Azure Asset Inventory Transformation documentation

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Official

Azure Asset Inventory

dbt Azure Asset Inventory (free version)

Publisher

cloudquery

Repositorygithub.com

Latest version

v1.3.5

Type

Policy

Published

December 2, 2024

CloudQuery × dbt: Azure Asset Inventory Package

Overview #

Welcome to our free edition of the Azure 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 Azure environment. Currently, this package supports usage with PostgreSQL, Snowflake, and BigQuery databases.

Coming soon #

Azure Asset Inventory Dashboard

Example Queries #

How many resources are there per subscription? (PostgreSQL)

select subscription_id, count(*)
from azure_resources
group by subscription_id
order by count(*) desc

Resources by id and location (PostgreSQL)

select id, location, count(*)
from azure_resources
group by id, location
order by count(*) desc

Requirements #

One of the below databases:

Models Included

azure_resources: Azure Resources View, available for PostgreSQL.
- Required tables: This model has no specific table dependencies, other than requiring a single CloudQuery table from the Azure plugin that has a subscription id.

Columns Included

_cq_id
_cq_source_name
_cq_sync_time
subscription_id
id
location
name
kind
_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):

azure_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 Azure 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 PostgreSQL destination.

kind: source
spec:
 name: "azure" # The source type, in this case, Azure.
 path: "cloudquery/azure" # The plugin path for handling Azure sources.
 registry: "cloudquery" # The registry from which the Azure plugin is sourced.
 version: "v12.1.2" # The version of the Azure plugin.
 destinations: ["postgresql"] # The destination for the data, in this case, PostgreSQL.
 tables: ["azure_compute_virtual_machines"] # Include any tables that meet your requirements, separated by commas
 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>