[DOCS-13743] EXP- GA BigQuery guide

content/en/experiments/connecting_bigquery.md

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+---
+title: Connect BigQuery for Warehouse Native Experiment Analysis
+description: Connect a BigQuery 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 BigQuery to Datadog to enable warehouse-native experiment analysis in four steps: connecting a Google Cloud Platform (GCP) service account, creating resources in GCP, granting permissions to the service account, and configuring experiment-specific settings in Datadog.
+
+## Step 1: Connect a Google Cloud service account
+
+Datadog connects to BigQuery using a service account created for Datadog. If you have already connected BigQuery to Datadog you can continue to use that service account for Datadog Experiments. Otherwise, see the [Google Cloud Platform integration page][1] to create a new service account.
+
+Once you have created a service account, continue on to the next section.
+
+<div class="alert alert-info">If you're only using the Google Cloud integration for warehouse native experiment analysis, you can opt out of collecting other resources.</div>
+
+## Step 2: Create Google Cloud resources
+
+Datadog Experiments requires a Google Cloud Storage bucket to stage experiment exposure records and a BigQuery dataset to cache intermediate experiment results. Follow the steps below to create these resources.
+
+1. In the Google Cloud Console, navigate to **BigQuery**.
+1. Click on your project, then click **Create Dataset**.
+1. Enter a dataset ID (e.g., `datadog_experiments_output`), select a data location, and click **Create Dataset**.
+1. Follow [Google's documentation][2] to create a new bucket for Datadog to stage experiment exposure records.
+
+## Step 3: Grant IAM roles to the service account
+
+In addition to the permissions described in the [Google Cloud Platform integration page][1], the Datadog Experiments service account requires the following permissions:
+
+1. [BigQuery Job User][4] — allows the service account to run BigQuery jobs.
+1. [BigQuery Data Owner][5] - grants the service account full access to the Datadog Experiments output dataset.
+1. [Storage Object User][6] - allows the service account to read and write objects in the storage bucket used by Datadog Experiment.
+1. [BigQuery Data Viewer][7] - allows the service account to read table used in warehouse native metrics.
+
+To assign these roles at the project level:
+
+1. Navigate to **IAM & Admin** > **IAM** in the Google Cloud Console.
+2. Click **Grant Access**.
+3. Enter the service account email in the **New principals** field.
+4. Add the roles listed above, then click **Save**.
+
+To grant read access to specific source tables, follow the steps below:
+
+1. Navigate to **BigQuery** in the Google Cloud Console.
+1. Select the dataset containing your source tables.
+1. Click **Sharing** > **Permissions**.
+1. Click **Add Principal**, enter the service account email, and assign the **BigQuery Data Viewer** role.
+1. Repeat for each dataset that is needed for building experiment metrics.
+
+## Step 4: Configure experiment settings
+
+Once your BigQuery service account is connected to Datadog, navigate to the [Experiment Warehouse Connection][8] page and click **Connect a data warehouse** to configure experiment settings.
+
+Select the appropriate service account and project as well as the dataset and Google Cloud Storage bucket created in step 2. Click **Save** to finish the setup.
+
+{{< img src="/product_analytics/experiment/guide/bigquery_experiment_setup.png" alt="The Edit Data Warehouse modal with BigQuery selected, showing two sections: Select BigQuery Account with fields for GCP Service Account and Project, and Dataset and GCS Bucket with fields for Dataset and GCS Bucket." style="width:90%;" >}}
+
+After you save your warehouse connection, create experiment metrics using your BigQuery data. See [Create Experiment Metrics][9].
+
+## Further reading
+
+{{< partial name="whats-next/whats-next.html" >}}
+
+[1]: /integrations/google-cloud-platform/ 
+[2]: https://docs.cloud.google.com/storage/docs/creating-buckets#console
+[4]: https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery#bigquery.jobUser
+[5]: https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery#bigquery.dataOwner
+[6]: https://docs.cloud.google.com/iam/docs/roles-permissions/storage#storage.objectUser
+[7]: https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery#bigquery.dataViewer
+[8]: https://app.datadoghq.com/product-analytics/experiments/settings/warehouse-connections
+[9]: /experiments/defining_metrics
+