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Official
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
Category
Cloud Infrastructure
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
andaws_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.
Login to CloudQuery #
Because this policy uses premium features and tables you must login to your cloudquery account using
cloudquery login
in your terminalSyncing 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.