SAP Procure to Pay accelerator

The SAP accelerator for the procure-to-pay process is a sample implementation of the SAP Table Batch Source feature in Cloud Data Fusion. The SAP Procure to Pay accelerator helps you get started when you create your end-to-end procure-to-pay process and analytics. It includes sample Cloud Data Fusion pipelines that you can configure to perform the following tasks:

• Connect to your SAP data source.
• Perform transformations on your data in Cloud Data Fusion.
• Store your data in BigQuery.
• Set up analytics in Looker. This includes dashboards and an ML model, where you can define the key performance indicators (KPIs) for your procure-to-pay process.

This guide describes the sample implementation, and how you can get started with your configurations.

The accelerator is available in Cloud Data Fusion environments running in version 6.4.0 and above.

Before you begin

1. Sign in to your Google Account.

   If you don't already have one, sign up for a new account.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. Enable the Cloud Data Fusion and BigQuery APIs.

   Enable the APIs

5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

6. Make sure that billing is enabled for your Google Cloud project.

7. Enable the Cloud Data Fusion and BigQuery APIs.

   Enable the APIs

8. Download the SAP Table Batch Source.
9. You must have access to a Looker instance and have the marketplace labs feature turned on to install the Looker Block. You can request a free trial to get access to an instance.

Required skills

Setting up the SAP Procure to Pay accelerator requires the following skills:

• Expertise in SAP on-premises ERP systems and configuration
• Familiarity with the Cloud Data Fusion
• Familiarity with BigQuery
• Familiarity with Looker
• Familiarity with Identity and Access Management (IAM) service accounts and access control
• Familiarity with data analytics, including writing SQL queries
• Familiarity with Kimball's dimensional data model

Required users

The configurations described on this page require changes in your SAP system and in Google Cloud. You need to work with the following users of those systems to perform the configurations:

User type	Description
SAP admin	Administrator for your SAP system who can access the SAP service site for downloading software.
SAP user	An SAP user who is authorized to connect to an SAP system.
GCP admin	Administrator who controls IAM access for your organization, who creates and deploys service accounts and grants permissions for Cloud Data Fusion, BigQuery, and Looker.
Cloud Data Fusion user	Users who are authorized to design and run data pipelines in Cloud Data Fusion.
BigQuery Data Owner	Users who are authorized to create, view, and modify BigQuery datasets.
Looker Developer	These users can install the Looker Block through the Marketplace. They must have develop, manage_model, and deploy permissions.

Required IAM roles

In the accelerator's sample implementation, the following IAM roles are required. You might need additional roles if your project relies on other Google Cloud services.

• BigQuery Admin (roles/bigquery.admin)
• BigQuery Data Owner (roles/bigquery.dataOwner)
• Storage Object Viewer (roles/storage.objectViewer)
• Cloud Data Fusion Runner (roles/datafusion.runner) needs to be granted to the Dataproc service account

Process overview

You can implement the accelerator in your project by following these steps:

1. Configure the SAP ERP system and install the SAP transport provided.
2. Set up your Cloud Data Fusion environment to use the SAP Table Batch Source plugin.
3. Create datasets in BigQuery. The accelerator provides sample datasets for staging, dimensional, and fact tables.
4. Configure the sample Cloud Data Fusion pipelines from the accelerator to integrate your SAP data.
5. From the Cloud Data Fusion Hub, deploy the pipelines associated with the procure-to-pay analytics process. These pipelines must be configured correctly to create the BigQuery dataset.
6. Connect Looker to the BigQuery project.
7. Install and deploy the Looker Block.

For more information, see Using the SAP Table Batch Source plugin.

Sample datasets in BigQuery

In the sample implementation in this accelerator, the following datasets are created in BigQuery.

Dataset name	Description
sap_cdf_staging	Contains all the tables from the SAP Source system as identified for that business process.
sap_cdf_dimension	Contains the key dimension entities like Customer Dimension and Material Dimension.
sap_cdf_fact	Contains the fact tables generated from the pipeline.

Sample pipelines in Cloud Data Fusion

Note: A table is a collection of records inside a dataset in BigQuery. A pipeline is the series of ETL functions that exist to move data between a source and a destination (such as a BigQuery table).

