SAP Transformation dbt Package (Docs)

What does this dbt package do?

Provides recreations of the SAP extractor models to enable you to better understand your SAP data. The package achieves this by performing the following:
- Brings in essential master attribute tables like Company Code (sap__0comp_code_attr), Customer Master (sap__0customer_attr), Employee (sap__0employee_attr), G/L Account Number (sap__0gl_account_attr), Material Data (sap__0material_attr), and Vendor Number (sap__0vendor_attr).
- Brings in general ledger models like General Ledger: Balances, Leading Ledger (sap__0fi_gl_10) and Line Items Leading Ledger (sap__0fi_gl_14).
- Brings in master text models like Company Code (sap__0comp_code_text), Company (sap__0company_text), and Vendor (sap__0vendor_text).
Produces modeled tables that leverage SAP data from Fivetran's SAP connectors, like LDP SAP Netweaver, HVA SAP or SAP ERP on HANA and build off the output of our SAP source package.
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 tables materialized within this package by default.

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

Table	Description
sap__0comp_code_attr	This model is used for loading company code attributes, extracting from the t001 data source.
sap__0comp_code_text	This model is used for loading company code text information, extracting from the t001 data source.
sap__0company_text	This model is used for loading customer text data, extracting from the t880 data source.
sap__0customer_attr	This model is used for loading customer master data, originating from the kna1 source.
sap__0employee_attr	This model contains information that concerns the employee's work relationship, extracting master data from the personnel administration tables.
sap__0fi_gl_10	This model extracts the transaction figures from the leading ledger in the new General Ledger.
sap__0fi_gl_14	This model extracts line items from the leading ledger in new General Ledger Accounting.
sap__0gl_account_attr	This model is used for loading G/L Account Number master data, originating from the ska1 source.
sap__0material_attr	This model is used to display material attribute information, originating from the mara source.
sap__0vendor_attr	This model is used to display vendor attributes, originating from the lfa1 source.
sap__0vendor_text	This model is used to display vendor text, originating from the lfa1 source.

How do I use the dbt package?

Step 1: Prerequisites

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

At least one Fivetran of the following SAP connectors:
Within the connector, syncing the following respective tables into your destination:
- bkpf
- bseg
- faglflexa
- faglflext
- kna1
- lfa1
- mara
- pa0000
- pa0001
- pa0007
- pa0008
- pa0031
- ska1
- t001
- t503
- t880
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 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/sap
    version: [">=0.1.0", "<0.2.0"]

It's our recommendation that you 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

Expand to view details

Filter the data you bring in with field variable conditionals

By default, these models are set to bring in all your data from SAP, but you may be interested in bringing in only a smaller sample of data given the relative size of the SAP source tables.

We have set up where conditions in our data to allow you to bring in only the data you need to run in. Configure the below variables in your dbt_project.yml to bring in only the rows that return these values in the fields specified.

vars:
    bkpf_mandt_var: 'value1' # The client field in the `sap__0fi_gl_14` model, this filter allows you to parse down to one client's records.
    kna1_mandt_var: 'value2' # The client field in the `sap__0customer_attr` model, this filter allows you to parse down to one client's records.
    lfa1_mandt_var: 'value3' # The client field in the `sap__0vendor_attr` model, this filter allows you to parse down to one client's records.
    mara_mandt_var: 'value4' # The client field in the `sap__0vendor_attr` model, this filter allows you to parse down to one client's records.
    ska1_mandt_var: 'value5' # The client field in the `sap__0gl_account_attr` model, this filter allows you to parse down to one client's records.
    t001_mandt_var: 'value6' # The client field in the `sap__0comp_code_attr` model, this filter allows you to parse down to one client's records.
    faglflexa_rldnr_var: 'value7' # The ledger field in the `sap__0fi_gl_14` model, this filter allows you to parse down to one ledger's records.
    faglflext_rbukrs_var: 'value8' # The company code field in the `sap__0fi_gl_10` model, this filter allows you to parse down to one company's records.
    faglflext_rclnt_var: 'value9' # The client in the `sap__0fi_gl_10` model, this filter allows you to parse down to one client's records.
    faglflext_rldnr_var: 'value10' # The ledger account field in the `sap__0fi_gl_10` model, this filter allows you to parse down to one ledger account's records.
    faglflext_ryear_var: 'value11' # The fiscal year in the `sap__0fi_gl_10` model, this filter allows you to parse down to one fiscal year.

Change the build schema

By default, this package builds the SAP staging models within a schema titled (<target_schema> + stg_sap) and the SAP final models within a schema titled (<target_schema> + _sap) in your target database. If this is not where you would like your modeled sap data to be written to, add the following configuration to your root dbt_project.yml file:

models:
    sap:
      +schema: my_new_schema_name # leave blank for just the target_schema
    sap_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:
    sap_<default_source_table_name>_identifier: your_table_name

(Optional) Step 5: 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/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, 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.