Twilio Source dbt Package (docs)

📣 What does this dbt package do?

• Materializes Twilio staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Twilio data from Fivetran's connector for analysis by doing the following:
  • Adds descriptions to tables and columns that are synced using Fivetran
  • Models staging tables, which will be used in our transform package
  • 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 source and modeled Twilio data through the dbt docs site.
• These tables are designed to work simultaneously with our Twilio transformation package.

🎯 How do I use the dbt package?

Step 1: Prerequisites

To use this dbt package, you must have the following:

• At least one Fivetran Twilio 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

Include the following Twilio 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:
  - package: fivetran/twilio_source
    version: [">=0.2.0", "<0.3.0"]

Step 3: Define database and schema variables

By default, this package runs using your destination and the twilio schema of your target database. If this is not where your Twilio data is (for example, if your Twilio schema is named twilio_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
  twilio_database: your_database_name
  twilio_schema: your_schema_name 

Step 4: Enabling/Disabling Models

Your Twilio connector might not sync every table that this package expects, for example if you are not using the Twilio messaging service feature. If your syncs exclude certain tables, it is either because you do not use that functionality in Twilio or have actively excluded some tables from your syncs. In order to enable or disable the relevant tables in the package, you will need to add the following variable(s) to your dbt_project.yml file.

By default, all variables are assumed to be true.

vars:
  using_twilio_call: False # Disable this if not using call
  using_twilio_messaging_service: False # Disable this if not using messaging_service

(Optional) Step 5: Additional configurations

Expand/Collapse details

Changing the Build Schema

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

models:
    twilio_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 what the package expects (but is in the same schema and database as the other tables), 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:
    twilio_<default_source_table_name>_identifier: "your_table_name"

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

Expand for 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. Please 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 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"]

    - 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.

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!

🏪 Are there any resources available?

• If you have questions or want to reach out for help, please 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.