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GCP Asset Inventory

dbt GCP Asset Inventory pack (free version)



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CloudQuery × dbt: GCP Asset Inventory Package

Overview #

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

Coming soon #

  • GCP Asset Inventory Dashboard

Example Queries #

How many resources are there per project? (PostgreSQL)
select project_id, count(*)
from gcp_resources
group by project_id
order by count(*) desc
How many resources by project and region? (PostgreSQL)
select project_id, region, count(*)
from gcp_resources
group by project_id, region
order by count(*) desc

Requirements #

One of the below databases:
Models Included
  • gcp_resources: GCP Resources View, available for PostgreSQL.
    • Required tables: This model has no specific table dependencies, other than requiring a single CloudQuery table from the GCP plugin that has a project id.
Columns Included
  • _cq_id
  • _cq_source_name
  • _cq_sync_time
  • project_id
  • id
  • region
  • name
  • description
  • _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):
gcp_asset_inventory: # This should match the name in your dbt_project.yml
  target: dev
      type: postgres
      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.

Login to CloudQuery #

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

Syncing GCP 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
 name: "gcp" # The source type, in this case, GCP.
 path: "cloudquery/gcp" # The plugin path for handling GCP sources.
 registry: "cloudquery" # The registry from which the GCP plugin is sourced.
 version: "v12.3.2" # The version of the GCP plugin.
 tables: ["gcp_storage_buckets"] # Include any tables that meet your requirements, separated by commas
 destinations: ["postgresql"] # The destination for the data, in this case, PostgreSQL.
   # GCP Spec
   project_ids: ["my-project"] # The name of the GCP project you are working in

kind: destination
 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.

   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>

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