Amazon Ads dbt Package (Docs)

What does this dbt package do?

Produces modeled tables that leverage Amazon Ads data from Fivetran's connector in the format described by this ERD and builds off the output of our Amazon Ads source package.
Provides insight into your ad performance across the following grains:
- Account, portfolio, campaign, ad group, ad, keyword, and search term
Materializes output models designed to work simultaneously with our multi-platform Ad Reporting package.
Generates a comprehensive data dictionary of your source and modeled Amazon Ads data through the dbt docs site.

The following table lists all tables that are materialized within this package by default.

TIP: See more details about these tables in the package's dbt docs site.

Table	Description
amazon_ads__account_report	Each record in this table represents the daily performance at the account level.
amazon_ads__portfolio_report	Each record in this table represents the daily performance at the portfolio level.
amazon_ads__campaign_report	Each record in this table represents the daily performance at the campaign level.
amazon_ads__ad_group_report	Each record in this table represents the daily performance at the ad group level.
amazon_ads__search_report	Each record in this table represents the daily performance at the search term level.
amazon_ads__keyword_report	Each record in this table represents the daily performance at the keyword level.
amazon_ads__ad_report	Each record in this table represents the daily performance at the ad level.

How do I use the dbt package?

Step 1: Prerequisites

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.

Databricks Dispatch Configuration

If you are using a Databricks destination with this package, you will need to add the following dispatch configuration (or a variation) within your dbt_project.yml. This is necessary to ensure that this package searches for macros in the dbt-labs/spark_utils package before searching the dbt-labs/dbt_utils package.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Step 2: Install the package (skip if also using the `ad_reporting` combo package)

Include the following amazon_ads package version in your packages.yml file if you are not also using the upstream Ad Reporting combination package:

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
    version: [">=0.4.0", "<0.5.0"] # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the amazon_ads_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.

Step 3: Define database and schema variables

By default, this package uses your destination and the amazon_ads schema. If your Amazon Ads data is in a different database or schema (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_destination_name
    amazon_ads_schema: your_schema_name

Step 4: Disable models for non-existent sources

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.

(Optional) Step 5: Additional configurations

Expand/Collapse details

Union multiple connectors

If you have multiple amazon_ads connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either the amazon_ads_union_schemas OR amazon_ads_union_databases variables (cannot do both) in your root dbt_project.yml file:

vars:
    amazon_ads_union_schemas: ['amazon_ads_usa','amazon_ads_canada'] # use this if the data is in different schemas/datasets of the same database/project
    amazon_ads_union_databases: ['amazon_ads_usa','amazon_ads_canada'] # use this if the data is in different databases/projects but uses the same schema name

NOTE: The native source.yml connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined source.yml.

To connect your multiple schema/database sources to the package models, follow the steps outlined in the Union Data Defined Sources Configuration section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.

Passing Through Additional Metrics

By default, this package will select clicks, impressions, cost, purchases_30_d, and sales_30_d from the source reporting tables to store into the staging and end models. If you would like to pass through additional metrics to the package 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 Make sure to exercise due diligence when adding metrics to these models. The metrics added by default 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"
        transform_sql: "coalesce(unique_string_field, 'NA')"
    amazon_ads__advertised_product_passthrough_metrics: 
      - name: "new_custom_field"
        alias: "custom_field"
        transform_sql: "coalesce(custom_field, 'NA')" # reference alias in transform_sql if aliasing 
      - 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"

Changing the Build Schema

By default, this package will build the Amazon Ads staging models (11 views, 11 tables) within a schema titled (<target_schema> + amazon_ads_source) and the Amazon Ads intermediate (1 view) and end models (7 tables) within a schema titled (<target_schema> + amazon_ads) in your destination. If this is not where you would like your Amazon Ads staging and modeling 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
    amazon_ads:
      +schema: my_new_schema_name # leave blank for just the target_schema

Change the source table references

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. This is not available when running the package on multiple unioned connectors.

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

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

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.

Does this package have dependencies?

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 these packages, refer to the dbt hub site.

IMPORTANT: If you have any of the dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/amazon_ads_source
      version: [">=0.4.0", "<0.5.0"]

    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

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.

Opinionated Decisions

In creating this package, which is meant for a wide range of use cases, we had to take opinionated stances on certain decisions, such as logic choices or column selection. Therefore, we have documented significant choices in the DECISIONLOG.md and will continue to update this as the package evolves. We are always open to and encourage feedback on these choices and the package in general.

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. Check out this dbt Discourse article on the best workflow for contributing to a package.

Contributors

We thank everyone who has taken the time to contribute. Each PR, bug report, and feature request has made this package better and is truly appreciated.

A special thank you to Seer Interactive, who we closely collaborated with to introduce native conversion support to our Ad packages.

Are there any resources available?

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.