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.
• 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	Details
amazon_ads__account_report	Represents daily performance aggregated at the account level, including spend, clicks, impressions, and conversions. Example Analytics Questions: How does performance compare across different accounts by account manager? Are currency fluctuations affecting results across markets?
amazon_ads__ad_group_report	Represents daily performance at the ad group level, including spend, clicks, impressions, and conversions. Example Analytics Questions: Which ad groups have the strongest engagement relative to their budget? Do certain ad groups dominate impressions within a campaign? Are new ad groups ramping up as expected after launch?
amazon_ads__ad_report	Represents daily performance at the individual ad level, including spend, clicks, impressions, and conversions. Example Analytics Questions: Which ad creatives are driving the lowest cost per click? Do expanded text ads perform better than responsive search ads? How do performance trends change after refreshing ad copy?
amazon_ads__campaign_report	Represents daily performance aggregated at the campaign level, including spend, clicks, impressions, and conversions. Example Analytics Questions: Which campaigns are most efficient in terms of cost per conversion? Are paused or limited-status campaigns still accruing impressions? Which campaigns contribute most to overall spend or conversions?
amazon_ads__keyword_report	Represents daily performance at the keyword level, enriched with account, campaign, ad group, and criterion context. Includes metrics such as spend, clicks, impressions, and conversions. Example Analytics Questions: Which keywords are driving the highest quality traffic at the lowest cost? Are branded vs. non-branded keywords performing differently? Should underperforming keywords be reallocated to different match types?
amazon_ads__portfolio_report	Represents daily performance at the portfolio level, including spend, clicks, impressions, and conversions. Example Analytics Questions: Which portfolios are delivering the best return on ad spend? How do different portfolio strategies compare in terms of performance? What are the spending trends across my portfolio segments?
amazon_ads__search_report	Represents daily performance at the search term level, enriched with account, campaign, and ad group context. Includes metrics such as spend, clicks, impressions, and conversions. Example Analytics Questions: What new search terms are emerging that I should add as keywords? Which irrelevant search terms should be added as negatives to reduce wasted spend? Are there seasonal shifts in search terms driving conversions?

Many of the above reports are now configurable for visualization via Streamlit. Check out some sample reports here.

Example Visualizations

Curious what these tables can do? The Amazon Ads models provide advertising performance data that can be visualized to track key metrics like spend, impressions, click-through rates, conversion rates, and return on ad spend across different campaign structures and time periods. Check out example visualizations in the Fivetran Ad Reporting Streamlit App, and see how you can use these tables in your own reporting. Below is a screenshot of an example dashboard; explore the app for more.

Materialized Models

Each Quickstart transformation job run materializes 30 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.

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 connection 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: [">=1.1.0", "<1.2.0"] # we recommend using ranges to capture non-breaking changes automatically

All required sources and staging models are now bundled into this transformation package. Do not include fivetran/amazon_ads_source in your packages.yml since this package has been deprecated.

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 connection 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 connections

If you have multiple amazon_ads connections 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:
      +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.
      staging:
        +schema: my_new_schema_name # Leave +schema: blank to use the default 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 connections.

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/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.