AWS

Tutorials

How to Load Infrastructure Data into AWS Athena

• January 15, 2023

Introduction #

Athena is a serverless query service by AWS that allows you to query data in S3 using standard SQL. In this tutorial, we will show you how to load your cloud infrastructure data into S3 using CloudQuery and query it using Athena. This allows you to get fine-grained insights into your infrastructure data, all from the convenience of a serverless query environment running on AWS.

By the end of this post, you will be able to query your AWS infrastructure data using Athena:

To accomplish this we will load data into S3 using CloudQuery, set up a Glue Crawler to automatically create the database and tables in a Glue Catalog, and then query those tables using Athena. Let's get started!

Step 1: Create a Bucket for the Data #

We will need to upload the sync results to S3 so that Athena can query them. We'll use the AWS CLI to do this in this tutorial, but you can also use the AWS web console or Terraform/CloudFormation if you prefer.

Let's start by exporting the name of the bucket we'll be using in the rest of this guide as an environment variable:

export BUCKET_NAME=cloudquery-athena-example  # Change this to your own bucket name

Now we will create a bucket to store the data:

aws s3 mb s3://$BUCKET_NAME

This will create the bucket with default AWS SSE-S3 encryption. If you have different requirements for bucket setup and encryption, you may use the s3api create-bucket command to set more fine-grained options.

Step 2: Install CloudQuery #

To sync infrastructure data to S3, we will need an installation of the CloudQuery CLI (or Docker image). See our Quickstart for detailed instructions.

When this step is complete, you should be able to run cloudquery --help and see the help output:

$ cloudquery --help
CloudQuery CLI

Open source data integration at scale.

[...]

Step 3: Configure the AWS Source Plugin #

We will configure the CloudQuery AWS source plugin to sync data from your AWS account(s). To do so, we'll create a file called aws.yml with the following config:

kind: source
spec:
  name: aws
  path: cloudquery/aws
  registry: cloudquery
  version: "v30.1.0"
  tables: ["aws_s3_buckets"] # See full list of tables at https://hub.cloudquery.io/plugins/source/cloudquery/aws/tables
  destinations: ["s3"]

This is the most basic configuration of the AWS source plugin. It will work as long as the AWS credentials you have configured in your environment have the appropriate permissions (e.g. via aws sso login). For more information on configuring the AWS source plugin, see the AWS Source Plugin documentation.

Step 4: Configure the S3 Destination Plugin #

Similar to the config we created for the AWS plugin, we also need a destination config that is configured to write the AWS data to JSON files in S3. We'll call this file s3.yml:

kind: destination
spec:
  name: "s3"
  path: "cloudquery/s3"
  registry: "cloudquery"
  version: "v7.7.1"
  write_mode: "append"
  spec:
    bucket: "${BUCKET_NAME}"
    region: "${AWS_REGION}"
    path: "cloudquery/{{TABLE}}/{{UUID}}.json"
    format: "json"
    athena: true

Take special note of the athena: true flag: this is important, as it will tell the S3 destination to sanitize JSON columns for use with Glue & Athena. For more information on configuring the S3 destination plugin, see the S3 Destination Plugin documentation.

Step 5: Run CloudQuery sync #

With the CLI installed, these two files in place, and an environment authenticated to AWS, we can now run the following command to sync our AWS infrastructure data to S3:

cloudquery sync aws.yml s3.yml

This will write the AWS data as JSON files to the S3 bucket. You can see the files that were created by running aws s3 ls s3://$BUCKET_NAME.

Step 6: Create a Glue Crawler #

Athena can query data in S3, but it needs to know the schema of the data in order to do so. We can use a Glue Crawler to automatically infer the schema of the data and create a table in the Athena database we created in the previous step. We'll use the AWS CLI again.

First we'll create a role for the crawler to use. It will need access to the S3 bucket we created in the previous step. The following trust policy will give the role what it needs:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "glue.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

With the above policy saved as crawler-trust-policy.json, we can now create the role (make a note of the ARN for the newly created role):

aws iam create-role \
    --role-name cloudquery-athena-example-crawler \
    --assume-role-policy-document file://crawler-trust-policy.json

We should also attach a policy to the role that gives it access to the S3 bucket we created in the previous step. The following policy will give the crawler access to the S3 bucket. Make sure to update the bucket name to the value of BUCKET_NAME you chose earlier (in our example, cloudquery-athena-example):

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::cloudquery-athena-example/*"
            ]
        }
    ]
}

Let's attach this policy to the role:

aws iam put-role-policy \
    --role-name cloudquery-athena-example-crawler \
    --policy-name cloudquery-athena-example-crawler-s3-access \
    --policy-document file://crawler-policy-s3-access.json

