SparQ SAP Clean Core Certification Bridges S/4HANA Cloud with Azure Analytics

Kagool’s SparQ has been formally recognised by SAP’s Integration and Certification Center as SAP‑certified for clean core with SAP S/4HANA Cloud — a milestone announced on February 24, 2026 that positions the platform as a governed bridge between SAP’s digital core and Microsoft Azure analytics services. The certification covers SparQ integration software, Version 2.3, and reinforces Kagool’s positioning: deliver enterprise-grade, AI‑ready datasets from S/4HANA Cloud into Azure while preserving the integrity of the SAP clean core strategy.

Background: why "clean core" matters now​

The concept of a clean core has moved from best practice to board‑level strategy for SAP customers. SAP’s clean core guidance encourages organisations to keep the S/4HANA core as close to standard as possible, push differentiation to side‑by‑side extensions, and manage custom logic through upgrade‑safe approaches. The result is faster upgrades, lower technical debt, and a more resilient digital core ready for business innovation.
At the same time, enterprises are under pressure to democratise analytics and scale AI use cases. That creates a tension: business teams demand fast, flexible access to data for Power BI dashboards and generative AI experiments, while IT and SAP teams must avoid changes that jeopardise upgradeability or create compliance risk in the core ERP.
Kagool’s SparQ is pitched squarely at that tension. The product promises a governed, automated data pipeline to Azure that gives business users analytic agility while maintaining SAP clean core hygiene — and the SAP certification is intended to signal vendor alignment with SAP’s architecture and upgrade commitments.

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What Kagool announced: the essentials​

• The announcement date was February 24, 2026 and references SparQ integration software, Version 2.3, as SAP‑certified for clean core with SAP S/4HANA Cloud.
• SparQ is framed as a governed enterprise data platform that moves and prepares SAP data into Microsoft Azure for reporting, analytics, and AI.
• Core product themes described by the vendor include:
• Automated, governed SAP‑to‑Azure data pipelines
• Pre‑built domain data models (Finance, HR, Procurement, Inventory)
• Enterprise governance (metadata enrichment, sensitivity classification, compliance tagging, lineage)
• Data observability (quality monitoring, freshness tracking)
• Centralised access controls (RBAC, Row‑Level Security, Column‑Level Security, audit trails)
• SAP BW Analyser capability to inventory and rationalise legacy BW landscapes
• An Approval Cockpit for delegated approvals of dataset uploads/downloads
• Example GUI workflows that automate Azure Data Factory (ADF) orchestration and Databricks CDC/transformation in the background

The vendor emphasises that SparQ is not a visualization tool; instead, it supplies the governed, trusted data foundation so tools like Power BI and other downstream analytics/AI platforms can consume consistent, compliant datasets.

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Why the SAP ICC clean core certification matters (and what it does — and doesn’t — guarantee)​

The SAP Integration and Certification Center (ICC) clean core designation is an important signal for SAP customers evaluating third‑party solutions for S/4HANA Cloud integration.
What the certification delivers:

• A formal validation that the vendor’s integration approach complies with SAP’s clean core principles and integration patterns.
• A public assurance that the solution has been tested against SAP S/4HANA Cloud integration standards for the certified version.
• Confidence that the solution follows upgrade‑safe practices promoted by SAP, reducing the likelihood of unsupported modifications to SAP objects.

What the certification does not automatically guarantee:

• Zero operational work: integration still requires project architecture, SAP Basis and security configuration, and organisational governance.
• Pricing, performance at scale, or fit for a specific customer’s BW migration complexity — those remain implementation matters.
• Permanent compliance across future product versions: customers should verify upgrade and support commitments; SAP’s clean core program generally expects partners to maintain upgrade‑readiness as new S/4HANA releases appear.

In short, the certification is an important gate — it reduces integration risk — but CIOs and data leaders must still validate performance, TCO, and the vendor’s operational SLAs in their own environment.

