[DOCS-13741] EXP- GA Redshift guide

content/en/experiments/connecting_redshift.md

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+---
+title: Connect Redshift for Warehouse Native Experiment Analysis
+description: Connect a Redshift service account to enable warehouse native experiment analysis.
+private: true
+further_reading:
+- link: "/experiments/defining_metrics"
+  tag: "Documentation"
+  text: "Defining metrics in Datadog Experiments"
+- link: "https://www.datadoghq.com/blog/experimental-data-datadog/"
+  tag: "Blog"
+  text: "How to bridge speed and quality in experiments through unified data"
+---
+
+## Overview
+
+This guide walks through connecting AWS Redshift to Datadog to enable warehouse-native experiment analysis in four steps: connecting AWS to Datadog, creating necessary resources in AWS, granting permissions to the Datadog service user, and configuring experiment-specific settings in Datadog.
+
+## Step 1: Connect AWS to Datadog
+
+Datadog Experiments connects to Redshift through the Datadog AWS integration. If you already have an [AWS integration][1] configured for the account containing your Redshift cluster, you can skip to the next step.
+
+If you haven't set up the AWS integration yet:
+
+1. Navigate to the [AWS integration tile][2] in Datadog.
+2. Click **Add AWS Account** and follow the setup flow using the CloudFormation template. This creates an IAM role that allows Datadog to make API calls to your AWS account.
+3. For more details, see the [AWS integration documentation][1].
+
+If you're only using the AWS integration for warehouse native experiment analysis, you can opt out of collecting other resources.
+
+## Step 2: Create resources in AWS
+
+Before you connect Datadog Experiments to your data warehouse, create a service user for Datadog.
+
+You will also need to create a schema for Datadog Experiments to write intermediary tables to, as well as grant the service User read access to tables you'd like Datadog Experiments to query SQL definitions from.
+
+### Create a Service User for Datadog Experiments
+
+1. Connect to your Redshift cluster with a user that has superuser or admin privileges.
+2. Create a user with the following command:
+
+```sql
+CREATE USER datadog_experiments_user PASSWORD '<strong_password>';
+```
+
+3. Grant this user the appropriate privileges for any tables needed for building experiment metrics. Replace `<schema>` and `<table>` with the appropriate values.
+
+```sql
+GRANT USAGE ON SCHEMA <schema> TO datadog_experiments_user;
+
+-- individual tables
+GRANT SELECT ON TABLE <schema>.<table> TO datadog_experiments_user;
+
+-- all tables
+GRANT SELECT ON ALL TABLES IN SCHEMA <schema> TO datadog_experiments_user;
+```
+
+### Create Schema for Datadog Experiments to Write Results to
+
+Create a schema for Datadog Experiments to write intermediate results and temporary tables.
+
+```sql
+CREATE SCHEMA IF NOT EXISTS datadog_experiments_output;
+GRANT ALL ON SCHEMA datadog_experiments_output TO datadog_experiments_user;
+```
+
+### Create an S3 Bucket
+
+Create an S3 bucket for importing exposure events into your warehouse. The bucket must start with `"datadog-experimentation-"`, e.g. `datadog-experimentation-[AWS account ID]`. Default settings can be used.
+
+## Step 3: Grant additional IAM permissions
+
+Because Datadog Experiments writes data into your warehouse, your AWS IAM role used by the Datadog integration needs additional permissions beyond those required for the standard [Amazon Redshift Datadog integration][1].
+
+Add the following permissions to the IAM role used by your Datadog AWS integration:
+
+```JSON
+{
+	"Version": "2012-10-17",
+	"Statement": [
+		{
+			"Sid": "GetClusterCreds",
+			"Effect": "Allow",
+			"Action": [
+				"redshift:GetClusterCredentials"
+			],
+			"Resource": [
+				"[Redshift cluster ARN]",
+				"[Redshift user ARN]",
+				"[Redshift database ARN]"
+			]
+		},
+		{
+			"Sid": "QueryRedshift",
+			"Effect": "Allow",
+			"Action": [
+				"redshift-data:ExecuteStatement",
+				"redshift-data:GetStatementResult",
+				"redshift-data:DescribeStatement",
+				"redshift-data:ListStatements",
+				"redshift-data:CancelStatement"
+			],
+			"Resource": "*"
+		},
+		{
+			"Sid": "ListTheBucket",
+			"Effect": "Allow",
+			"Action": [
+				"s3:ListBucket"
+			],
+			"Resource": "[S3 bucket ARN]"
+		},
+		{
+			"Sid": "ObjectRW",
+			"Effect": "Allow",
+			"Action": [
+				"s3:GetObject",
+				"s3:PutObject",
+				"s3:DeleteObject"
+			],
+			"Resource": "[S3 bucket ARN]/*"
+		}
+	]
+}
+```
+
+Replace the following with the appropriate values: 
+
+| Field | Example |
+|-------|---------|
+| `[Redshift cluster ARN]` | `arn:aws:redshift:us-east-1:[account-id]:namespace:[namespace-id]` |
+| `[Redshift user ARN]` | `arn:aws:redshift:us-east-1:312078929830:dbuser:[cluster-name]/[user]` |
+| `[Redshift database ARN]`| `arn:aws:redshift:us-east-1:[account-id]:dbname:[cluster-name]` |
+| `[S3 bucket ARN]` | `arn:aws:s3:::[bucket-name]` |
+
+## Step 4: Configure experiment settings
+
+Once your AWS service user is connected to Datadog, follow these steps to finish the integration.
+
+1. Navigate to the [Warehouse Connections page][3].
+2. Click **Redshift**.
+3. Under **Select AWS Account**, choose the AWS account you configured above.
+4. Under **Cluster Connection**, enter:
+    - **AWS region** — the region your Redshift cluster is in (e.g., `us-east-1`)
+    - **Cluster identifier** — the name of your Redshift cluster
+    - **Cluster endpoint** — the full endpoint URL for your cluster
+    - **Port** — the port your cluster is listening on (default: `5439`)
+5. Under Database and Storage, enter:
+    - **Database** — the database containing your source tables
+    - **Database user** — the service user you created earlier (e.g., `datadog_experiments_user`)
+    - **Schema** — the schema you created for Datadog Experiments to write to (e.g., `datadog_experiments_output`)
+    - **Temp S3 bucket** — the S3 bucket you created earlier (e.g., `datadog-experimentation-[AWS account ID]`)
+6. Click **Save**
+
+{{< img src="/product_analytics/experiment/guide/redshift_experiment_setup.png" alt="The Edit Data Warehouse modal with Redshift selected, showing inputs for AWS account, AWS region, cluster identifier, cluster endpoint ,database, database user, schema, and temp S3 bucket" style="width:90%;" >}}
+
+After you save your warehouse connection, create experiment metrics using your Redshift data. See [Create Experiment Metrics][4].
+
+## Further reading
+
+{{< partial name="whats-next/whats-next.html" >}}
+
+[1]: https://docs.datadoghq.com/integrations/amazon-web-services/
+[2]: https://app.datadoghq.com/integrations/aws
+[3]: https://app.datadoghq.com/product-analytics/experiments/settings/warehouse-connections
+[4]: /experiments/defining_metrics