About This Architecture

SAP S/4HANA data flows into Google Cloud Platform via dual-path ingestion using Cloud Dataflow for batch and Cloud Pub/Sub for streaming, feeding a multi-tier BigQuery data lake architecture. ETL processing stages—row-level transform, cleansing, conformance, and aggregation—progressively refine data across raw, curated, and aggregated tiers, with Dataplex providing unified governance and metadata management. AI-assisted development through Gemini AI and GitHub Copilot accelerates transformation logic and semantic modeling, while the serving layer exports business-ready facts and dimensions to Power BI's semantic model for analytics. This architecture demonstrates modern cloud-native ELT patterns with built-in AI acceleration, enabling organizations to modernize legacy SAP systems with scalable, governed data pipelines. Fork and customize this diagram on Diagrams.so to adapt ingestion patterns, tier definitions, or AI tooling to your enterprise requirements.

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How do you build a scalable ETL pipeline from SAP to BigQuery and Power BI with AI-assisted development and data governance?

This diagram shows a production-grade multi-tier architecture where SAP S/4HANA feeds dual ingestion paths (Cloud Dataflow batch and Cloud Pub/Sub streaming) into BigQuery's raw, curated, and aggregated tiers. Dataplex provides unified governance across the pipeline, while Gemini AI and GitHub Copilot accelerate ETL transformation logic and Power BI semantic modeling, enabling rapid modernization