In today’s data-driven enterprises, sales teams rely on real-time insights to make faster and smarter decisions. Yet, many organizations still struggle with fragmented data landscapes—especially when dealing with complex systems like SAP CPQ (Configure Price Quote), SAP Datasphere, and advanced analytics platforms.
To solve this challenge, we designed an end‑to‑end architecture that seamlessly connects SAP CPQ, Databricks, and SAP Datasphere. The result? A unified, governed, and scalable analytics ecosystem that unlocks deeper sales insights.
This blog breaks down the business goals, architecture, and implementation blueprint behind the integration.
Birds High View. (Process View)
Why This Integration Matters
The core objective is simple but powerful:
Bring SAP CPQ quotation data into SAP Datasphere—through Databricks—to enable centralized reporting, analytics, and visualization.
This pipeline creates:
A single source of truth for CPQ dataReal‑time or near‑real‑time insightsA governed, secure, and scalable data platformSmooth interoperability across SAP and non‑SAP systems
Business Requirements at a Glance
Functional Requirements
To support modern analytics use cases, the solution must:
Extract all sales quotation records from SAP CPQ via APIsStore data in a governed, versioned Delta Lake formatShare data securely with SAP DatasphereEnable full data lineage and governanceSupport real-time or near-real-time use cases
Non‑Functional Requirements
Behind the scenes, the platform must also ensure:
Performance: Process incremental CPQ changes with <5‑minute latencySecurity: Encryption and role‑based access control throughoutScalability: Handle 100,000+ quotations per dayCompliance: GDPR and SOX adherence for sensitive financial data
Solution Architecture Overview
The integration spans four major layers: data sources, data platform, data sharing, and data consumption.
1. Data Sources
The journey begins with SAP CPQ, which exposes:
REST APIs for extracting quotation dataOAuth 2.0 authentication using client credentials
These APIs provide structured access to all quotation objects and line items.
2. Databricks – The Data Transformation Engine
Databricks acts as the data engineering and governance hub.
PySpark ETL notebooks orchestrate ingestion and transformationUnity Catalog oversees centralized governanceDelta Lake ensures ACID transactions and version-controlled storage
This allows teams to build clean, enriched, analytics-ready datasets.
3. Data Sharing: Delta Sharing + SAP BDC
For secure, open data sharing, the architecture leverages:
Delta Sharing Protocol for exchanging datasets using industry standardsSAP BDC (Business Data Connectivity) for ORD-based integration into Datasphere
The combination ensures that SAP Datasphere receives fresh, trusted data with minimal friction.
4. Consumption Layer
Finally, business users analyze the curated CPQ data through:
SAP Datasphere for modeling and semantic layeringSAP Analytics Cloud (SAC) for dashboards and reports
The result is a seamless analytics experience with governed enterprise data.
The Data Model: What’s Being Shared?
To enable deep sales analytics, the shared data model includes:
Key Entities
Quotations: Quote ID, Quote Status, Opportunity ID, Customer ID, Amount, Status, Quote Created DateLine Items: Product ID, Quantity, Unit Price, Net Price, Target Price, MarginsCustomers: Business Partners, Region, SegmentProducts: Product Name, Category, SKU
Relationships
Quotation → Line Items: One-to-manyQuotation → Customer: Many-to-one
This structure supports reporting such as win rate trends, regional sales patterns, product profitability, and more.
Integration Flows
1. Data Ingestion
Frequency: Hourly incremental loadsOne Example API Endpoint: /api/v1/quotesResilience: Retry logic with exponential backoff
2. Data Transformation in Databricks
Transformations include:
Cleansing and deduplicationEnriching quotations with master dataCreating derived metrics like:Win RateAverage Deal SizeQuote-to-close duration
3. Data Governance
To meet compliance and governance needs:
PII fields are tagged and maskedRow-level security applies by sales regionAll schema changes are version controlled through Delta Lake
Security & Compliance Framework
The pipeline is designed with enterprise-grade security:
Authentication: Service principals for API accessAuthorization: Fine-grained ACLs via Unity CatalogEncryption: TLS for in-transit, AES‑256 for at-rest dataAuditability: Complete logging of data access and transformations
This ensures audit readiness for GDPR, SOX, and internal IT security checks.
