Created on 05-23-2025 06:19 AM - edited 05-23-2025 06:32 AM
This guide provides a step-by-step approach to extracting data from SAP S/4HANA via OData APIs, processing it using Apache NiFi in Cloudera Data Platform (CDP), and storing it in an Iceberg-based Lakehouse for analytics and AI workloads.
SAP S/4HANA is a powerful ERP system designed for transactional processing, but it faces limitations when used for analytics, AI, and large-scale reporting:
Performance Impact:
Running complex analytical queries directly on SAP can degrade system performance.
Limited Scalability:
SAP systems are not optimized for big data workloads (e.g., petabyte-scale analytics).
High Licensing Costs:
Extracting and replicating SAP data for analytics can be expensive if done inefficiently.
Lack of Flexibility:
SAP’s data model is rigid, making it difficult to integrate with modern AI/ML tools.
A Lakehouse architecture (built on Apache Iceberg in CDP) solves these challenges by:
SAP provides several data extraction methods, but OData (Open Data Protocol) is one of the most efficient for real-time replication:
Method | Pros | Cons | Best For |
OData API | Real-time, RESTful, easy to use | Requires pagination handling | Incremental, near-real-time syncs |
SAP BW/Extractors | SAP-native, optimized for BW | Complex setup, not real-time | Legacy SAP BW integrations |
Database Logging (CDC) | Low latency, captures all changes | High SAP system overhead | Mission-critical CDC use cases |
SAP SLT (Trigger-based) | Real-time, no coding needed | Expensive, SAP-specific | Large-scale SAP replication |
Why OData wins for Lakehouse ingestion?
NiFi is the ideal tool for orchestrating SAP-to-Lakehouse pipelines because:
Low-Code UI:
Drag-and-drop processors simplify pipeline development (vs. writing custom Spark/PySpark code).
Built-in SAP Connectors:
Use InvokeHTTP for SAP S/4 HANA OData for deeper integrations.
Scalability & Fault Tolerance:
Backpressure handling – Prevents SAP API overload.
Automatic retries – If SAP API fails, NiFi retries without data loss.
Before building the SAP S/4HANA → NiFi → Iceberg pipeline, ensure the following components and access rights are in place.
Cloudera Data Platform (CDP) with:
Apache NiFi (for data ingestion)
Apache Iceberg (as the Lakehouse table format)
Storage: HDFS or S3 (via Cloudera SDX)
SAP S/4HANA access with OData API permissions
T-Code SEGW: Confirm OData services are exposed (e.g., API_MATERIAL_SRV).
Permissions:
SAP User Role: Must include S_ODATA and S_RFC authorizations.
Whitelist NiFi IP if SAP has network restrictions.
curl -u "USER:PASS" "https://sap-odata.example.com:443/sap/opu/odata/sap/API_SALES_ORDER_SRV/A_SalesOrder?$top=2"
Validate:
Pagination ($skip, $top).
Filtering ($filter=LastModified gt '2025-05-01').
Basic knowledge of NiFi flows, SQL, and Iceberg
Data movement:
SAP S/4HANA (OData API) → Apache NiFi (CDP) → Iceberg Tables (Lakehouse) → Analytics (Spark, Impala, Hive)
Archtecture Overview :
SAP provides OData services for tables like:
MaterialMaster (MM)
SalesOrders (SD)
FinancialDocuments (FI)
Example endpoint:
https://<SAP_HOST>:<PORT>/sap/opu/odata/sap/API_SALES_ORDER_SRV/A_SalesOrder?$top=2
Use InvokeHTTP processor to call SAP OData API.
Configure authentication (Basic Auth).
Handle pagination ($skip & $top parameters).
To get the JSON response, I added Accept=application/json Property.
Parse JSON responses using EvaluateJsonPath or JoltTransformJSON.
Filter & clean data using:
ReplaceText (for SAP-specific formatting)
QueryRecord (to convert JSON to Parquet/AVRO)
Enrich data (e.g., join with reference tables).
Check the Data using Provinance :
Use PutIceberg processor (NiFi 1.23+) to write directly to Iceberg.
Alternative Option: Write to HDFS/S3 as Parquet, then use Spark SQL to load into Iceberg
CREATE TABLE iceberg_db.sap_materials (
material_id STRING,
material_name STRING,
created_date TIMESTAMP
)
STORED AS ICEBERG;
By leveraging Cloudera’s CDP, NiFi, and Iceberg, organizations can efficiently move SAP data into a modern Lakehouse, enabling real-time analytics, ML, and reporting without impacting SAP performance.
Explore Cloudera Machine Learning (CML) for SAP data analytics.
Created on 06-04-2025 06:27 AM
Excellent article @zzeng 👍 !!