Twilio dbt Package

This dbt package transforms data from Fivetran's Twilio connector into analytics-ready tables.

Resources

Number of materialized models¹: 20
Connector documentation
- Twilio connector documentation
- Twilio ERD
dbt package documentation

What does this dbt package do?

This package enables you to produce modeled tables that leverage Twilio data. It creates enriched models with metrics focused on messaging information and account-level aggregations.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_twilio

Final output tables

By default, this package materializes the following final tables:

Table	Description
twilio__message_enhanced	Tracks details for every SMS and MMS message sent or received through Twilio including status, direction, content, pricing, and delivery information to analyze messaging activity and performance. Example Analytics Questions: What is the delivery success rate by message status (sent, delivered, failed) and direction (inbound vs outbound)? Which phone numbers or messaging campaigns generate the most messages and highest costs? What are the most common error codes and messages for failed deliveries?
twilio__number_overview	Provides aggregate messaging metrics for each phone number including total messages by status, inbound/outbound volumes, and total spend to understand performance at the number level. Example Analytics Questions: Which phone numbers send or receive the most messages? What are the delivery rates (delivered vs failed messages) and total costs by phone number? How do inbound versus outbound message volumes compare across phone numbers?
twilio__account_overview	Summarizes total messaging activity and costs across all Twilio accounts to monitor overall communication volume, spending, and performance. Example Analytics Questions: What is our total messaging volume and spend across all accounts? How are messaging costs and volumes trending over time by account? Which accounts have the highest message volumes or spending?

¹ Each Quickstart transformation job run materializes these 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.

Prerequisites

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

At least one Fivetran Twilio connection syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

To add the package in the Fivetran dashboard, follow our Quickstart guide.
To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

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. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.