This is a template to create connectors for The Tuva Project on version 0.14.x.

This template includes a general project structure for connectors for The Tuva Project, including the data models that The Tuva Project Package expects as inputs, some data tests and column-level documentation, and some notes on how to build on this connector in structuring your own projects.

What is a connector?

Running a correctly-built connector prepares data to run through The Tuva Project dbt package. In effect, connectors help map raw data sources to the Tuva Data Model.

Connector Project Structure

As a general pattern, connectors are roughly 1:1 with raw data sources, because each raw data source often has its own unique challenges. The typical workflow and project structure for mapping raw data to the Tuva Data Model within a connector is:

• staging layer: source() raw data and map it to the Tuva Data Model
• int layer: handle any consequential transformations, including Adjustments, Denials, and Reversals (ADR) for claims and deduplication.
• final layer: data is ready to run through The Tuva Project—the models in this layer are expected by The Tuva Project Package.

Running a connector

When you've completed mapping, and you're ready to run the connector without running the whole Tuva Project, you can build and test all models by running:

dbt build --full-refresh -s tag:input_layer

Tuva Resources:

• The Tuva Project docs
• The Tuva Project dbt docs
• The Tuva Project package

dbt Resources:

• Learn more about dbt in the 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 blog for the latest news on dbt's development and best practices