Sage Intacct Source dbt Package (Docs)

What does this dbt package do?

• Materializes Sage Intacct staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Sage Intacct data from Fivetran's connector for analysis by doing the following:
  • Names columns for consistency across all packages and for 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 sage_intacct data through the dbt docs site.
• These tables are designed to work simultaneously with our Sage Intacct 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 Sage Intacct 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 (skip if also using the sage_intacct transformation package)

If you are not using the Sage Intacct transformation package, include the following sage_intacct_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/sage_intacct_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 sage_intacct schema. If this is not where your Sage Intacct data is (for example, if your Sage Intacct schema is named sage_intacct_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    sage_intacct_database: your_database_name
    sage_intacct_schema: your_schema_name 

(Optional) Step 4: Additional configurations

Passthrough Columns

This package allows users to add additional columns to the stg_sage_intacct__gl_account table. Columns passed through must be present in the upstream source tables. See below for an example of how the passthrough columns should be configured within your dbt_project.yml file.

# dbt_project.yml

...
vars:
  sage_intacct_source:
    sage_account_pass_through_columns: ['new_custom_field', 'custom_field_2']
    sage_gl_pass_through_columns: ['custom_field_3', 'custom_field_4']

Disabling and Enabling Models

When setting up your Sage Intacct connection in Fivetran, it is possible that not every table this package expects will be synced. This can occur because you either don't use that functionality in Sage or have actively decided to not sync some tables. In order to disable the relevant functionality in the package, you will need to add the relevant variables.

By default, all variables are assumed to be true. You only need to add variables for the tables you would like to disable:

# dbt_project.yml

config-version: 2

vars:
    sage_intacct__using_invoices: false                 # default is true
    sage_intacct__using_bills: false                    # default is true

Change the Build Schema

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

# dbt_project.yml

...
models:
    sage_intacct_source:
      +schema: my_new_schema_name # leave blank for just the target_schema

(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™

vars:
    sage_intacct_<default_source_table_name>_identifier: your_table_name

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.

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"]

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.