SAP Transformation dbt Package (Docs)

📣 What does this dbt package do?

• Produces modeled tables that leverage SAP data from Fivetran's connector and build off the output of our SAP source package.
• Enables you to better understand your SAP data. The package achieves this by performing the following:
  • Brings in essential tables like G/L Account Number attribute (sap__0gl_account_attr) and Master Material data (sap__0material_attr).
  • Adds general ledger models like General Ledger: Balances, Leading Ledger (sap__0fi_gl_10) and Line Items Leading Ledger (sap__0fi_gl_14).
• Generates a comprehensive data dictionary of your source and modeled sap data through the dbt docs site.

The following table provides a detailed list of all models materialized within this package by default.

TIP: See more details about these models in the package's dbt docs site.

🎯 How do I use the dbt package?

Step 1: Prerequisites

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

• At least one Fivetran SAP 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

Include the following sap_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.

- git: https://github.com/fivetran/dbt_sap.git 
  revision: main
  warn-unpinned: false

Do NOT include the sap_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.

Step 3: Define database and schema variables

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

vars:
    sap_database: your_destination_name
    sap_schema: your_schema_name 

(Optional) Step 4: Additional configurations

vars:
    bkpf_mandt_var: 'value1'
    mara_mandt_var: 'value2'
    ska1_mandt_var: 'value3'
    faglflexa_rldnr_var: 'value4'
    faglflext_prctr_var: 'value5'
    faglflext_racct_var: 'value6'
    faglflext_racct_var: 'value7'
    faglflext_rbukrs_var: 'value8'
    faglflext_rclnt_var: 'value9'
    faglflext_rldnr_var: 'value10'
    faglflext_ryear_var: 'value11'

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

vars:
    sap_<default_source_table_name>_identifier: your_table_name 

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

🔍 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/sap_source
      version: [">=0.1.0", "<0.2.0"]

    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.3.0", "<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, 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.
• Submit any questions you have about our packages in our Fivetran dbt community so our Engineering team can provide guidance as quickly as possible!