Click New Reference Table +, then upload a CSV file, name the appropriate columns, and define the primary key for lookups.

Note: The manual CSV upload method supports files up to 4MB.

Reference Tables can automatically pull a CSV file from an Amazon S3 bucket to keep your data up to date. The integration looks for changes to the CSV file in S3, and when the file is updated it replaces the Reference Table with the new data. This also enables API updating with the S3 API once the initial Reference Table is configured. Note: Reference Tables are not replaced if the content of the CSV file is unchanged.

To update Reference Tables from S3, Datadog uses the IAM role in your AWS account that you configured for the AWS integration. If you have not yet created that role, follow these steps to do so. To allow that role to update your Reference Tables, add the following permission statement to its IAM policies. Be sure to edit the bucket names to match your environment.

Note: If using server-side encryption, you can only upload Reference Tables encrypted with Amazon S3-managed keys (SSE-S3).

{
	"Statement": [
		{
			"Sid": "EnrichmentTablesS3",
			"Effect": "Allow",
			"Action": [
				"s3:GetObject",
				"s3:ListBucket"
			],
			"Resource": [
				"arn:aws:s3:::<MY_BUCKET_NAME_1/*>",
				"arn:aws:s3:::<MY_BUCKET_NAME_2>"
			]
		}
	],
	"Version": "2012-10-17"
}

Define the table

Click New Reference Table +, then add a name, select Amazon S3, fill out all fields, click import, and define the primary key for lookups.

Note: The upload from an S3 bucket method supports files up to 200MB.

1. If you haven’t already, set up the Azure integration within the subscription that holds the storage account from which you want to import your Reference Table. This involves creating an app registration that Datadog can integrate with.

2. In the Azure Portal, select the storage account that stores your Reference Table files.

3. Within your storage account, navigate to Access Control (IAM) and select Add > Add Role Assignment.

4. Input and select the Storage Blob Data Reader Role. The Storage Blob Data Reader role allows Datadog to read and list storage containers and blobs.

5. In the Members tab, click + Select members. Select the app registration you created in Step 1.

After reviewing and assigning the role, you can import into Reference Tables from Azure. It may take a few minutes for your Azure configuration to update in Datadog.

For more information, see the Azure integration documentation.

Note: The upload from cloud object storage supports files up to 200MB.

1. If you have not set up a Google Cloud integration with Datadog or you are using legacy Google project ID files (legacy projects are indicated in your GCP integration tile), follow the instructions for setting up the Google Cloud Platform integration. This involves creating a Google Cloud service account.

2. From the Google Cloud console, navigate to the Cloud Storage page.

3. Find the bucket you’d like to grant access to and click on it.

4. Click on the Permissions tab. Under “View By Principals”, click the Grant Access button.

5. In the window that appears, under the “New principals” field, enter the service account email that you created and added to the GCP tile in Step 1. Under “Assign roles”, select the Storage Object Viewer role. Click Save.

After reviewing and assigning the role, you can import into Reference Tables from Google Cloud. It may take a few minutes for your configuration to update in Datadog.

Note: The upload from cloud object storage supports files up to 200MB.