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What SparQ brings technically: a closer look​

SparQ is presented as a unified data platform with several discrete technical layers and capabilities. Breaking these down:

Ingestion & change data capture (CDC)​

• The product automates the extraction of SAP S/4HANA Cloud data and delivers it to Azure targets. Kagool references orchestration with Azure Data Factory (ADF) and transformation / DeltaLake processing in Databricks, with CDC support to keep datasets current.
• CDC modes and delta strategies are essential in SAP migrations: timestamp deltas, counter fields, and join‑aware CDC reduce load and preserve transactional integrity. Kagool’s approach appears to combine ABAP‑based extraction logic (proprietary or certified ABAP extractors) with Azure orchestration.

Transformation & modelling​

• SparQ provides a low‑code/no‑code pipeline studio for configuring transformations, naming conventions, validation, enrichment, and domain data models.
• Pre‑built models for Finance, HR, Procurement and Inventory aim to accelerate delivery and ensure consistent KPI definitions for downstream reporting.

Governance, security & observability​

• Automated metadata enrichment, sensitivity classification, compliance tagging, and full lineage tracing from SAP tables to reporting datasets are core claims.
• Access controls include RBAC, Row‑Level Security (RLS), Column‑Level Security (CLS), and an Approval Cockpit for delegated approvals when sensitive datasets are uploaded or exported.
• Data quality monitoring and freshness tracking provide operational observability and SLA enforcement.

SAP BW Analyser​

• One notable capability is the SAP BW Analyser which inventories legacy BW artifacts, identifies duplicated models and unused reports, maps usage/volumetrics, and estimates the number of Azure models needed. This helps create a migration roadmap and estimate implementation effort.

Integration stance​

• Kagool positions SparQ as an enterprise data layer, not a BI front end. The platform is designed to feed trusted, governed datasets to Power BI, Databricks, Fabric, and other analytics runtimes.

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Strengths: where SparQ can immediately add value​

• Alignment with SAP clean core guidance. The certification removes a key vendor evaluation blocker for SAP S/4HANA Cloud customers worried about side‑effects of third‑party integration.
• Speed of delivery for analytics. Pre‑built data models and automated ingestion pipelines reduce the time to generate trusted reporting datasets for Power BI teams.
• Governance by design. Integrated lineage, metadata, sensitivity classification, and approval workflows help enterprises meet audit and compliance requirements while enabling decentralised report creation.
• Targeted BW rationalisation. The BW Analyser is a practical tool: BW estates are often littered with duplicate cubes, unused queries, and obsolete reports. A discovery tool that quantifies re‑use opportunities and rationalisation can substantially reduce migration scope.
• Azure native orchestration. Leveraging ADF + Databricks is a pragmatic pattern in Microsoft ecosystems — ADF for control and orchestration, Databricks for heavy transformation — which many enterprises already support operationally.
• Business‑friendly operations. Low‑code/no‑code pipeline configuration and governance workflows enable business analysts to participate without bypassing central controls.

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Practical risks, caveats, and areas for buyer due diligence​

While the certification and product claims are compelling, there are several practical considerations every CIO and data leader should evaluate before committing.

1. Scope of the certification vs. implementation realities​

• Certification covers the integration software for the declared version (2.3). Customers should confirm the scope and whether companion components (extraction packages, connectors) used in their rollout are included in the certified artefacts.
• Future S/4HANA updates will require the vendor to maintain upgrade readiness — confirm contractual commitments and the vendor’s roadmap for certification continuity.

2. Hidden complexity in BW migrations​

• The SAP BW Analyser can quantify and prioritise assets, but migrating complex BW logic (calculated key figures, process chains, multi‑dimensional models) to relational or lakehouse models is non‑trivial.
• Expect significant effort for semantic parity: KPI definitions, currency translations, time‑dependent attributes and calculated measures must be re‑engineered, tested, and validated.

3. Cost and vendor lock‑in considerations​

• ADF and Databricks have predictable operational and compute cost models, but large‑scale CDC and heavy transformations can be expensive. Model expected consumption and run a cost projection.
• Organisations should review whether SparQ workflows create dependencies on Kagool‑specific metadata stores, and ensure exportability of models and lineage to avoid vendor lock‑in.

