SAP Transformation dbt Package (Docs)

What does this dbt package do?

Recreates common SAP extractor reports and provides a star schema for analyzing sales and purchase orders.
- 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).
- Provides sales and procurement models including facts and dimensions for purchase and sales orders.
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.

| sap__dim_customer | Represents customer dimension data from the kna1 source to support dimensional reporting. SAP field names are mapped to English readable column names. | | sap__dim_material | Provides enriched material and material type data by combining attributes from the mara, makt, t134, and t134t sources to support dimensional reporting. SAP field names are mapped to English readable column names. | | sap__dim_plant | Delivers plant-level dimension data from the t001w source to support dimensional reporting. SAP field names are mapped to English readable column names. | | sap__dim_purchasing_order | Provides enriched purchase order data by combining attributes from the ekko, dd07l, dd07t, t024, and t161 sources to support dimensional reporting. SAP field names are mapped to English readable column names. | | sap__dim_purchasing_organization | Provides enriched purchasing organization data by combining attributes from the t024e, t024et, and related sources to support dimensional reporting. SAP field names are mapped to English readable column names. | | sap__dim_rejection_reason | Provides enriched sales rejection reason data by combining attributes from the tvag and tvagt sources to support dimensional reporting. SAP field names are mapped to English readable column names. | | sap__dim_vendor | Represents vendor dimension data from the lfa1 source to support dimensional reporting. SAP field names are mapped to English readable column names. | | sap__fact_purchasing_order | Consolidates purchase order fact data from the ekbe, eket, ekko, ekpo, and t001w sources, representing transactional procurement activity across line items and orders. SAP field names are mapped to English readable column names. | | sap__fact_sales_order | Contains fact-level sales order data, integrating records from vbak, vbap, vbuk, and vbup sources to provide visibility into sales transaction performance. SAP field names are mapped to English readable column names. |

Materialized Models

Each Quickstart transformation job run materializes 46 models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

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 connection:
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: [">=0.2.0", "<0.3.0"]

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/collapse details

Disable individual sources

All source tables are enabled by default, but you can disable any of them by setting their sap_using_* variable to false in your dbt_project.yml. Example usage:

vars:
    sap_using_vbak: false # default is true.
    sap_using_vbap: false # default is true.
    sap_using_ekko: false # default is true.
    # ...additional sap_using_* variables

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:
    sales_and_procurement_mandt_var: ['100', '200', '300', '800'] # This sets the filter used in the sales_and_procurement models. The default is '800', but a list of allowable values can be passed.
    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

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/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.
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