Export from Datadog to S3

CloudQuery is an open-source data integration platform that allows you to export data from any source to any destination.

The CloudQuery Datadog plugin allows you to sync data from Datadog to any destination, including S3. It's free, open source, requires no account, and takes only minutes to get started.

Ready? Let's dive right in!

Step 1. Install the CloudQuery CLI

The CloudQuery CLI is a command-line tool that runs the sync. It supports MacOS, Linux and Windows.

brew install cloudquery/tap/cloudquery

Step 2. Configure the Datadog source plugin

Create a configuration file for the Datadog plugin and set up authentication.

Configuration

Create a file called datadog.yaml and add the following contents:

kind: source
spec:
  # Source spec section
  name: "datadog"
  path: "cloudquery/datadog"
  version: "v4.2.1"
  tables: ["*"]
  destinations: ["s3"]

  spec:
    accounts:
      - name: example_account # Required. Name to distinguish accounts
        api_key: <DD_CLIENT_API_KEY> # Required. API key
        app_key: <DD_CLIENT_APP_KEY> # Required. App key
    # Optional parameters
    # site: datadoghq.eu
    # concurrency: 10000

Fine-tune this configuration to match your needs. For more information, see the Datadog Plugin ↗ page in the docs.

Authentication

CloudQuery requires only DD_CLIENT_API_KEY and DD_CLIENT_APP_KEY. Follow this guide (opens in a new tab) for how to create an API key and app key for CloudQuery.

CloudQuery requires only read permissions (we will never make any changes to your Datadog account), so, following the principle of the least privilege, it's recommended to grant it read-only permissions.

Step 3. Configure the S3 destination plugin

Create a configuration file for the S3 plugin and set up authentication.

Configuration

Create a file called s3.yaml and add the following contents:

This example uses the parquet format, to create parquet files in s3://bucket_name/path/to/files, with each table placed in its own directory.

The (top level) spec section is described in the Destination Spec Reference.

kind: destination
spec:
  name: "s3"
  path: "cloudquery/s3"
  version: "v4.8.0"
  spec:
    bucket: "bucket_name"
    region: "region-name" # Example: us-east-1
    path: "path/to/files/{{TABLE}}/{{UUID}}.parquet"
    format: "parquet" # options: parquet, json, csv
    format_spec:
      # CSV-specific parameters:
      # delimiter: ","
      # skip_header: false

    # Optional parameters
    # compression: "" # options: gzip
    # no_rotate: false
    # athena: false # <- set this to true for Athena compatibility
    # test_write: true # tests the ability to write to the bucket before processing the data
    # endpoint: "" # Endpoint to use for S3 API calls.
    # endpoint_skip_tls_verify # Disable TLS verification if using an untrusted certificate
    # use_path_style: false
    # batch_size: 10000 # 10K entries
    # batch_size_bytes: 52428800 # 50 MiB
    # batch_timeout: 30s # 30 seconds

It is also possible to use {{YEAR}}, {{MONTH}}, {{DAY}} and {{HOUR}} in the path to create a directory structure based on the current time. For example:

path: "path/to/files/{{TABLE}}/dt={{YEAR}}-{{MONTH}}-{{DAY}}/{{UUID}}.parquet"

Other supported formats are json and csv.

Note that the S3 plugin only supports append write-mode. The (top level) spec section is described in the Destination Spec Reference.

Fine-tune this configuration to match your needs. For more information, see the S3 Plugin ↗ page in the docs.

Authentication

The plugin needs to be authenticated with your account(s) in order to sync information from your cloud setup.

The plugin requires only PutObject permissions (we will never make any changes to your cloud setup), so, following the principle of least privilege, it's recommended to grant it PutObject permissions.

There are multiple ways to authenticate with AWS, and the plugin respects the AWS credential provider chain. This means that CloudQuery will follow the following priorities when attempting to authenticate:

The AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_SESSION_TOKEN environment variables.
The credentials and config files in ~/.aws (the credentials file takes priority).
You can also use aws sso to authenticate cloudquery - you can read more about it here (opens in a new tab).
IAM roles for AWS compute resources (including EC2 instances, Fargate and ECS containers).

You can read more about AWS authentication here (opens in a new tab) and here (opens in a new tab).

Environment Variables

CloudQuery can use the credentials from the AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_SESSION_TOKEN environment variables (AWS_SESSION_TOKEN can be optional for some accounts). For information on obtaining credentials, see the AWS guide (opens in a new tab).

To export the environment variables (On Linux/Mac - similar for Windows):

export AWS_ACCESS_KEY_ID={Your AWS Access Key ID}
export AWS_SECRET_ACCESS_KEY={Your AWS secret access key}
export AWS_SESSION_TOKEN={Your AWS session token}

Shared Configuration files

The plugin can use credentials from your credentials and config files in the .aws directory in your home folder. The contents of these files are practically interchangeable, but CloudQuery will prioritize credentials in the credentials file.

For information about obtaining credentials, see the AWS guide (opens in a new tab).

Here are example contents for a credentials file:

~/.aws/credentials

[default]
aws_access_key_id = YOUR_ACCESS_KEY_ID
aws_secret_access_key = YOUR_SECRET_ACCESS_KEY

You can also specify credentials for a different profile, and instruct CloudQuery to use the credentials from this profile instead of the default one.

For example:

~/.aws/credentials

[myprofile]
aws_access_key_id = YOUR_ACCESS_KEY_ID
aws_secret_access_key = YOUR_SECRET_ACCESS_KEY

Then, you can either export the AWS_PROFILE environment variable (On Linux/Mac, similar for Windows):

export AWS_PROFILE=myprofile

IAM Roles for AWS Compute Resources

The plugin can use IAM roles for AWS compute resources (including EC2 instances, Fargate and ECS containers). If you configured your AWS compute resources with IAM, the plugin will use these roles automatically. For more information on configuring IAM, see the AWS docs here (opens in a new tab) and here (opens in a new tab).

User Credentials with MFA

In order to leverage IAM User credentials with MFA, the STS "get-session-token" command may be used with the IAM User's long-term security credentials (Access Key and Secret Access Key). For more information, see here (opens in a new tab).

aws sts get-session-token --serial-number <YOUR_MFA_SERIAL_NUMBER> --token-code <YOUR_MFA_TOKEN_CODE> --duration-seconds 3600

Then export the temporary credentials to your environment variables.

export AWS_ACCESS_KEY_ID=<YOUR_ACCESS_KEY_ID>
export AWS_SECRET_ACCESS_KEY=<YOUR_SECRET_ACCESS_KEY>
export AWS_SESSION_TOKEN=<YOUR_SESSION_TOKEN>

Using a Custom S3 Endpoint

If you are using a custom S3 endpoint, you can specify it using the endpoint spec option. If you're using authentication, the region option in the spec determines the signing region used.

Step 4. Start the Sync

Run the following command in your terminal to start the sync

cloudquery sync datadog.yaml s3.yaml

And away we go! 🚀 The sync will run until completion, fetching all selected tables from Datadog. Any errors will be logged to a file called cloudquery.log.

Export from Datadog to S3

Step 1. Install the CloudQuery CLI

Step 2. Configure the Datadog source plugin

Configuration

Authentication

Step 3. Configure the S3 destination plugin

Configuration

Authentication

Environment Variables

Shared Configuration files

IAM Roles for AWS Compute Resources

User Credentials with MFA

Using a Custom S3 Endpoint

Step 4. Start the Sync

Further Reading

Environment Variables

Shared Configuration files

IAM Roles for AWS Compute Resources

User Credentials with MFA

Using a Custom S3 Endpoint