4. Security & compliance posture​

• The platform claims encryption, sensitivity classification, and auditability, but buyers must validate:
• Where encryption keys are managed (customer‑managed keys recommended)
• How CLS/RLS integrates with the organisation’s identity provider and access management
• Data residency and retention controls for regulated industries

5. Operational ownership & skillset​

• Orchestration across ADF, Databricks, and SAP extraction layers requires a blended skillset: SAP Basis/ABAP, Azure platform engineering, data engineering on Databricks, and data governance teams.
• Organisations must ensure SRE runbooks, incident escalation, and monitoring are defined; certification does not eliminate the need for operational maturity.

6. Vendor claims and performance metrics​

• Marketing claims (e.g., “reduce manual reporting effort by X%” or “3x faster insight delivery”) should be validated via proof of value pilots. Ask for customer references, measurable before/after metrics, and architecture blueprints for similar deployments.

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Recommended adoption playbook for CIOs and data leaders​

If you’re evaluating SparQ (or any certified clean core integration solution), follow a staged adoption approach to reduce risk and accelerate value.

• Pilot selection and business case
• Pick one domain (Finance or Inventory) with moderate complexity and clear KPIs.
• Define success metrics: data freshness SLAs, report delivery time, reduction in manual ETL hours.
• Discovery & BW rationalisation
• Run the SAP BW Analyser to identify candidate reports/models for migration.
• Rationalise: retire unused artefacts, consolidate duplicates, and prioritise high‑value models.
• Architecture & security design
• Define target Azure architecture (ADF orchestration, Databricks compute, ADLS/Delta Lake storage).
• Confirm key management, identity integration (Azure AD), and encryption at rest/in transit.
• Proof of Value pipeline
• Configure a single, governed pipeline for extraction, CDC, and transformation into a gold dataset.
• Validate lineage, sensitivity labels, and RLS/CLS enforcement with a sample report consumed by Power BI.
• Operationalise observability & runbooks
• Implement monitoring (pipeline health, freshness, quality alerts) and incident runbooks.
• Define roles: data owners, data stewards, platform SRE, and consumer champions.
• Scale and expand
• Expand to other domains, iterating on model templates and governance policies.
• Optimise compute patterns for cost and performance — e.g., use Databricks job clusters for heavy transforms and ADF for orchestration/light processing.
• Contractual & certification governance
• Ensure SLAs for vendor support, patching, and upgrade readiness are contractually agreed.
• Define a cadence to re‑validate certification alignment after major S/4HANA releases.

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Operational and cost considerations to model up front​

• Plan CDC cadence and retention: How long will you store historical versions in Delta Lake? Retention impacts storage costs and compliance.
• Model compute patterns: short‑lived interactive Databricks clusters for dev/validation and scheduled job clusters for production transforms can optimise costs.
• Security: use customer‑managed keys, and ensure audit events from SparQ flow into enterprise SIEM for compliance monitoring.
• Licensing: confirm the licensing model — platform subscription, per‑pipeline pricing, or capacity tiers — and how that interacts with Azure consumption.
• Change management: training and governance for citizen Power BI developers to ensure they consume published datasets, not bypass governance.

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How this fits broader cloud and analytics strategies​

• For organisations standardising on Microsoft Azure, a certified integration that automates pipelines into ADF/Databricks reduces integration project risk and speeds analytics enablement.
• Enterprises balancing decentralised BI and centralised governance will benefit from a model where trusted datasets are centrally produced and curated, while report authors retain agility to build business‑focused dashboards.
• For customers migrating off SAP BW, the combination of a discovery/rationalisation tool plus automated pipeline generation can significantly reduce the initial lift — but ongoing semantic reengineering remains the critical workstream.

