> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kameleoon.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Use Amazon Redshift as a destination
> Stream Kameleoon experiment exposure events into your Amazon Redshift database to enable centralized analysis and reporting of campaign results.
With the Amazon Redshift integration, you can store campaign results in your Redshift database for easy analysis and reporting.
**Key benefits:**
* Effortlessly store campaign results for centralized analysis.
* Benefit from secure storage and streamlined data transfer, accessing necessary data for decision-making with just your project ID.
Data warehouse integrations are available as a premium add-on for the Web Experimentation and Feature Experimentation module. For more information, contact your Customer Success Manager.
Once you have [enabled the Amazon Redshift integration for your project](./setting-up-amazon-redshift), you can activate **Use Amazon Redshift as a destination** to seamlessly send events to your Amazon Redshift account whenever one of your Kameleoon experiments exposes visitors.
## Activate Amazon Redshift as a destination
To activate Amazon Redshift as a destination:
1. Click **Use Redshift as a destination**.
2. Choose your cluster type: **Provisioned** or **Serverless**.
* For the **Provisioned** cluster type, you must provide:
* **Redshift cluster ID:** The identifier of your Amazon Redshift cluster, a critical component for directing campaign results to the correct destination within Redshift
* **S3 bucket name:** The full name of an Amazon S3 bucket for staging streamed events before periodic bulk loading into Redshift. The policy attached to the AWS role you provided must grant access to this bucket
* **Redshift AWS region:** The Region code where your source Redshift cluster resides. Find this code in your AWS console. Example: `eu-west-1`
* **Database:** The name of the Redshift database containing the data you're ingesting
* For the **Serverless** cluster type, you must provide:
* **Redshift serverless workgroup:** Your workgroup's name
* **S3 bucket name:** The full name of an Amazon S3 bucket for staging streamed events before periodic bulk loading into Redshift. The policy attached to the AWS role you provided must grant access to this bucket
* **Redshift AWS region:** The Region code where your source Redshift cluster resides. Find this code in your AWS console. Example: `eu-west-1`
* **Database:** The name of the Redshift database containing the data you're ingesting
## What **Use Amazon Redshift as a destination** does
Enabling **Use Amazon Redshift as a destination** streams all Kameleoon experiment exposure events into Amazon.
Kameleoon stores events in the `kameleoon_events` schema of your `{database}`, the name of the Redshift database where Kameleoon ingests the data. This database is the one you set up during [setup](./setting-up-amazon-redshift), with write access granted to the Kameleoon user. Kameleoon saves the data in a table named `kameleoon_experiment_event`.
The SQL schema of this table is the following:
```sql theme={null}
CREATE TABLE kameleoon_experiment_event (
nonce BIGINT PRIMARY KEY, -- unique identifier of the event
timestamp BIGINT, -- timestamp in millis of the event
visitor_code VARCHAR(255), -- Kameleoon visitor identifier
custom_visitor_id VARCHAR(255), -- Visitor identifier used by your company
experiment_id BIGINT, -- Kameleoon id of the experiment
variation_id BIGINT -- Kameleoon id of the variation
);
```
Kameleoon reads the `custom_visitor_id` from the [Cross Device Reconciliation custom data](../../../../developer-docs/cross-device-experimentation), if you set it.
With campaign data stored in Amazon, you gain the ability to perform in-depth analysis and reporting. You can leverage Amazon’s querying capabilities to extract valuable insights from the collected data, helping you make data-driven decisions to optimize your campaigns and user experience.
By centralizing Kameleoon campaign results in Amazon, you contribute to the enrichment of your Amazon database, making it a comprehensive repository of user data that you can use for a variety of analytical and business intelligence purposes.