Unleash Your SAP Data: Using Snowflake as Your Cloud Data Service

Turning SAP’s transactional data into analytics-ready intelligence is a challenge for many enterprises. Accel4 helps you connect SAP to Snowflake, creating a cloud-native data foundation that powers smarter decisions and scalable growth.

November 5, 2025

SAP systems hold the most critical data for global enterprises, but getting that data out for modern analytics, machine learning, and reporting has historically been complex.

With Snowflake, that changes. This post explores how organizations can connect SAP and Snowflake to simplify integration, harmonize data, and build a scalable data foundation.

The Challenge: Why Move SAP Data to Snowflake?

SAP systems like S/4HANA and ECC are optimized for transactional processing, not analytical queries at scale. When you need to combine SAP transactional data with customer data from a CRM, web logs, or IoT streams, the traditional approach often involves complex, brittle, and slow ETL processes.

Snowflake addresses this by providing a single, modern platform to:

  • Harmonize Data: Merge structured, semi-structured, and unstructured data from SAP and non-SAP sources.
  • Scale Effortlessly: Handle massive data volumes and concurrent analytical workloads without manual management.
  • Power AI/ML: Utilize tools like Snowpark to build data products and intelligent applications right next to your SAP data.
  • Simplify Consumption: Provide a single source of truth for all your BI, reporting, and data science teams.

The Connection Strategy: SAP to Snowflake

Moving data from SAP to Snowflake typically involves a hybrid approach, leveraging the strengths of both platforms, often through specialized integration tools.

1. Preparing the SAP Environment

The primary challenge is safely and efficiently extracting data from SAP’s complex, proprietary structure (the application layer and the underlying database).

  • Operational Data Provisioning (ODP): This is SAP’s modern, push-based extraction framework, often the preferred method. It allows for continuous, near-real-time data streaming and incremental updates without heavy lifting on the SAP source system.
  • CDS Views/OData APIs (S/4HANA): For S/4HANA, creating custom or using standard Core Data Services (CDS) Views and exposing them via OData APIs offers a well-governed, semantic layer-based extraction.
  • Direct Database Access (Less Common): Directly accessing the SAP database is often restricted by licensing or architecture and is generally discouraged, though some high-volume, self-hosted solutions can utilize it.

2. The Integration Layer: Tooling is Key

To bridge the gap between SAP’s structure and Snowflake’s cloud architecture, most enterprises rely on purpose-built connectors or integration platforms:

Integration Method Best For Key Benefit
Managed SaaS Connectors Fast time-to-value, diverse SAP systems No-code/Low-code setup, automated schema management.
SAP Data Services / BW Bridge (via SAP BDC / Datasphere) SAP-centric governance, existing SAP tool investment Leveraging SAP’s semantic modeling and security layer.
Cloud-Native ETL/ELT (e.g., Azure Data Factory, Custom Snowpipe/Snowpark) High customization, deep cloud platform integration Total control over data transformation and pipeline logic.

3. Loading and Serving in Snowflake

Once the data is extracted, the integration layer stages the data (usually in an internal or external cloud storage like S3, ADLS, or GCS) and uses Snowflake’s high-performance ingestion mechanism, Snowpipe, to load it.

The final step is to use Snowflake’s platform capabilities:

  • Data Transformation: Use dbt (Data Build Tool) or Snowpark to transform the raw SAP tables into clean, consumable data marts within Snowflake.
  • Data Sharing: Use Snowflake Data Sharing to securely share curated SAP data products with partners, customers, or internal business units instantly without copying the data.
  • Data Service: Expose your cleansed, harmonized SAP data via Snowflake’s ODBC/JDBC drivers or APIs to power downstream applications, reporting tools (like Tableau or Power BI), and AI models, effectively using Snowflake as the central data service hub.

Summary: A Modern Data Foundation

Connecting SAP data to Snowflake is a strategic move that modernizes your analytics foundation. It shifts your focus from wrestling with complex data extraction to driving business value from combined, governed, and highly available data products. By choosing the right integration method, you can unlock the full potential of your SAP investment in the Snowflake Data Cloud.

Partner with Accel4 to design and deploy a scalable data foundation. From integrating SAP transactional systems into the Snowflake Data Cloud to delivering AI-powered insights, our team helps you unlock your enterprise data in a secure, scalable, and governed way.