Twitter Ads Transformation dbt Package (Docs)

What does this dbt package do?

Produces modeled tables that leverage Twitter Ads data from Fivetran's connector in the format described by this ERD and builds off the output of our Twitter Ads source package.
Enables you to better understand the performance of your ads across varying grains:
Providing an account, campaign, line item (ad group), keyword, promoted tweet, and utm/url level reports.
Materializes output models designed to work simultaneously with our multi-platform Ad Reporting package.
Generates a comprehensive data dictionary of your source and modeled Twitter Ads data through the dbt docs site.

The following table provides a detailed list of all tables materialized within this package by default.

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

Table	Description
twitter_ads__account_report	Each record in this table represents the daily performance at the account and ad-placement level.
twitter_ads__campaign_report	Each record in this table represents the daily performance of ads at the account, campaign, and ad-placement level.
twitter_ads__line_item_report	Each record in this table represents the daily performance of ads at the campaign, account, line item, and ad-placement level. Line items are essentially ad groups in other platforms.
twitter_ads__keyword_report	Each record in this table represents the daily performance of a keyword at the account, campaign, line item, keyword, and ad-placement level.
twitter_ads__promoted_tweet_report	Each record in this table represents the daily performance of ads at the account, campaign, line item, promoted tweet, and ad-placement level.
twitter_ads__url_report	Each record in this table represents the daily performance of ads at the account, campaign, line item, promoted tweet, url, and ad-placement 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 Twitter 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 below (or a variation of the below) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

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

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

If you are not using the downstream Ad Reporting combination package, include the following Twitter package version in your packages.yml file:

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages

# packages.yml
packages:
  - package: fivetran/twitter_ads
    version: [">=0.8.0", "<0.9.0"] # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the twitter_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 runs using your destination and the twitter_ads schema. If this is not where your Twitter Ads data is (for example, if your twitter schema is named twitter_fivetran), add the following configuration to your root dbt_project.yml file:

# dbt_project.yml
vars:
    twitter_ads_schema: your_schema_name
    twitter_ads_database: your_destination_name

Step 4: Disabling Keyword Models

This package takes into consideration that not every Twitter Ads account tracks keyword performance, and allows you to disable the corresponding functionality by adding the following variable configuration:

# dbt_project.yml
vars:
    twitter_ads__using_keywords: False # Default = true

(Optional) Step 5: Additional configurations

Expand/Collapse details

Union multiple connectors

If you have multiple twitter 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 twitter_ads_union_schemas OR twitter_ads_union_databases variables (cannot do both) in your root dbt_project.yml file:

vars:
    twitter_ads_union_schemas: ['twitter_usa','twitter_canada'] # use this if the data is in different schemas/datasets of the same database/project
    twitter_ads_union_databases: ['twitter_usa','twitter_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.

Customizing Types of Conversions

The package will include conversion metrics provided to the following variables in each model.

Variable	Definition	Default Values
`twitter_ads__conversion_fields`	Which fields should be included in calculating total number of conversions.	`conversion_purchases_metric`, `conversion_custom_metric`
`twitter_ads__conversion_sale_amount_fields`	Which `*_sale_amount` fields should be included in calculating the total value of conversions.	`conversion_purchases_sale_amount`, `conversion_custom_sale_amount`

In addition, the fields included in var('twitter_ads__conversion_fields') will be summed up into a total_conversions field, and the fields included in var('twitter_ads__conversion_sale_amount_fields') will be summed up into a total_conversions_sale_amount field.

By default, the data models include purchases and custom conversion events in both variables. However, you can configure each to include any types of conversions available in the Twitter Ads source *_report tables:

# dbt_project.yml
vars:
    twitter_ads__conversion_fields:
        - conversion_purchases_metric
        - conversion_sign_ups_metric
        - mobile_conversion_payment_info_additions_post_engagement
        - mobile_conversion_add_to_wishlists_post_engagement
        - mobile_conversion_add_to_carts_post_engagement
        - mobile_conversion_checkouts_initiated_post_engagement
        - <any conversion field you want to include>
    twitter_ads__conversion_sale_amount_fields: 
        - conversion_purchases_sale_amount
        - conversion_sign_ups_sale_amount
        - <any conversion value/sale amount field you want to include>

We recommend using the same types of conversion events for twitter_ads__conversion_fields and twitter_ads__conversion_sale_amount_fields so that total_conversions and total_conversions_sale_amount properly map onto each other, but this is not required. We chose to split conversions and conversion values into 2 distinct variables due to the N:1 relationship beteen conversions and conversion value fields.

Passing Through Additional Metrics

Besides the above conversion fields, this package by default will select clicks, url_clicks, impressions, spend (calculated from billed_charge_local_micro), and spend_micro (aliased from billed_charge_local_micro) 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 below configurations to your dbt_project.yml file. These variables allow for the pass-through fields to be aliased (alias) if desired, but not required. Use the below format for declaring the respective pass-through variables:

IMPORTANT: Make sure to exercise due diligence when adding metrics to these models. The metrics added by default (taps, impressions, and spend) have been vetted by the Fivetran team, maintaining this package for accuracy. There are metrics included within the source reports, such as metric averages, which may be inaccurately represented at the grain for reports created in this package. You must ensure that whichever metrics you pass through are appropriate to aggregate at the respective reporting levels in this package.

# dbt_project.yml
vars:
    twitter_ads__campaign_report_passthrough_metrics: 
        - name: "new_custom_field"
          alias: "custom_field"
    twitter_ads__line_item_report_passthrough_metrics: 
        - name: "unique_int_field"
          alias: "field_id"
    twitter_ads__line_item_keywords_report_passthrough_metrics: 
        - name: "that_field"
    twitter_ads__promoted_tweet_report_passthrough_metrics: 
        - name: "that_field"

Changing the Build Schema

By default this package will build the Twitter Ads staging models within a schema titled (<target_schema> + _twitter_ads_source) and the Twitter Ads final models with a schema titled (<target_schema> + _twitter_ads) in your target database. If this is not where you would like your modeled Twitter Ads data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml
models:
  twitter_ads:
    +schema: my_new_schema_name # leave blank for just the target_schema
  twitter_ads_source:
    +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:

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

# dbt_project.yml
vars:
    twitter_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. 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 root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/twitter_ads_source
      version: [">=0.8.0", "<0.9.0"]
    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]
    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]
    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

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 a few different questions we came across during development. We've consolidated significant choices we made in the DECISIONLOG.md, and will continue to update 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, see 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.