The crawler will also need additional permissions to perform all its tasks, such as writing CloudWatch logs. We attach another policy to the role to give it these permissions. This is a simplified version of the default policy created by AWS when creating a crawler through the console. You should review these permissions to ensure they are appropriate for your use case:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "glue:*",
                "s3:GetBucketLocation",
                "s3:ListBucket",
                "s3:ListAllMyBuckets",
                "s3:GetBucketAcl",
                "iam:ListRolePolicies",
                "iam:GetRole",
                "iam:GetRolePolicy",
                "cloudwatch:PutMetricData"
            ],
            "Resource": [
                "*"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": [
                "arn:aws:logs:*:*:/aws-glue/*"
            ]
        }
    ]
}

aws iam put-role-policy \
    --role-name cloudquery-athena-example-crawler \
    --policy-name cloudquery-athena-example-crawler \
    --policy-document file://crawler-policy.json

Now we can create the crawler, making sure to reference the ARN for the role we created above:

aws glue create-crawler \
    --name cloudquery-athena-example \
    --database-name cloudquery-athena-example \
    --role arn:aws:iam::123456789012:role/cloudquery-athena-example-crawler \
    --targets "S3Targets=[{Path=s3://$BUCKET_NAME}]" \
    --schema-change-policy "UpdateBehavior=UPDATE_IN_DATABASE,DeleteBehavior=DEPRECATE_IN_DATABASE"

If you see

An error occurred (AccessDeniedException) when calling the CreateCrawler operation: Cross-account pass role is not allowed

, make sure you updated the role ARN in the command above to that of the role we created earlier.

With our crawler created, we can run it on demand like this:

aws glue start-crawler --name cloudquery-athena-example

(You can also run the crawler on a schedule, but we won't cover that here.)

Step 7: Query the data #

The crawler should have created a database and tables in the Glue Data Catalog. Now we can query the data using Athena! Let's use the AWS Console for this step. Navigate to the Athena service in the AWS Console, go to the Query Editor page, and select the database we created earlier. You should see a list of tables in the database. Let's run a simple query to find all S3 buckets that are permitted to be public:

-- Find all S3 buckets that are permitted to be public
SELECT "arn", "region"
FROM "cloudquery-athena-example"."aws_s3_buckets"
WHERE "block_public_acls" <> TRUE
    OR "block_public_policy" <> TRUE
    OR "ignore_public_acls" <> TRUE
    OR "restrict_public_buckets" <> TRUE

Wait, isn't that the bucket we just created for this guide? Better go lock it down 😉

{//}

Athena is case-insensitive. If you see an error like Row is not a valid JSON Object - JSONException: Duplicate key, this means some of the data in a JSON column contained keys where only the case differed (e.g. Name and name). AWS tags are case sensitive, so this can be a common cause. For more information about ways to address this issue, see this GitHub comment.

{//}

Conclusion #

In this tutorial, we showed how to use CloudQuery to load infrastructure data into an S3 bucket, and then use Glue Crawler and Athena to query the data. This allows you to use the power of Athena to query your infrastructure data, and use the results to inform your security and compliance decisions.

One thing we haven't covered here is how data will be updated the next time you run a sync. The S3 destination plugin only supports the "append" write mode, which means it will keep adding new files and never remove the old ones. When querying data from the second sync onward, there will be duplicate entries. To deal with this, most queries should make use of the latest _cq_sync_time, a unique timestamp assigned to every sync:

-- Find all S3 buckets that are permitted to be public (updated to only use data from latest sync)
SELECT "arn", "region"
FROM "cloudquery-athena-example"."aws_s3_buckets"
WHERE ("block_public_acls" <> TRUE
    OR "block_public_policy" <> TRUE
    OR "ignore_public_acls" <> TRUE
    OR "restrict_public_buckets" <> TRUE)
    AND (_cq_sync_time = (select max(_cq_sync_time) from "cloudquery-athena-example"."aws_s3_buckets"))

In this way you will maintain a historical record of your data, but still be able to only query the latest data.

Going forward, we will continue making it easier and faster to load data into Athena, but also easier to maintain views over the latest data. Stay tuned for more updates!

Ready to get started with CloudQuery? You can try out CloudQuery locally with our quick start guide or explore the CloudQuery Platform (currently in beta) for a more scalable solution.

Want help getting started? Join the CloudQuery community to connect with other users and experts, or message our team directly here if you have any questions.

Written by Herman Schaaf

Herman is the Director of Engineering at CloudQuery and an Apache Arrow contributor. A polyglot with a preference for Go and Python, he has spoken at QCon London and Data Council New York.

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