Recurly Source dbt package (Docs)

What does this dbt package do?

Materializes Recurly staging tables which leverages data in the format described by this ERD. These staging tables clean, test, and prepare your Recurly data from Fivetran's connector for analysis by doing the following:
- Name columns for consistency across all packages and easier analysis.
- Adds freshness tests to source data.
- 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 Recurly data through the dbt docs site.
These tables are designed to work simultaneously with our Recurly 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 Recurly connector syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, 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 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 also using the `recurly` transformation package)

If you are not using the Recurly transformation package, include the following recurly_source 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/recurly_source
    version: [">=0.3.0", "<0.4.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define database and schema variables

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

vars:
    recurly_database: your_destination_name
    recurly_schema: your_schema_name

Step 4: Disable models for non-existent sources

Your Recurly connector may not be syncing all tabes that this package references. This might be because you are excluding those tables. If you are not using those tables, you can disable the corresponding functionality in the package by specifying the variable in your dbt_project.yml. By default, all packages are assumed to be true. You only have to add variables for tables you want to disable, like so:

vars:
  recurly__using_credit_payment_history: false # Disable if you do not have the credit_payment_history table
  recurly__using_subscription_add_on_history: false # Disable if you do not have the subscription_add_on_history table
  recurly__using_subscription_change_history: false # Disable if you do not have the subscription_change_history table

(Optional) Step 5: Additional configurations

Expand to view configurations

Passing Through Additional Fields

This package includes all source columns defined in the macros folder. You can add more columns using our pass-through column variables. These variables allow for the pass-through fields to be aliased (alias) and casted (transform_sql) if desired, but not required. Datatype casting is configured via a sql snippet within the transform_sql key. You may add the desired sql while omitting the as field_name at the end and your custom pass-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables:

vars:
    recurly_account_pass_through_columns: 
      - name: "new_custom_field"
        alias: "custom_field"
        transform_sql: "cast(custom_field as string)"
      - name: "another_one"
    recurly_subscription_pass_through_columns:
      - name: "this_field"
        alias: "cool_field_name"

Change the build schema

By default, this package builds the recurly staging models within a schema titled (<target_schema> + _recurly_source) in your destination. If this is not where you would like your recurly staging data to be written to, add the following configuration to your root dbt_project.yml file:

models:
    recurly_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.

vars:
    <default_source_table_name>_identifier: your_table_name

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

Expand to view 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/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 that 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 to learn how to contribute to a dbt package.

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.