Use the Amazon S3 destination to send logs to Amazon S3. To send logs in Datadog-rehydratable format to Amazon S3 for archiving and rehydration, configure Log Archives and then set up the Amazon S3 destination in your pipeline.

You can also route logs to Snowflake using the Amazon S3 destination.

Configure Log Archives

If you want to send logs to Amazon S3 in Datadog-rehydratable format for archiving and rehydration, you need to set up a Datadog Log Archive. If you already have a Datadog Log Archive configured for Observability Pipelines, skip to Set up the destination for your pipeline.

You need to have Datadog’s AWS integration installed to set up Datadog Log Archives.

Create an Amazon S3 bucket

1. Navigate to Amazon S3 buckets.
2. Click Create bucket.
3. Enter a descriptive name for your bucket.
4. Do not make your bucket publicly readable.
5. Optionally, add tags.
6. Click Create bucket.

Set up an IAM policy that allows Workers to write to the S3 bucket

1. Navigate to the IAM console.
2. Select Policies in the left side menu.
3. Click Create policy.
4. Click JSON in the Specify permissions section.
5. Copy the below policy and paste it into the Policy editor. Replace <MY_BUCKET_NAME> and <MY_BUCKET_NAME_1_/_MY_OPTIONAL_BUCKET_PATH_1> with the information for the S3 bucket you created earlier.

   {
       "Version": "2012-10-17",
       "Statement": [
           {
               "Sid": "DatadogUploadAndRehydrateLogArchives",
               "Effect": "Allow",
               "Action": ["s3:PutObject", "s3:GetObject"],
               "Resource": "arn:aws:s3:::<MY_BUCKET_NAME_1_/_MY_OPTIONAL_BUCKET_PATH_1>/*"
           },
           {
               "Sid": "DatadogRehydrateLogArchivesListBucket",
               "Effect": "Allow",
               "Action": "s3:ListBucket",
               "Resource": "arn:aws:s3:::<MY_BUCKET_NAME>"
           }
       ]
   }
   

6. Click Next.
7. Enter a descriptive policy name.
8. Optionally, add tags.
9. Click Create policy.

Connect the S3 bucket to Datadog Log Archives

1. Navigate to Datadog Log Forwarding.
2. Click New archive.
3. Enter a descriptive archive name.
4. Add a query that filters out all logs going through log pipelines so that none of those logs go into this archive. For example, add the query observability_pipelines_read_only_archive, assuming no logs going through the pipeline have that tag added.
5. Select AWS S3.
6. Select the AWS account that your bucket is in.
7. Enter the name of the S3 bucket.
8. Optionally, enter a path.
9. Check the confirmation statement.
10. Optionally, add tags and define the maximum scan size for rehydration. See Advanced settings for more information.
11. Click Save.

See the Log Archives documentation for additional information.

Set up the destination for your pipeline

Set up the Amazon S3 destination and its environment variables when you set up an Archive Logs pipeline. The information below is configured in the pipelines UI.

1. Enter the S3 bucket name for the S3 bucket you created earlier.
2. Enter the AWS region the S3 bucket is in.
3. Enter the key prefix.
   • Prefixes are useful for partitioning objects. For example, you can use a prefix as an object key to store objects under a particular directory. If using a prefix for this purpose, it must end in / to act as a directory path; a trailing / is not automatically added.
   • See template syntax if you want to route logs to different object keys based on specific fields in your logs.
4. Select the storage class for your S3 bucket in the Storage Class dropdown menu.
5. Optionally, select an AWS authentication option. If you select Assume role:
   1. Enter the ARN of the IAM role you want to assume.
   2. Optionally, enter the assumed role session name and external ID.

Set the environment variables

There are no environment variables to configure.

Route logs to Snowflake using the Amazon S3 destination

You can route logs from Observability Pipelines to Snowflake using the Amazon S3 destination by configuring Snowpipe in Snowflake to automatically ingest those logs. To set this up:

1. Configure Log Archives if you want to archive and rehydrate your logs. If you only want to send logs to Amazon S3, skip to step 2.
2. Set up a pipeline to use Amazon S3 as the log destination. When logs are collected by Observability Pipelines, they are written to an S3 bucket using the same configuration detailed in Set up the destination for your pipeline, which includes AWS authentication, region settings, and permissions.
3. Set up Snowpipe in Snowflake. See Automating Snowpipe for Amazon S3 for instructions. Snowpipe continuously monitors your S3 bucket for new files and automatically ingests them into your Snowflake tables, ensuring near real-time data availability for analytics or further processing.

How the destination works

AWS Authentication

The Observability Pipelines Worker uses the standard AWS credential provider chain for authentication. See AWS SDKs and Tools standardized credential providers for more information.

Permissions

For Observability Pipelines to send logs to Amazon Security Lake, the following policy permissions are required:

• s3:ListBucket
• s3:PutObject

Event batching

A batch of events is flushed when one of these parameters is met. See event batching for more information.