Aws Cloud Cost dbt Package

This dbt package transforms data from Fivetran's Aws Cloud Cost connector into analytics-ready tables.

Resources

Number of materialized models¹: 5
Connector documentation
- AWS Cloud Cost connector documentation
- AWS Cost & Usage Report
dbt package documentation

What does this dbt package do?

This package enables you to monitor and investigate cost & usage of different AWS services across your organizations. It creates enriched models with metrics focused on billing, pricing, line item buckets, products, reservations, and savings plans.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_aws_cloud_cost

Final output tables

By default, this package materializes the following final tables:

Table	Description
aws_cloud_cost__daily_overview	Provides a comprehensive daily view of AWS costs and usage across all services, accounts, and billing dimensions from the Standard Cost & Usage Report (2.0) to monitor spending patterns, optimize costs, and track reservations and savings plans. Example Analytics Questions: What are our daily AWS costs by billing account, product, or region? How much are we saving through reservations and savings plans compared to on-demand pricing? Which line item buckets or pricing categories are driving the biggest spending increases?
aws_cloud_cost__daily_product_report	Breaks down daily AWS spending by individual product and service for each account to identify which services consume the most budget and where optimization opportunities exist. Example Analytics Questions: Which AWS products or services drive the highest costs by account? How are product-specific costs trending day-over-day or month-over-month? Where should we focus cost optimization efforts based on product spending patterns?
aws_cloud_cost__daily_instance_report	Analyzes daily costs and usage for EC2 instances across all accounts to optimize compute spending and identify underutilized or oversized instances. Example Analytics Questions: Which EC2 instance types generate the highest daily costs by account? How does EC2 instance usage and spending vary across different accounts or environments? Are there opportunities to right-size instances based on usage patterns?

¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

Prerequisites

To use this dbt package, you must have the following:

At least one Fivetran AWS Cloud Cost connection syncing data into your destination.
A BigQuery, Snowflake, Redshift, Databricks, or PostgreSQL destination.

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

To add the package in the Fivetran dashboard, follow our Quickstart guide.
To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

DISCLAIMER: This package transforms source data of potentially very high volumes. Please be aware of the size of your dataset(s) and take this into consideration when configuring the frequency with which you will orchestrate the package models. See Additional configurations for tools to mitigate compute and storage costs.

How is this package maintained and can I contribute?

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

Contributions

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.