Amazon Ads Source dbt Package (Docs)
- Materializes Amazon Ads staging tables, which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Amazon Ads data from Fivetran's connector for analysis by doing the following:
- Names columns for consistency across all packages and for easier analysis
- Adds freshness tests to source data
- Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
- Generates a comprehensive data dictionary of your Amazon Ads data through the dbt docs site.
- These tables are designed to work simultaneously with our Amazon Ads transformation package.
To use this dbt package, you must have the following:
- At least one Fivetran Amazon_ads connector syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
Include the following amazon_ads_source package version in your packages.yml
file.
TIP: Check dbt Hub for the latest installation instructions, or read dbt's Package Management documentation for more information on installing packages.
packages:
- package: fivetran/amazon_ads_source
version: [">=0.1.0", "<0.2.0"]
By default, this package runs using your destination and the amazon_ads
schema. If this is not where your Amazon_ads data is (for example, if your Amazon_ads schema is named amazon_ads_fivetran
), add the following configuration to your root dbt_project.yml
file:
vars:
amazon_ads_database: your_database_name
amazon_ads_schema: your_schema_name
Your Amazon Ads connector may not sync every table that this package expects. If you do not have the PORTFOLIO_HISTORY
table synced, add the following variable to your root dbt_project.yml
file:
vars:
amazon_ads__portfolio_history_enabled: False # Disable if you do not have the portfolio table. Default is True.
Expand for configurations
By default, this package will select clicks
, impressions
, and cost
from the source reporting tables to store into the staging models. If you would like to pass through additional metrics to the staging models, add the following configurations to your dbt_project.yml
file. These variables allow the pass-through fields to be aliased (alias
) if desired, but not required. Use the following format for declaring the respective pass-through variables:
Note Ensure you exercised due diligence when adding metrics to these models. The metrics added by default (clicks, impressions, and cost) have been vetted by the Fivetran team maintaining this package for accuracy. There are metrics included within the source reports, for example, metric averages, which may be inaccurately represented at the grain for reports created in this package. You want to ensure whichever metrics you pass through are indeed appropriate to aggregate at the respective reporting levels provided in this package.
vars:
amazon_ads__campaign_passthrough_metrics:
- name: "new_custom_field"
alias: "custom_field"
amazon_ads__ad_group_passthrough_metrics:
- name: "unique_string_field"
alias: "field_id"
amazon_ads__advertised_product_passthrough_metrics:
- name: "new_custom_field"
alias: "custom_field"
- name: "a_second_field"
amazon_ads__targeting_keyword_passthrough_metrics:
- name: "this_field"
amazon_ads__search_term_ad_keyword_passthrough_metrics:
- name: "unique_string_field"
alias: "field_id"
By default, this package will build the Amazon_ads staging models within a schema titled (<target_schema> + amazon_ads_source
) in your destination. If this is not where you would like your Amazon Ads staging data to be written, add the following configuration to your root dbt_project.yml
file:
models:
amazon_ads_source:
+schema: my_new_schema_name # leave blank for just the target_schema
If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:
IMPORTANT: See this project's
dbt_project.yml
variable declarations to see the expected names.
vars:
amazon_ads_<default_source_table_name>_identifier: your_table_name
Expand for more details
Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core™ setup guides.
This dbt package is dependent on the following dbt packages. Be aware that these dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.
IMPORTANT: If you have any of these dependent packages in your own
packages.yml
file, we highly recommend that you remove them from your rootpackages.yml
to avoid package version conflicts.
packages:
- package: fivetran/fivetran_utils
version: [">=0.4.0", "<0.5.0"]
- package: dbt-labs/dbt_utils
version: [">=1.0.0", "<2.0.0"]
The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend that 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.
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. Check out this dbt Discourse article to learn how to contribute to a dbt package!
- If you have questions or want to reach out for help, refer to the GitHub Issue section to find the right avenue of support for you.
- If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.
- Have questions or just want to say hi? Book a time during our office hours on Calendly or email us at solutions@fivetran.com.