Reporting platform for a SAP S/4HANA transformation for operational teams and enterprise stakeholders.

Global contract food services leader · SAP S/4HANA · AWS Apache Hudi Lakehouse · Amazon Redshift · Power BI

Engagement at a glance

Greenfield SAP S/4HANA implementation, reporting, and analytics layer designed from zero
Two audiences served: operational teams and enterprise stakeholders
Cloud-native Lakehouse architecture on AWS, ACID-compliant, time-travel querying
Single trusted source of truth across the supply chain and financial reporting

The business context

When one of the world's largest contract food services organizations took the strategic decision to migrate to SAP S/4HANA on a greenfield basis, the reporting and analytics layer was the make-or-break component of the transformation program. Operational teams across thousands of sites needed real-time, SAP-native insights to run the day.
Enterprise stakeholders in finance, supply chain, and the executive suite needed deep, cross-domain analytics across a global footprint that spans dozens of countries, hundreds of suppliers, and tens of millions of transactions each month. The two audiences could not be served by a single reporting tool stacked on top of S/4HANA, nor by separate, disconnected platforms. The brief was a cloud-native enterprise data platform that could serve from day one of go-live.

The challenge

A greenfield S/4HANA implementation simplifies many things, but the reporting layer carries unique complexity. Operational reporting close to the source demands low-latency querying of live SAP transactions. Enterprise analytics across global business units demands historical depth, cross-domain joins, and analytical performance that S/4HANA itself is not architected to deliver. Stacking both on the same engine compromises both. The challenge was architectural: design a reporting platform where each audience gets the engine that fits, without fragmenting the data model or the user experience the business sees.

What the team did

The team led the end-to-end design and delivery of the reporting and analytics platform across two engineered layers, unified at the visualization tier.

SAP-native analytics layer

CDS views were designed directly in S/4HANA to surface real-time operational metrics at the source. This kept latency low for transactional reporting and maintained full fidelity with SAP's data model, so operational teams could trust the numbers without translation.

AWS Lakehouse and data marts

For enterprise-scale analytics, the team architected a modern Lakehouse on AWS using Apache Hudi, enabling incremental data ingestion, ACID-compliant updates, and time-travel querying across large historical datasets. Purpose-built data marts in Amazon Redshift served as the high-performance query layer, structured around the specific analytical domains of the business.

Power BI reporting layer

Power BI was deployed as the unified visualization platform, connecting to both the Redshift data marts and the SAP analytics layer to deliver a consistent reporting experience across all teams. Role-based access ensured each user, from a regional supply chain analyst to the global CFO, saw the data calibrated to their decision rights.

Analytics coverage

Reporting was structured across two critical enterprise domains. Supply chain analytics covered spend analytics across categories and suppliers, vendor performance tracking and benchmarking, and pricing analysis and variance monitoring. Financial dashboards covered profit-and-loss reporting, trial balance and period-end close support, and profitability analysis across business units and geographies.

The platform was engineered as one practice, not two. The Lakehouse, the data marts, the CDS views, and the Power BI semantic model were governed by a single data architecture, so the metric a regional supply chain analyst reads matches the metric the global CFO reads.

The outcome

The platform delivered a single, trusted source of truth for both supply chain and financial reporting, built to scale with global operations from day one of go-live. Operational teams gained real-time visibility into the SAP transactions that run the business day to day. Enterprise stakeholders gained the depth of analytics required to manage spend, vendor performance, and profitability across the global portfolio. The reporting cadence shifted from period-end batch reporting to near-real-time monitoring across both supply chain and finance, giving leadership the visibility a globally distributed food services business actually needs to run from.

What this means for your enterprise

If your enterprise is undertaking an SAP S/4HANA transformation and treating the reporting layer as a downstream activity, this engagement is a warning. The reporting architecture has to be designed in parallel with the core S/4HANA implementation, not retrofitted afterward. The Lakehouse-on-cloud pattern is the right answer when two audiences need two engines under one governed semantic layer. When done well, the reporting platform becomes the place the business actually runs from, not the dashboard it checks once a month.

"In a greenfield S/4HANA program, the reporting architecture is the business architecture."

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Frequently Asked Questions

What happens to the reporting platform when the business acquires a new entity or expands geographically?

The Lakehouse architecture was deliberately designed to absorb growth without structural rework. New entities are onboarded as additional data sources feeding into the existing ingestion layer, mapped to the governed semantic model already in place. Dashboards inherit the new data once the domain mapping is complete. Expansion adds volume and scope; it does not require rebuilding the platform.
How are data quality issues caught before they reach executive dashboards?

Data quality is enforced at the ingestion layer, before records move into the Lakehouse. Validation rules flag anomalies, missing cost center assignments, and incomplete vendor master records at the point of entry rather than at the point of consumption. Executives see clean, reconciled data because problems are caught upstream, not papered over at the visualization tier.
Can business users run their own analyses without involving the data team?

The platform is structured to support both. Governed dashboards cover the standard reporting, P&L, vendor performance, and spend analytics, without any technical involvement. For ad hoc analysis, the Power BI semantic model exposes a curated, business-friendly data layer that analysts can query directly without touching the underlying Lakehouse. Self-service operates within guardrails, so flexibility does not come at the cost of metric consistency.
What is Apache Hudi, and why was it chosen over a conventional data warehouse?

Apache Hudi is an open-source framework that brings database-grade capabilities, ACID compliance, incremental updates, and time-travel querying to cloud storage at scale. A conventional data warehouse handles static snapshots well but struggles with continuous ingestion from a high-volume SAP environment. Hudi handles late-arriving data, record-level updates, and historical point-in-time queries without full reloads, which matters significantly when tens of millions of transactions flow through monthly.
How is sensitive financial data protected across the entire reporting pipeline?

Data is encrypted in transit and at rest across every layer, from SAP extraction through the AWS Lakehouse to the Redshift query tier. Access to raw Lakehouse data is restricted to the data engineering layer; business users interact exclusively through the governed Power BI semantic model, where row-level and role-level security controls are applied.
Who owns the platform after go-live, and what does maintenance require from internal IT?

Operational ownership sits with a lean internal team. The cloud-native AWS stack automatically scales infrastructure, and Power BI is manageable for analysts with moderate BI experience. Deeper technical areas, Hudi compaction, semantic model updates, and SAP CDS view changes are handled through a managed support arrangement or a small internal data engineering function, depending on organizational maturity.