Sample pipelines for this accelerator are available in the Cloud Data Fusion Hub.

To get the sample pipelines from the Hub:

1. Go to your instance:

   1. In the Google Cloud console, go to the Cloud Data Fusion page.

   2. To open the instance in the Cloud Data Fusion web interface, click Instances, and then click View instance.

      Go to Instances

2. Click Hub.
3. Select the SAP tab.
4. Select Pipelines. A page of sample pipelines opens.
5. Select the desired pipelines to download them.

Each of the pipelines contains macros that you can configure to run in your environment.

Note: Changing the SQL macros is non-trivial. For example, a change to the target schema also requires changes to the data loading and Looker configurations.

There are three types of sample pipelines:

• Staging layer pipelines: The staging dataset in this type of pipeline is a direct mapping to the original source table in SAP. The sample staging layer pipelines have names that refer to the SAP source table and the BigQuery target table. For example, a pipeline named LFA1_Supplier_Master refers to the SAP Source Table (LFA1) and BigQuery target table (CustomerMaster).
• Dimension layer pipelines: The dimension layer dataset in this type of pipeline is a curated and refined version of the staging dataset that creates the dimension and facts needed for the analysis. The sample pipelines have names that refer to the target entity in the target BigQuery dataset. For example, a pipeline called customer_dimension refers to the Customer Dimension entity in the BigQuery dataset sap_cdf_fact.
• Fact layer pipelines: The fact layer dataset is a curated and refined version of the staging dataset that creates the facts that are necessary for the analysis. These sample pipelines have names that refer to the target entity in the target BigQuery dataset. For example, a pipeline called sales_order_fact delivers curated data to the Sales Order Fact entity in the corresponding BigQuery dataset sap_cdf_fact.

The following sections summarize how to get the pipelines to work in your environment.

Configure staging layer pipelines

There are two configuration steps for the staging pipelines:

1. Configure the source SAP system.
2. Configure the target BigQuery dataset and table.

Parameters for the SAP Table Batch Source plugin

The SAP Table Batch Source plugin reads the content of an SAP table or view. The accelerator provides the following macros, which you can modify to control your SAP connections centrally.

Macro name	Description	Example
${SAP Client}	SAP client to use	100
${SAP Language}	SAP logon language	EN
${SAP Application Server Host}	SAP server name or IP address	10.132.0.47
${SAP System Number}	SAP system number	00
${secure(saplogonusername)}	SAP user name	For more information, see Using Secure Keys.
${secure(saplogonpassword)}	SAP user password	For more information, see Using Secure Keys.
${Number of Rows to Fetch}	Limits the number of extracted records	100000

For more information, see Configuring the plugin.

Parameters for the BigQuery target

The accelerator provides the following macros for BigQuery targets.

BigQuery target connector configuration

Macro name	Description	Example
${ProjectID}	The project ID where the BigQuery dataset has been created.	sap_adaptor
${Dataset}	Target dataset	sap_cdf_staging

Sample pipelines used for procure-to-pay KPIs

The following key business entities in the procure-to-pay process correspond with sample pipelines in the accelerator. These pipelines deliver the data that powers the analytics about these entities.

Key business entities	Corresponding pipeline name
Supplier SAP source tables capture details about the supplier as they pertain to the business. Information from these tables contributes to the supplier_dimension in the data warehouse dimensional layer.	LFA1_SupplierMaster LFB1_SupplierMasterCompanyCode BUT000_BPGeneralInformation
Material or Product is the commodity that is traded between the enterprise and its customers. Information from these tables contributes to the material_dimension in the data warehouse dimensional layer.	MARA_MaterialMaster
The procure-to-pay process begins with an order, which includes order quantity and details about the material items.	EKKO_PurchaseOrderHeader EKPO_PurchaseOrdertItem
The Goods Receipt sub-process, which includes movement details about Material items.	MATDOC_GoodsReceipt
The Invoicing sub-processes, which includes requested invoice document details.	RBKP_InvoiceHeader RSEG_InvoiceLineItem
The procure-to-pay process ends when the invoice payment is logged in your system.	ACDOCA_UniversalJournalItem

All Cloud Data Fusion staging pipelines

The following Cloud Data Fusion staging pipeline samples are available in the accelerator:

• ACDOCA_JournalLedgerDetails
• ADR6_SupplierMasterEMailDetails
• ADRC_SupplierMasterAddressDetails
• BKPF_AccountingDocumentHeaderDetail
• BSEG_AccountDocumentItem
• BUT000_BusinessPartnerGeneralDataDetails
• BUT020_BusinessPartnerAddressDetails
• CEPCT_ProfitCenterDescription
• EBAN_PurchaseRequisitionDetails
• EKBE_PurchaseOrderHistoryDetail
• EKET_PurchaseOrderScheduleLinesDetail
• EKKO_PurchaseOrderHeaderDetail
• EKPO_PurchaseOrderItemDetail
• FINSC_BTTYPE_T_BusinessTransactionTypeDescription
• FINSC_LEDGER_T_JournalLedgerDescription
• LFA1_SupplierMasterDetails
• LFB1_SupplierMasterCompanyCodeDetails
• MARA_MaterialMaster
• MATDOC_MaterialMovementDetails
• MKPF_MaterialMovementHeaderDetail
• MSEG_MaterialMovementItemDetail
• RBKP_InvoiceReceiptHeaderDetail
• RSEG_IncomingInvoiceItemDetail
• T001_CompanyCodes
• T001_CompanyCodes
• T001K_ValuationAreaDetails
• T001L_MaterialStorageLocation
• T001W_PlantDetails
• T002T_LanguageKeyDescription
• T003T_AccountingDocumentTypeDescription
• T005_CountryMaster
• T006A_UnitOfMeasure
• T007S_PurchaseSalesTaxCodeDescription
• T023T_MaterialGroupDescription
• T024_PurchasingGroupsDetails
• T024E_PurchasingOrganizationsDetails
• T024W_PlantPurchasingOrganizationsDetails
• T156HT_MaterialMovementTypeDescription
• T161T_PurchasingDocumentTypeDescription
• T163M_ConfirmationCategoryDescription
• T16FE_PurchaseDocumentReleaseIndicatorDescription
• TBSLT_PostingKeyDescription
• TCURT_CurrencyCodesText
• TKA01_ControllingAreaMaster

Configure dimensional layer pipelines

You can extract KPIs from source SAP tables. To prepare the data for analysis, organize the data in the source table to match the BigQuery table's schema structure.

The accelerator creates the following sample tables:

Table name	Table description
Supplier_dimension	Curated list* of Suppliers and their associated facts such as supplier general information and supplier sales-related information.
Material_dimension	Curated list of Materials and associated facts such as SKU number, product hierarchy, and classification.
Purchase_Order_Fact	List of purchase orders, including purchase org, group, and order type.
Goods_Receipt_Fact	Curated list of goods receipts, including profit center and movement type information.
Invoice_Fact	Curated list of Invoice related information, including Invoice type, item quantity, value, and date of Invoice posting.
Accounting_Fact	Curated list of accounting postings for each purchase order line item.

*In this context, the curated list comes from business logic that gets applied to the selected list of columns.

The accelerator builds the dimensional layer of the BigQuery dataset using SQL scripts, which you can modify for your project. For example, you can adapt these scripts to add more columns to the target BigQuery dataset entities.

Transformation to star schema: BigQuery executor pipeline names

The following BigQuery executor pipelines in Cloud Data Fusion load data into dimension and fact tables:

All dimensional transformation pipelines:

• Supplier_dimension
• Material_dimension
• Purchase_Order_Fact
• Goods_Receipt_Fact
• Invoice_Fact
• Accounting_Fact

BigQuery executor configuration

Macro name	Example
${ProjectID}	sap_adaptor
${StagingDatasetName}	sap_cdf_staging
${TargetDatasetName}	sap_cdf_dimension

Connect Looker to the BigQuery project

To connect Looker to BigQuery, see the Looker documentation about BigQuery connections.

Install the block

Note: You need access to a Looker instance to install and use this block.

You can access the SAP Looker Block on GitHub.

The Looker Block installs a pre-configured LookML model with two Explore environments and two dashboards.

What's next

• Learn more about Cloud Data Fusion.
• Learn more about SAP on Google Cloud.
• Learn more about BigQuery.
• Learn more about Looker Blocks.