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Final assessment: credible step forward with sensible caveats​

Kagool’s announcement that SparQ (Version 2.3) is certified by SAP for clean core with S/4HANA Cloud is a meaningful commercial and technical validation. The certification removes a key hurdle for organisations that must reconcile SAP clean core principles with enterprise demands for faster, AI‑ready analytics. The product’s emphasis on governance, automated pipelines, CDC support, and a BW Analyser makes it a credible option for Azure‑centric SAP customers seeking an accelerated migration and reporting modernisation path.
However, certification is a starting point — not a guarantee of project simplicity. Buyers should validate the vendor’s operational maturity, confirm the scope and limits of the certification for their planned architecture, model ongoing run costs for ADF and Databricks workloads, and factor in the non‑trivial semantic work required to faithfully reproduce BW logic in Azure models.
For CIOs and data leaders evaluating SparQ, recommended next steps are: run a focused proof of value on one domain, validate governance and RLS/CLS integration with your identity management, and verify contractual commitments for upgrade readiness as SAP releases evolve. Done well, a certified platform like SparQ can become the trusted data backbone for enterprise reporting and AI — accelerating insight delivery while honouring the discipline of a clean SAP core.

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Conclusion
SparQ’s SAP clean core certification is a timely validation that addresses a real market tension: enabling enterprise analytics and AI without compromising the upgradeability and integrity of S/4HANA Cloud. The platform’s combination of ingestion automation, BW rationalisation tooling, governance controls, and Azure‑native pipeline orchestration positions it as a practical option for organisations embracing Microsoft cloud for analytics. Still, the usual caveats apply: test with a pilot, quantify migration effort for complex BW semantics, verify operational and cost assumptions, and ensure contractual commitments hold across S/4HANA release cycles. With those precautions, SparQ could materially shorten the path from SAP data to trusted, auditable insights in Azure.

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Source: Weekly Voice SparQ by Kagool Is Certified by SAP® for clean core with SAP S/4HANA Cloud | Weekly Voice

 

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Kagool today announced that SparQ (Version 2.3) is SAP‑certified for clean core with SAP S/4HANA Cloud, a development the vendor says will let organisations automate governed SAP→Azure data pipelines while preserving the integrity of the SAP digital core. This certification — presented by Kagool and repeated in multiple press distributions on February 24, 2026 — positions SparQ as a governed data‑fabric that feeds analytics and AI workloads in Microsoft Azure (Power BI, Databricks, ADF, and related services) without forcing customisations into the S/4HANA core.

Background / Overview​

The “clean core” concept has become a strategic mandate for SAP customers: keep S/4HANA as close to standard as possible, avoid permanent custom code in the digital core, and route differentiation to upgrade‑safe, side‑by‑side extensions. SAP’s Integration and Certification Center (SAP ICC) introduced a formal clean‑core certification path so partner extensions can be validated against those upgrade and architecture principles.
SparQ is a governed enterprise data platform from Kagool that sits alongside SAP, ingests and rationalises SAP data, and publishes standardised, traceable, analytics‑ready models into Azure. According to the vendor, the SAP ICC has certified the SparQ integration software for clean core with SAP S/4HANA Cloud — a stamp intended to reassure SAP customers that the product adheres to SAP’s extensibility and upgradeability rules while enabling business‑led reporting and AI experimentation.
This announcement arrives at a moment when many enterprises are migrating from SAP BW/BI landscapes to cloud data platforms (Azure, Databricks, Microsoft Fabric) and simultaneously trying to scale business intelligence and generative AI use cases without jeopardising ERP upgrade hygiene. SparQ’s proposition is to act as the governed “intelligence layer” between SAP and Azure tools: not a visualization product, but the trusted data foundation those visualization and ML tools depend on.

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What the certification announcement actually says​

• Product and version: SparQ, integration software Version 2.3, is declared SAP‑certified for clean core with SAP S/4HANA Cloud.
• Primary value proposition: Automated, governed data pipelines from S/4HANA Cloud to Microsoft Azure that produce analytics‑ready semantic models and datasets.
• Key platform capabilities called out by Kagool:
• Pre‑built domain data models (Finance, HR, Procurement, Inventory).
• Automated metadata enrichment, sensitivity classification and compliance tagging.
• End‑to‑end lineage (from SAP source tables to reporting datasets).
• Data quality monitoring and freshness tracking.
• Centralised access controls: RBAC, Row‑Level Security (RLS), Column‑Level Security (CLS), approval workflows and audit logging.
• SAP BW Analyser: tooling to rationalise legacy BW footprints, identify duplicated models/reports, measure usage/volumes, and estimate migration work to Azure.
• Operational GUI that automates Azure orchestration (Azure Data Factory) and Databricks change‑data‑capture/transform processes in the background.
• Vendor claims: The certification proves that organisations can extend SAP safely, accelerate insight delivery, and build trusted reporting at scale while maintaining clean‑core principles.

