> ## Documentation Index
> Fetch the complete documentation index at: https://confidence-auth-testing.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Event Connectors

> Export event data ingested through the Confidence event sender SDKs.

**Event Connectors**

Export event data ingested through the Confidence event sender SDKs.

Event connectors export event data ingested through the Confidence event sender SDKs.

**BigQuery**

Export events to BigQuery. The connector writes each event type to a separate table in the configured dataset with an optional prefix.

### Required GCP Roles

* BigQuery data owner for the destination dataset.

### Configuration

* **Project** - The GCP project the destination table exists in
* **Service account** - A GCP service account that has write access to the destination table. Configure the service account so that the Confidence service account can impersonate it.
* **Dataset** - The dataset in which to create the destination tables
* **Table prefix** - An optional prefix to use for the tables created by this connector

**Redshift**

Export events to tables in Redshift, by first writing the data as Parquet files to an S3 bucket, and then importing these files into Redshift tables, one per event type.

### Required AWS Permissions/Policies for Role

* `s3:GetObject`
* `s3:GetObjectAcl`
* `s3:PutObject`
* `s3:PutObjectAcl`
* `AmazonRedshiftDataFullAccess` policy
* `AmazonRedshiftAllCommandsFullAccess` policy

The role also needs to have permissions to create tables and insert rows into those tables in the configured database.

### Configuration

* **Table Prefix** - A prefix to use for all tables created by the connector. The connector loads each event type into its own table.
* **Cluster** - The name of the Redshift cluster to use.
* **Database** - The name of the Redshift database to use.
* **Schema** - The name of the Redshift schema in which the connector creates the table.
* **Redshift region** - The AWS region of the cluster. Because of AWS limitations, the region of the cluster needs to match the region of the S3 bucket.
* **Redshift Role ARN** - The role the Redshift `COPY` jobs use. This role needs to have permission to create tables and copy data into tables in the configured schema, and load files from the S3 bucket.
* **Bucket** - The S3 bucket to write the Parquet files to.
* **Bucket Role ARN** - The role Confidence has when writing files to the S3 bucket.
* **Bucket Region** - The AWS region of the bucket. The bucket needs to be in the same region as the Redshift cluster.
* **Batch settings** - These settings control the size and max age of the Parquet files written to S3.

**Databricks**

Export events to Databricks tables. Confidence writes Parquet files to the selected staging provider and then loads them into Databricks tables, typically one table per event type.

### Prerequisites

Before configuring this connector, follow the [Databricks setup guide](/docs/start/warehouse-native/databricks.md). It walks you through creating or reusing a Databricks service principal and SQL warehouse, granting the warehouse and Unity Catalog permissions that Confidence needs, and configuring S3 or ADLS Gen2 staging in your cloud account.

### Configuration

* **Schema** - The fully qualified Unity Catalog schema, such as `main.confidence`.
* **Host** - The Databricks workspace URL, including `https://`, for example `https://1234567890123456.7.gcp.databricks.com`.
* **Warehouse ID** - The ID of the SQL warehouse that runs the load statements.
* **Client ID** - The Databricks service principal application ID, also called the OAuth client ID.
* **Client Secret** - The OAuth secret generated for the Databricks service principal.
* **Staging provider** - Select **AWS - S3** or **Azure - ADLS Gen2**, then enter the provider-specific settings.
* **Batch settings** - These settings control the size of the Parquet files written to staging. If not set or 0, Confidence uses the default settings.

Select **Validate** before continuing. Confidence checks storage access, Databricks access, table creation, and a test load.