AWS Data Resilience (Backup) Transformation documentation

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AWS Data Resilience (Backup)

Provides a high level view on your AWS data resilience posture (via AWS Backup)

Publisher

cloudquery

Latest version

v2.1.6

Type

Transformation

Published

October 14, 2024

CloudQuery + dbt AWS Data Resilience (AWS Backup)

Overview #

Welcome to our free edition of the AWS Data Resilience package, a solution that works on top of the CloudQuery framework. This package offers automated insight into your AWS Backup posture in your AWS environment. Currently, this package only supports usage with PostgreSQL databases.

We recommend using this transformation with our AWS Data Resilience Dashboard

Models #

aws_data_resilience__overview: AWS Backup overview for aws_dynamodb_tables, aws_ec2_instances and aws_s3_buckets, available for PostgreSQL.

Columns

account_id
resource_arn
tags
last_backup_time
resource_type

Requirements #

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_data_resilience: # 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 PostgreSQL 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_dynamodb_tables","aws_ec2_instances","aws_s3_buckets"] # 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.