AWS Asset Inventory Transformation documentation

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Official

AWS Asset Inventory

dbt AWS Asset Inventory pack

Publisher

cloudquery

Repositorygithub.com

Latest version

v2.3.1

Type

Policy

Published

November 4, 2024

CloudQuery × dbt: AWS Asset Inventory Package

Overview #

Welcome to our free edition of the AWS Asset Inventory package, a solution that works on top of the CloudQuery framework. This package offers automated line-item listing of all active resources in your AWS environment. Currently, this package only supports usage with PostgreSQL databases.

We recommend using this transformation with our AWS Asset Inventory Dashboard

Example Queries #

Which accounts have the most resources? (PostgreSQL)

select account_id, count(*)
from aws_resources
group by account_id
order by count(*) desc

Which services are most used in each account? (PostgreSQL)

select account_id, service, count(*)
from aws_resources
group by account_id, service
order by count(*) desc;

Which resources are not tagged? (PostgreSQL)

select * from aws_resources
where tags is null or tags = '{}';

Requirements #

One of the below databases:

PostgreSQL

Models Included

aws_resources: AWS Resources View, available for PostgreSQL.
- Required tables: This model has no specific table dependencies, other than requiring a single CloudQuery table from the AWS plugin that has an ARN.
Columns Included
_cq_id
_cq_source_name
_cq_sync_time
account_id
request_account_id
type
arn
region
tags
partition
service
_cq_table

To run this package you need to complete the following steps #

Setting up the DBT profile #

First, install dbt:

pip install dbt-postgres

Create the profile directory:

mkdir -p ~/.dbt

Create a profiles.yml file in your profile directory (e.g. ~/.dbt/profiles.yml):

aws_asset_inventory: # This should match the name in your dbt_project.yml
  target: dev
  outputs:
    dev:
      type: postgres
      host: 127.0.0.1
      user: postgres
      pass: pass
      port: 5432
      dbname: postgres
      schema: public # default schema where dbt will build the models
      threads: 1 # number of threads to use when running in parallel

Test the Connection:

After setting up your profiles.yml, you should test the connection to ensure everything is configured correctly:

dbt debug

This command will tell you if dbt can successfully connect to your PostgreSQL instance.

Because this policy uses premium features and tables you must login to your cloudquery account using cloudquery login in your terminal

Syncing AWS data #

Based on the models you are interested in running you need to sync the relevant tables. This is an example sync for the relevant tables for all the models (views) in the policy and with a Postgres destination.

kind: source
spec:
 name: aws # The source type, in this case, AWS.
 path: cloudquery/aws # The plugin path for handling AWS sources.
 registry: cloudquery # The registry from which the AWS plugin is sourced.
 version: "v25.5.3" # The version of the AWS plugin.
 tables: ["aws_ec2_instances"] # Include any tables that meet your requirements, separated by commas
 destinations: ["postgresql"] # The destination for the data, in this case, PostgreSQL.
 spec:

---
kind: destination
spec:
 name: "postgresql" # The type of destination, in this case, PostgreSQL.
 path: "cloudquery/postgresql" # The plugin path for handling PostgreSQL as a destination.
 registry: "cloudquery" # The registry from which the PostgreSQL plugin is sourced.
 version: "v8.0.1" # The version of the PostgreSQL plugin.

 spec:
   connection_string: "${POSTGRESQL_CONNECTION_STRING}"  # set the environment variable in a format like 
   # postgresql://postgres:pass@localhost:5432/postgres?sslmode=disable
   # You can also specify the connection string in DSN format, which allows for special characters in the password:
   # connection_string: "user=postgres password=pass+0-[word host=localhost port=5432 dbname=postgres"

Running Your dbt Project

Navigate to your dbt project directory, where your dbt_project.yml resides.

Before executing the dbt run command, it might be useful to check for any potential issues:

dbt compile

If everything compiles without errors, you can then execute:

dbt run

This command will run your dbt models and create tables/views in your destination database as defined in your models.

Note: If running locally, ensure you are using dbt-core and not dbt-cloud-cli as dbt-core does not require extra authentication.

To run specific models and the models in the dependency graph, the following dbt run commands can be used:

For a specific model and the models in the dependency graph:

dbt run --select +<model_name>

For a specific folder and the models in the dependency graph:

dbt run --models +<model_name>

Syncing Aws Asset Inventory to ClickHouse Using dbt #

This guide will walk you through the process of syncing aws_asset_inventory to ClickHouse as a destination using dbt.

We recommend utilizing the aws_resources model for this purpose. However, due to ClickHouse's limitations in handling large and complex queries—specifically related to query size and AST (Abstract Syntax Tree) restrictions—you may encounter some challenges. To resolve these, follow the steps outlined below.

Steps to Configure ClickHouse for dbt #

1. Create a `cloudquery.yml` ClickHouse Configuration File #

Create a cloudquery.yml file in your dbt project directory with the following configuration:

<profiles>
    <default>
        <max_query_size>10000000</max_query_size>
        <max_ast_elements>150000</max_ast_elements>
    </default>
</profiles>

2. Pass the Configuration File to ClickHouse (Example with Docker) #

Run the following command to create a Docker container for ClickHouse with the custom configuration file:

docker run --platform linux/amd64  --name clickhouse-server --rm -p 8123:8123 -p 9000:9000 \
            -e CLICKHOUSE_PASSWORD=test \
            -e CLICKHOUSE_USER=cq \
            -e CLICKHOUSE_DB=cloudquery \
            -e CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT=1 \
            -v ./clickhouse.xml:/etc/clickhouse-server/users.d/cloudquery.xml \
            clickhouse/clickhouse-server:22.1.2

This command will download the ClickHouse image and start a container that exposes the required ports for interaction.

3. Update your `dbt` Profile for ClickHouse #

Update your dbt profile to include the ClickHouse destination:

aws_asset_inventory:
  target: dev
  outputs:
    dev:
      type: clickhouse
      schema: cloudquery
      host: localhost
      port: 9000
      user: cq
      password: test

4. Run `dbt` #

Once the ClickHouse server is properly configured, you can now run dbt with the appropriate profile:

dbt run

This will execute your dbt models and sync the data to your ClickHouse destination. Make sure everything is properly set up in the dbt profile to connect to the ClickHouse server.