Official

Azure Asset Inventory

dbt Azure Asset Inventory (free version)

Publisher

cloudquery

Repository

github.com

Latest version

v1.3.9

Type

Policy

Published

CloudQuery × dbt: Azure Asset Inventory Package

Overview #

This Azure Asset Inventory package works on top of the CloudQuery framework. This package offers automated line-item listing of all active resources in your Azure environment. This package supports usage with PostgreSQL, Snowflake, and BigQuery databases.

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 destination plugins based on your database:

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 #

1. Download and extract this package. #

Click the Download now button on the top. You may need to create a CloudQuery account first.

2. Install DBT and set up the DBT profile #

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. First, switch to the directory where you extracted this package. Then run this command:

dbt debug

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

...
09:37:00    retries: 1
09:37:00  Registered adapter: postgres=1.9.0
09:37:00    Connection test: [OK connection ok]

09:37:00  All checks passed!

To run a sync with CloudQuery, you will need to create an account and log in.

cloudquery login

4. Syncing Azure data #

This is an example sync config for the relevant tables for all the models (views) in this transformation. Save this to a file named azure.yaml. For detailed Azure authentication and configuration options and additional tables to add to the sync config, see the Azure plugin documentation.

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: "v17.11.0" # 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: "v8.9.0" # 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"

Run the sync:

cloudquery sync azure.yaml

5. Create the views #

Navigate to your dbt project directory, where you extracted this package.

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.

6. Explore the data #

Connect to your database and start exploring the data

select * from azure_resources limit 10

7. Production deployment #

This transformation creates a view on top of the database tables synced by CloudQuery CLI. You need to re-run the transformation (using the dbt run command) only if you add or remove tables from the sync.

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CloudOps

Azure Asset Inventory

CloudQuery × dbt: Azure Asset Inventory Package

Overview #

Example Queries #

Requirements #

Models Included

Columns Included

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

1. Download and extract this package. #

2. Install DBT and set up the DBT profile #

3. Login to CloudQuery #

4. Syncing Azure data #

5. Create the views #

6. Explore the data #

7. Production deployment #

CloudQuery × dbt: Azure Asset Inventory Package

Overview #

Example Queries #

Requirements #

Models Included

Columns Included

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

1. Download and extract this package. #

2. Install DBT and set up the DBT profile #

3. Login to CloudQuery #

4. Syncing Azure data #

5. Create the views #

6. Explore the data #

7. Production deployment #