SAP and Salesforce Data Integration for Supplier Analytics on Databricks

· Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

Databricks enables direct integration of SAP S/4HANA and Salesforce data to create unified supplier analytics. This solution utilizes Lakeflow Connect for incremental Salesforce data ingestion and the SAP Business Data Cloud (BDC) Connector for zero-copy access to SAP S/4HANA data products via Delta Sharing. The platform unifies CRM and ERP data, governed by Unity Catalog, into a single, trusted view of vendors, payments, and performance metrics. Data engineers use Lakeflow Declarative Pipelines to build a medallion architecture, transforming raw data into a curated gold layer. This gold layer then powers analytics through AI/BI Dashboards and natural-language exploration with Genie, supporting use cases like faster dispute resolution, early-pay savings, and cleaner vendor master data.

Key takeaway

For data engineers and MLOps teams building enterprise data platforms, integrating SAP S/4HANA and Salesforce data on Databricks offers a streamlined approach to supplier analytics. Your team can achieve a unified, governed view of vendor data without traditional ETL complexities, enabling faster insights and improved financial efficiency. Consider adopting Lakeflow Connect and SAP BDC Connector to simplify data ingestion and ensure real-time, zero-copy access.

Key insights

Unifying SAP and Salesforce data on Databricks provides a single, governed view for comprehensive supplier analytics.

Principles

Method

Ingest Salesforce data via Lakeflow Connect, access SAP S/4HANA data via SAP BDC Connector and Delta Sharing, then blend and transform data using Lakeflow Declarative Pipelines into a governed gold layer for analytics.

In practice

Topics

Best for: Data Engineer, MLOps Engineer, Business Analyst

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.