Note: Kagool and press outlets publicised the certification; SAP’s own certified‑solutions directory and SAP ICC pages explain the clean‑core program and its re‑certification requirements. At the time this article was prepared, readers should confirm the certification listing in SAP’s certified solutions directory or with SAP ICC for definitive verification.

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Why this matters to CIOs and data leaders​

Maintaining upgradeability and reducing technical debt are board‑level concerns for SAP customers. At the same time, lines of business demand agility for dashboards, predictive models and generative AI experiments. That tension produces two conflicting impulses:

• IT must preserve a stable, standard S/4HANA digital core to ensure predictable upgrades and supportability.
• Business teams want rapid, decentralised access to governed data so they can build Power BI dashboards, run scenario analyses and prototype AI-enabled workflows.

SparQ’s certified positioning addresses that tension in three concrete ways:

• It promises to keep business‑facing transformations and metric logic out of S/4HANA, placing them in a governed Azure layer that can evolve independently.
• It provides prebuilt domain models and a semantic layer intended to eliminate duplicated metric definitions across business teams.
• The SAP clean‑core certification is a signal (if validated) that the integration follows SAP’s extensibility and API rules — lowering the risk that the integration itself will create upgrade hurdles.

For CIOs evaluating migration or modernisation programmes, a certified integration platform can shorten procurement decision cycles and reduce vendor due‑diligence friction. But certification is a starting point — not a guarantee of fit for purpose.

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Technical anatomy — what SparQ appears to deliver​

Kagool describes SparQ as a governed, AI‑ready data platform that integrates S/4HANA Cloud with Azure and delivers a curated semantic layer. From public descriptions and product pages the following architecture components are emphasised:

• Ingestion and Change‑Data‑Capture (CDC)
• Automated SAP‑to‑Azure pipelines, described as leveraging Azure Data Factory orchestration and Databricks CDC/transformation engines.
• Support for continuous data refreshes and multiple delta modes to keep datasets current.
• Transformation and Modelling
• Low‑code / no‑code pipeline studio to define cleansing, validation and naming conventions.
• Domain‑specific templates for Finance, HR, Procurement and Inventory that produce analytics‑ready models.
• Governance and Observability
• Automated metadata enrichment and classification.
• Data quality metrics, freshness tracking and lineage across the pipeline.
• Approval workflows, audit trails and centralised RBAC with RLS/CLS enforcement.
• Migration & Rationalisation Tools
• An SAP BW Analyser to inventory existing BW objects, measure usage and propose a rationalised set of Azure models — effectively a migration planning accelerator.

These functions are the building blocks you’d expect in a governed data platform designed to be enterprise‑grade. The explicit mention of Azure Data Factory and Databricks indicates the platform is built to operate within a Microsoft/Azure ecosystem and to produce Power BI‑friendly semantic datasets.
Caveat: Kagool’s public materials and the press release do not disclose all integration mechanics — for example whether SparQ uses SAP released APIs (OData, CDS views), extractors, RFCs, IDocs, or an agent‑based approach — and the certification text does not indicate the clean‑core level (A/B/C) awarded. Customers must validate the exact SAP integration method (and confirm it aligns with their security expectations) during POC and contract negotiation.

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Strengths and likely benefits​

• Alignment with clean‑core principles. If the SAP ICC certification is confirmed, it signals that SparQ follows SAP’s recommended extensibility and upgrade‑safe frameworks — reducing the risk of custom logic being embedded inside S/4HANA.
• Governed semantic layer for enterprise BI. Prebuilt domain models and centralised KPI definitions can eliminate inconsistent KPIs across teams, a pervasive problem in large SAP estates.
• Acceleration of BW → Azure migrations. The SAP BW Analyser capability promises to reduce discovery and scoping effort by identifying duplicated models and estimating migration effort — a common bottleneck.
• Operational transparency. End‑to‑end lineage, data‑quality monitoring and audit logging are essential for compliance, and SparQ positions those features as core capabilities.
• Business‑led analytics with IT guardrails. By enabling citizen Power BI development on top of centrally governed datasets, organisations can decentralise development while keeping a single source of truth.
• Enterprise security constructs. Row and column level security, RBAC and approval workflows are described as built‑in, which matters for regulated industries and large multinationals.

