This package models Twitter Ads data from Fivetran’s connector. It uses data in the format described by this ERD.
Modelslink
This package contains staging models, designed to work simultaneously with our Twitter Ads transform package and our multi-platform Ad Reporting package. The staging models name columns consistently across all packages:
- Boolean fields are prefixed with
is_
orhas_
- Timestamps are appended with
_timestamp
- ID primary keys are prefixed with the name of the table. For example, the campaign table’s ID column is renamed
campaign_id
.
Installation Instructionslink
Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.
Include in your packages.yml
packages:
- package: fivetran/twitter_ads_source
version: [">=0.4.0", "<0.5.0"]
Configurationlink
By default, this package will look for your Twitter Ads data in the twitter_ads
schema of your target database. If this is not where your Twitter Ads data is, please add the following configuration to your dbt_project.yml
file:
...
config-version: 2
vars:
twitter_ads_schema: your_schema_name
twitter_ads_database: your_database_name
Changing the Build Schemalink
By default this package will build the Twitter Ads staging models within a schema titled (<target_schema> + _stg_twitter_ads
) in your target database. If this is not where you would like your Twitter Ads staging data to be written to, add the following configuration to your dbt_project.yml
file:
...
models:
twitter_ads_source:
+schema: my_new_schema_name # leave blank for just the target_schema
Database Supportlink
This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.
Databricks Dispatch Configurationlink
dbt v0.20.0
introduced a new project-level dispatch configuration that enables an “override” setting for all dispatched macros. 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']
Contributionslink
Additional contributions to this package are very welcome! Please create issues
or open PRs against master
. Check out
this post
on the best workflow for contributing to a package.
Resources:link
- Provide feedback on our existing dbt packages or what you’d like to see next
- Have questions, feedback, or need help? Book a time during our office hours using Calendly or email us at solutions@fivetran.com
- Find all of Fivetran’s pre-built dbt packages in our dbt hub
- Learn how to orchestrate your models with Fivetran Transformations for dbt Core™
- Learn more about Fivetran overall in our docs
- Check out Fivetran’s blog
- Learn more about dbt in the dbt docs
- Check out Discourse for commonly asked questions and answers
- Join the chat on Slack for live discussions and support
- Find dbt events near you
- Check out the dbt blog for the latest news on dbt’s development and best practices