
+ A descriptive name for the new data source tile.

  Each new data source connection needs a unique and descriptive name. This name displays on the Amazon Quick Sight list of existing data sources, which is at the bottom of the **Create a Data Set** screen. Use a name that makes it easy to distinguish your data sources from other similar data sources. Your new Amazon Quick Sight data source profile displays both the database software logo and the custom name that you assign.
+ The name of the server or instance to connect to.

  A unique name or other identifier identifies the server connector of the data source on your network. The descriptors vary depending on which one you're connecting to, but it's usually one or more of the following: 
  + Hostname
  + IP address
  + Cluster ID
  + Instance ID
  + Connector
  + Site-based URL
+ The name of the collection of data that you want to use.

  The descriptor varies depending on the data source, but it's usually one of the following: 
  + Database
  + Warehouse
  + S3 bucket
  + Catalog
  + Schema

  In some cases, you might need to include a manifest file or a query. 
+ The user name that you want Amazon Quick Sight to use.

  Every time Amazon Quick Sight connects using this data source profile (tile), it uses the user name from the connection settings. In some cases, this might be your personal login. But if you're going to share this with other people, ask the system administrator about creating credentials to use for Amazon Quick Sight connections. 
+ What type of connection to use. You can choose a public network or a VPC connection. If you have more than one VPC connection available, identify which one to use to reach your source of data.
+ Additional settings, such as Secure Sockets Layer (SSL) or API tokens, are required by some data sources.

After you save the connection settings as a data source profile, you can create a dataset by selecting its tile. The connections are stored as data source connection profiles in Amazon Quick Sight. 

To view your existing connection profiles, open the Quick start page, choose **Data**, choose **Create**, and then choose **New Dataset**.

For a list of supported data source connections and examples, see [Connect to your data with integrations and datasets](connecting-to-data-examples.md).

After you create a data source in Quick Sight, you can [create a dataset](https://docs.aws.amazon.com/quicksuite/latest/userguide/creating-data-sets) in Quick Sight that contains data from the connected data source. You can also [update data source connection](https://docs.aws.amazon.com/quicksuite/latest/userguide/edit-a-data-source) information at any time.

# Editing a data source


You can edit an existing database data source to update the connection information, such as the server name or the user credentials. You can also edit an existing Amazon Athena data source to update the data source name. You can't edit Amazon S3 or Salesforce data sources.

## Editing a database data source


Use the following procedure to edit a database data source.

1. From the Quick start page, choose **Data** at left. Choose **Create** and then choose **New dataset**.

1. Choose a database data source.

1. Choose **Edit Data Source**.

1. Modify the data source information:
   + If you are editing an autodiscovered database data source, you can modify any of the following settings:
     + For **Data source name**, enter a name for the data source.
     + For **Instance ID**, choose the name of the instance or cluster that you want to connect to from the list provided.
     + **Database name** shows the default database for the **Instance ID** cluster or instance. If you want to use a different database on that cluster or instance, enter its name.
     + For **UserName**, enter the user name of a user account that has permissions to do the following: 
       + Access the target database. 
       + Read (perform a `SELECT` statement on) any tables in that database that you want to use.
     + For **Password**, enter the password for the account that you entered.
   + If you are editing an external database data source, you can modify any of the following settings:
     + For **Data source name**, enter a name for the data source.
     + For **Database server**, enter one of the following values:
       + For an Amazon Redshift cluster, enter the endpoint of the cluster without the port number. For example, if the endpoint value is `clustername.1234abcd.us-west-2.redshift.amazonaws.com:1234`, then enter `clustername.1234abcd.us-west-2.redshift.amazonaws.com`. You can get the endpoint value from the **Endpoint** field on the cluster detail page on the Amazon Redshift console.
       + For an Amazon EC2 instance of PostgreSQL, MySQL, or SQL Server, enter the public DNS address. You can get the public DNS value from the **Public DNS** field on the instance detail pane in the EC2 console.
       + For a non–Amazon EC2 instance of PostgreSQL, MySQL, or SQL Server, enter the hostname or public IP address of the database server.
     + For **Port**, enter the port that the cluster or instance uses for connections.
     + For **Database name**, enter the name of the database that you want to use.
     + For **UserName**, enter the user name of a user account that has permissions to do the following: 
       + Access the target database. 
       + Read (perform a `SELECT` statement on) any tables in that database that you want to use.
     + For **Password**, enter the password for the account that you entered.

1. Choose **Validate connection**.

1. If the connection validates, choose **Update data source**. If not, correct the connection information and try validating again.

1. If you want to create a new dataset using the updated data source, proceed with the instructions at [Creating a dataset from a database](create-a-database-data-set.md). Otherwise, close the **Choose your table** dialog box.

## Editing an Athena data source


Use the following procedure to edit an Athena data source.

1. From the Quick start page, choose **Data** at left. Choose **Create** and then choose **New dataset**.

1. Choose an Athena data source.

1. Choose **Edit Data Source**.

1. For **Data source name**, enter a new name.

1. The **Manage data source sharing** screen appears. On the **Users** tab, locate the user that you want to remove. 

1. If you want to create a new dataset using the updated data source, proceed with the instructions at [Creating a dataset using Amazon Athena data](create-a-data-set-athena.md). Otherwise, close the **Choose your table** dialog box.

# Deleting a data source


You can delete a data source if you no longer need it. Deleting a query-based database data source makes any associated datasets unusable. Deleting an Amazon S3, Salesforce, or SPICE-based database data source doesn't affect your ability to use any associated datasets. This is because the data is stored in [SPICE](spice.md). However, you can no longer refresh those datasets.

**To delete a data source**

1. Choose the data source that you want to delete.

1. Choose **Delete**.