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Practical risks and limitations to evaluate​

No product is a silver bullet. Here are the main risks and limitations CIOs and data leaders should evaluate before committing to SparQ:

• Certification vs. reality: Vendor announcements about certification are helpful, but they’re not a substitute for verifying the SAP certified listing, the exact certification scope, and whether the certificate requires on‑going re‑certification with SAP release cycles.
• Unclear integration mechanics: Public materials do not detail exactly which SAP interfaces are used. The choice (CDS views, OData, IDoc, RFC, SLT, SAP Data Intelligence connectors) affects latency, load on the ERP, security posture, and licensing.
• Hidden business logic risk: Even with a governed semantic layer, historical business logic embedded in SAP customisations or BW transforms may need to be reverse‑engineered carefully. Rationalisation is often more people‑and‑process work than automation.
• Cost and operational footprint: Continuous CDC and Databricks transformations incur Azure compute, storage and Databricks costs. Organisations must model operational spend realistically, including spike loads for month‑end processes.
• Vendor and platform lock‑in: A deep investment in a vendor‑specific set of Azure orchestration patterns or prebuilt models can create migration costs if the organisation later changes strategy (for example, switching to another cloud or analytics fabric).
• Performance & scale testing: Integration complexity and dataset sizes in global SAP estates can stress pipelines; POC testing at realistic scale is essential.
• Security and sovereignty: Moving core financial and personnel data outside the ERP to cloud object stores raises data residency, encryption and access concerns. Customers must validate security controls, key management, and compliance with local regulations.
• Re‑certification cadence: SAP’s clean‑core program expects vendors to keep pace with S/4HANA releases; partners often commit to re‑certify within a set window of SAP’s RTC (release to customer) schedule. Customers should confirm the vendor’s re‑certification commitments contractually.

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Migration from BW to Azure — what the SAP BW Analyser offers (and what it does not)​

Kagool highlights a built‑in SAP BW Analyser that inventories legacy BW objects and proposes a migration roadmap. This capability is valuable — but understanding the boundaries is critical.
What the BW Analyser promises:

• Identify duplicated data models and reports in BW.
• Analyse usage, volumetrics and query patterns.
• Estimate the number of Azure models required and potential automation opportunities.
• Produce a structured migration roadmap with effort estimates.

Why that matters:

• Organisations commonly find thousands of BW objects with overlapping scope; automated inventory and usage analysis speeds rationalisation.
• Better discovery reduces rework in Azure and helps teams prioritise high‑impact deliverables.

What the BW Analyser may not do:

• Fully automate semantic parity. Business teams often expect exact feature parity for complex BW calculations, exceptions and custom hierarchies; these can require manual translation.
• Replace business sign‑off and validation. Technical mapping must be validated by finance, procurement, HR owners to ensure KPI equivalence.
• Remove the need for data‑quality remediation in source systems. The analyser helps plan, but data cleansing and process fixes remain essential.

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Security, governance and compliance — what to test in a POC​

When you evaluate SparQ (or any SAP→cloud data fabric), treat security and governance as first‑class acceptance criteria. Key checks to include in a proof‑of‑concept:

• Confirm the SAP integration method and ensure it uses released SAP APIs or upgrade‑safe connectors.
• Validate end‑to‑end lineage for a representative set of KPIs: source table → transformation → semantic model → Power BI dataset.
• Test Row‑Level Security and Column‑Level Security enforcement across the data pipeline and in downstream BI clients.
• Verify sensitivity classification and automated compliance tagging; ensure classifications persist through transformations and exports.
• Exercise approval workflows for sensitive dataset extraction and sharing; audit trails should be tamper‑resistant and readily downloadable.
• Perform load testing for full month‑end refreshes and concurrency patterns to measure Azure Databricks and ADF costs and latency.
• Confirm encryption at rest and in transit, and review key‑management policies (customer‑managed keys vs platform keys).
• Evaluate data residency and local regulation compliance for multinational deployments.

