SAP S/4HANA Analytics Modernisation on Google BigQuery for a Global Consumer Goods Group

The challenge

• The group was harmonising information across SAP and non-SAP systems as part of its digital strategy: one enterprise data model, standard business definitions, and consistent reporting across regions.
• A key objective was to expose SAP S/4HANA data into Google Cloud, with Microsoft Azure to follow, for cross-system analytics, integrated planning and AI/ML use cases, without losing governance or semantic consistency on the way out of SAP.
• SAP data reached analytics teams in days. High-volume tables such as financial documents, material master and sales orders needed change-data capture at scale, not batch extracts.

How we went about it

1. 1

   Datasphere at the centre

   We architected the SAP → Datasphere → Google Cloud flow with SAP Datasphere as the semantic and governance layer, so that what left SAP was already modelled, defined and governed rather than raw tables to be reinterpreted downstream.

2. 2

   Change-data capture at scale

   Replication Flows, SLT and ODP with ABAP CDS views delivered high-volume CDC from S/4HANA, including financial documents, material master and sales orders, with latency measured in minutes.

3. 3

   Layered, traceable models

   Inbound, harmonised and consumption layers in Datasphere; dimensions, facts, hierarchies and business entities aligned to finance, supply chain, sales and procurement, following clean-core practice.

4. 4

   BigQuery as the landing zone

   With the Google Cloud teams we defined the landing patterns, so BigQuery received clean, structured, business-ready datasets from Datasphere views and data products. Curated datasets and Looker dashboards opened self-service and KPI reporting to the business.

What we delivered

• End-to-end SAP S/4HANA → Datasphere → BigQuery architecture, designed and owned through implementation.
• CDC replication pipelines for finance, supply chain and sales, built on Datasphere Replication Flows, SLT and CDS views.
• Semantic models, data products and a three-layer modelling standard in Datasphere, with lineage, metadata and quality checks.
• Security and access controls through Datasphere spaces, privileges and role-based access, with IAM, encryption and data classification across SAP and Google Cloud.
• Curated BigQuery datasets and Looker dashboards for finance, supply chain and sales.