> ## Documentation Index
> Fetch the complete documentation index at: https://docs.domino.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Connect to Redshift

This topic describes how to connect to [Amazon Redshift](https://aws.amazon.com/pm/redshift/) from Domino. You must have network connectivity between Redshift and your Domino deployment.

The easiest way to connect to Redshift from Domino is to create a Domino Data Source as described below.

## Create a Redshift Data Source

1. From the navigation pane, click **Data > Data Sources**.

2. Click **Create a Data Source**.

3. In the New Data Source window, from **Select Data Store**, select **Amazon Redshift**.

4. Enter the **Host**, **Port**, and name of the **Database**.

5. Enter the **Data Source Name**.

6. Optional: Enter a **Description** to explain the purpose of the Data Source to others.

7. Click **Next**.

8. Specify the credentials for authenticating to Redshift.

   Basic authentication is supported by default. IAM credential propagation might also be available if your administrator has [enabled it](/cloud/admin/data-administration/external-data/configure-ds-auth#iam-auth).

   <Note>
     IAM-authenticated connections can be used only for executing jobs and workspaces. Other execution types, such as scheduled jobs and Domino endpoints, require basic authentication.
   </Note>

9. Select who can view and use the Data Source in projects.

10. Click **Finish Setup**.

## Alternate way to connect to a Redshift Data Source

<Warning>
  This section describes an alternate method to connect to the Redshift Data Source. Domino does not officially support this method.
</Warning>

### Prerequisites

* Domino recommends storing your database username and password as [environment variables](/cloud/platform-capabilities/core-concepts/compute-environments/manage-compute-environments/manage-environment-variables) in your project. This lets you access them at runtime without including them in your code.

### Python

To establish a connection to Redshift with the `psycopg2` library:

```python theme={null}
import psycopg2
import os

HOST = os.environ['REDSHIFT_HOST']
PORT = 5439 # redshift default
USER = os.environ['REDSHIFT_USER']
PASSWORD = os.environ['REDSHIFT_PASSWD']
DATABASE = 'mydatabase'

def db_connection():
    conn = psycopg2.connect(
        host=HOST,
        port=PORT,
        user=USER,
        password=PASSWORD,
        database=DATABASE,
    )
    return conn

example_query = "SELECT * FROM my_table LIMIT 5"

conn = db_connection()
try:
    cursor = conn.cursor()
    cursor.execute(example_query)
    results = cursor.fetchall() # careful, the results could be huge
    conn.commit()
    print results
finally:
    conn.close()

# using pandas
import pandas as pd
conn = db_connection()
try:
    df = pd.read_sql(example_query, conn)
    df.to_csv('results/outfile.csv', index=False)
finally:
    conn.close()
```

### R

To establish a connection to Redshift with the RPostgreSQL library:

```r theme={null}
install.packages("RPostgreSQL")
library(RPostgreSQL)

redshift_host <- Sys.getenv("REDSHIFT_HOST")
redshift_port <- "5439"
redshift_user <- Sys.getenv("REDSHIFT_USER")
redshift_password <- Sys.getenv("REDSHIFT_PASSWORD")
redshift_db <- "mydatabase"

drv <- dbDriver("PostgreSQL")
conn <- dbConnect(
    drv,
    host=redshift_host,
    port=redshift_port,
    user=redshift_user,
    password=redshift_password,
    dbname=redshift_db)

tryCatch({
    example_query <- "SELECT * FROM my_table LIMIT 5"
    results <- dbGetQuery(conn, example_query)
    print(results)
}, finally = {
    dbDisconnect(conn)
})
```

## Next steps

* After connecting to your Data Source, learn how to [Use Data Sources](/cloud/platform-capabilities/core-concepts/data/data-source-connectors/use-data-sources).

* [Share this Data Source](/cloud/platform-capabilities/core-concepts/data/sharing-and-security/share-data-sources) with your collaborators.