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Recommended evaluation checklist for CIOs and data leaders​

• Confirm SAP ICC certification details: scope, level, certificate number and SAP Certified Solutions Directory listing.
• Require vendor demonstration of the exact SAP integration pattern and API usage.
• Run a focused POC with realistic volumes and month‑end scenarios — include Power BI users.
• Validate lineage, KPI parity, and sign‑offs from finance/HR owners.
• Stress test CDC configuration for latency, data drift and cost modelling.
• Check security posture: encryption, key management and identity integration with corporate IAM.
• Confirm re‑certification commitments and roadmap alignment with SAP release cycles.
• Request detailed TCO modelling for Azure costs (storage, Databricks, ADF pipelines).
• Assess vendor support SLAs for incident response and upgrade windows.
• Plan for a staged BW rationalisation: inventory → rationalise → migrate → validate.

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Commercial and organisational considerations​

• Licensing and procurement: Understand how SparQ is licensed (subscription, per‑instance, per‑node) and whether Azure services are included or billed separately.
• Governance model: Organisations should decide early which team owns the semantic layer (central data team, SAP team, or a hybrid centre of excellence).
• Skills and change management: Modernising reporting demands cross‑training — SAP functional teams, data engineers (Databricks/ADF), and Power BI authors must collaborate on rules, definitions and validations.
• Hybrid operations: Many customers will run a hybrid scenario for months (or years) where BW remains in production for certain reports while Spark/Azure targets replace others. Migration runbooks must address dual‑write, reconciliation and cutover procedures.

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Where SparQ fits in the broader analytics and AI stack​

SparQ is positioned as the governed source-of-truth layer: it does not replace visualization tools or ML engines but prepares the data those tools consume. In a typical modern Microsoft ecosystem this becomes:

• S/4HANA Cloud remains the transactional system.
• SparQ ingests and transforms S/4HANA data and publishes governed models into Azure.
• Downstream tools (Power BI, Fabric, Databricks notebooks and MLOps pipelines) build analytics and AI use cases on top of the SparQ semantic layer.
• Central governance (data catalogue, classification and lineage) is enforced at SparQ and propagated downstream.

This approach supports decentralised report creation (citizen developers) while avoiding duplicated business logic, because all Power BI authors use the same governed models.

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Final assessment — practical verdict for WindowsForum readers​

Kagool’s announcement that SparQ (v2.3) is SAP‑certified for clean core with SAP S/4HANA Cloud is strategically relevant to organisations wrestling with BW migrations, KPI sprawl and AI readiness. The product’s core claims — governed SAP→Azure pipelines, prebuilt domains, lineage and the BW Analyser — map directly to common pain points we hear from CIOs and finance leaders.
That said, an announced certification and vendor marketing do not replace independent verification. The clean‑core certification program is real and meaningful; it requires partners to follow SAP‑endorsed integration approaches and to re‑certify for new SAP releases. Before committing to a platform like SparQ, organisations should:

• Confirm the SAP ICC listing and the certification scope.
• Understand the exact SAP interfaces used, and validate that they align with internal security and performance requirements.
• Run a realistic POC that exercises month‑end volumes, lineage checks and RLS/CLS enforcement.
• Model operational Azure costs and the expected savings from BW rationalisation.

If the certification and the POC both check out, SparQ should shorten migration timelines, reduce reporting inconsistency, and accelerate the safe adoption of analytics and generative AI on top of SAP data — without stuffing custom logic into the ERP core. For organisations prioritising clean core hygiene while demanding faster insight delivery, that combination is both compelling and pragmatic.
In short: the certification news is worth attention, but treat it as the beginning of technical due diligence — verify, test at scale, and align commercial commitments before you place production workloads on any new integration fabric.

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Source: The Desert Sun SparQ by Kagool Is Certified by SAP® for clean core with SAP S/4HANA Cloud