ETL Journey in SAP BODS

Bridging ABAP and Python for Smarter SAP Data Integration

In many SAP environments today, it’s common to combine the strength of ABAP inside SAP with the flexibility of Python for external processing. When used together with SAP BusinessObjects Data Services (BODS), the result is a clean and efficient ETL flow that connects SAP’s core data with modern analytics and integration frameworks.

This post outlines a practical approach for integrating ABAP, SAP BODS, and Python to build an end-to-end extraction and transformation process.

How the Integration Works

The process relies on clear division of tasks between the three components:

• ABAP handles data extraction directly from SAP tables.

• BODS serves as the central orchestration and transformation layer.

• Python performs post-processing or integration with external systems.

This approach allows SAP data to move securely through a managed flow without losing integrity or performance.

Step 1. Extracting Data from SAP Using ABAP

Below is an ABAP report that extracts customer data and sends it to BODS for further processing.

REPORT zextract_customer_data.

DATA: lt_customer_data TYPE TABLE OF zcustomer_data,
      ls_customer_data TYPE zcustomer_data.

SELECT * FROM zcustomer_data INTO TABLE lt_customer_data WHERE active = 'X'.

IF lt_customer_data IS NOT INITIAL.
  CALL FUNCTION 'Z_BODS_CUSTOMER_EXPORT'
    TABLES customer_data = lt_customer_data
    EXCEPTIONS others = 1.
  IF sy-subrc = 0.
    WRITE: 'Data successfully exported to Data Services'.
  ELSE.
    WRITE: 'Error exporting data'.
  ENDIF.
ELSE.
  WRITE: 'No active customer data found.'.
ENDIF.

This program retrieves active customer records and passes them to the function module Z_BODS_CUSTOMER_EXPORT, which initiates the data transfer to SAP BODS.

Step 2. Triggering the BODS Job

The next step is the function module that handles the RFC call to the BODS server. It prepares the job parameters, connects to the RFC destination, and starts the data load.

FUNCTION Z_BODS_CUSTOMER_EXPORT.
  DATA: lv_destination TYPE string VALUE 'SAP_BODS_DEST',
        lt_parameters  TYPE TABLE OF sbdstpar,
        ls_parameters  TYPE sbdstpar,
        lv_status      TYPE string.

  LOOP AT customer_data INTO DATA(ls_customer).
    CLEAR ls_parameters.
    ls_parameters-paramname = 'CUSTOMER_ID'.
    ls_parameters-paramvalue = ls_customer-kunnr.
    APPEND ls_parameters TO lt_parameters.
  ENDLOOP.

  CALL FUNCTION 'Z_RFC_FUNCTION_TO_START_BODS_JOB'
    DESTINATION lv_destination
    EXPORTING jobname = 'CUSTOMER_DATA_EXPORT'
    TABLES parameters = lt_parameters
    IMPORTING jobstatus = lv_status
    EXCEPTIONS others = 1.

  IF sy-subrc = 0 AND lv_status = 'SUCCESS'.
    WRITE: 'BODS Job executed successfully.'.
  ELSE.
    WRITE: 'BODS Job failed.'.
  ENDIF.
ENDFUNCTION.

This logic allows SAP to initiate a BODS job on demand, ensuring that the ETL process can run automatically without manual intervention.

Step 3. Post-Processing and External Integration in Python

Once the BODS job produces a CSV output, Python can take over to clean, enrich, and send the data to other systems such as APIs or reporting tools.

import pandas as pd
import requests

# Load customer data
customer_data = pd.read_csv('customer_data.csv')

# Add a full name field and filter for active customers
customer_data['full_name'] = (
    customer_data['first_name'].fillna('') + ' ' + customer_data['last_name'].fillna('')
)
customer_data = customer_data[customer_data['active'] == 'Y']

# Send to external API
api_url = 'https://example.com/api/customers'
headers = {'Content-Type': 'application/json'}
customer_data_json = customer_data.to_json(orient='records')

try:
    response = requests.post(api_url, headers=headers, data=customer_data_json)
    response.raise_for_status()
    print('Customer data successfully sent to external system.')
except requests.exceptions.RequestException as e:
    print(f'Failed to send data: {e}')

Python is used here for its flexibility and integration capabilities. It’s ideal for automating tasks such as API submissions, validation checks, or running machine learning models on top of extracted SAP data.

Why This Combination Works

Component	Role	Advantage
ABAP	Data extraction	Direct, secure access to SAP data structures
BODS	Transformation	Orchestrates the flow, handles data cleansing and scheduling
Python	Integration	Connects SAP output with external APIs or analytical systems

By clearly separating each responsibility, this model stays modular and easy to maintain. It’s also compatible with both on-premise and cloud-based deployments.

Practical Considerations

1. Security: Use secure credential management and avoid embedding passwords in code.

2. Error Handling: Build consistent logging across ABAP, BODS, and Python layers.

3. Performance: For large datasets, process in smaller batches to reduce memory load.

4. Testing: Validate each stage separately before linking them end-to-end.

5. Scalability: Structure your ABAP and Python scripts so they can be reused across multiple ETL processes.

Conclusion

Combining ABAP, SAP BODS, and Python provides a reliable and flexible way to move data between SAP and external systems. It allows enterprises to maintain SAP’s governance and security model while introducing automation and analytics outside the core system.

This architecture has been used successfully in several client environments where SAP data needed to flow into analytics platforms or third-party business systems with minimal manual effort.