How We Measure Success
To validate business adoption and technical reliability, we track:
Data Quality
99% accuracy of quotation data
Platform Availability
99.9% uptime SLA for the pipeline
User Adoption
50+ monthly active business users in SAP Analytics Cloud
In today’s data-driven enterprises, sales teams rely on real-time insights to make faster and smarter decisions. Yet, many organizations still struggle with fragmented data landscapes—especially when dealing with complex systems like SAP CPQ (Configure Price Quote), SAP Datasphere, and advanced analytics platforms.To solve this challenge, we designed an end‑to‑end architecture that seamlessly connects SAP CPQ, Databricks, and SAP Datasphere. The result? A unified, governed, and scalable analytics ecosystem that unlocks deeper sales insights.This blog breaks down the business goals, architecture, and implementation blueprint behind the integration.Birds High View. (Process View)Why This Integration MattersThe core objective is simple but powerful:Bring SAP CPQ quotation data into SAP Datasphere—through Databricks—to enable centralized reporting, analytics, and visualization.This pipeline creates:A single source of truth for CPQ dataReal‑time or near‑real‑time insightsA governed, secure, and scalable data platformSmooth interoperability across SAP and non‑SAP systemsBusiness Requirements at a GlanceFunctional RequirementsTo support modern analytics use cases, the solution must:Extract all sales quotation records from SAP CPQ via APIsStore data in a governed, versioned Delta Lake formatShare data securely with SAP DatasphereEnable full data lineage and governanceSupport real-time or near-real-time use casesNon‑Functional RequirementsBehind the scenes, the platform must also ensure:Performance: Process incremental CPQ changes with <5‑minute latencySecurity: Encryption and role‑based access control throughoutScalability: Handle 100,000+ quotations per dayCompliance: GDPR and SOX adherence for sensitive financial dataSolution Architecture OverviewThe integration spans four major layers: data sources, data platform, data sharing, and data consumption.1. Data SourcesThe journey begins with SAP CPQ, which exposes:REST APIs for extracting quotation dataOAuth 2.0 authentication using client credentialsThese APIs provide structured access to all quotation objects and line items.2. Databricks – The Data Transformation EngineDatabricks acts as the data engineering and governance hub.PySpark ETL notebooks orchestrate ingestion and transformationUnity Catalog oversees centralized governanceDelta Lake ensures ACID transactions and version-controlled storageThis allows teams to build clean, enriched, analytics-ready datasets.3. Data Sharing: Delta Sharing + SAP BDCFor secure, open data sharing, the architecture leverages:Delta Sharing Protocol for exchanging datasets using industry standardsSAP BDC (Business Data Connectivity) for ORD-based integration into DatasphereThe combination ensures that SAP Datasphere receives fresh, trusted data with minimal friction.4. Consumption LayerFinally, business users analyze the curated CPQ data through:SAP Datasphere for modeling and semantic layeringSAP Analytics Cloud (SAC) for dashboards and reportsThe result is a seamless analytics experience with governed enterprise data.The Data Model: What’s Being Shared?To enable deep sales analytics, the shared data model includes:Key EntitiesQuotations: Quote ID, Quote Status, Opportunity ID, Customer ID, Amount, Status, Quote Created DateLine Items: Product ID, Quantity, Unit Price, Net Price, Target Price, MarginsCustomers: Business Partners, Region, SegmentProducts: Product Name, Category, SKURelationshipsQuotation → Line Items: One-to-manyQuotation → Customer: Many-to-oneThis structure supports reporting such as win rate trends, regional sales patterns, product profitability, and more.Integration Flows1. Data Ingestion Frequency: Hourly incremental loadsOne Example API Endpoint: /api/v1/quotesResilience: Retry logic with exponential backoff2. Data Transformation in DatabricksTransformations include:Cleansing and deduplicationEnriching quotations with master dataCreating derived metrics like:Win RateAverage Deal SizeQuote-to-close duration3. Data GovernanceTo meet compliance and governance needs:PII fields are tagged and maskedRow-level security applies by sales regionAll schema changes are version controlled through Delta LakeSecurity & Compliance FrameworkThe pipeline is designed with enterprise-grade security:Authentication: Service principals for API accessAuthorization: Fine-grained ACLs via Unity CatalogEncryption: TLS for in-transit, AES‑256 for at-rest dataAuditability: Complete logging of data access and transformationsThis ensures audit readiness for GDPR, SOX, and internal IT security checks.How We Measure SuccessTo validate business adoption and technical reliability, we track:Data Quality99% accuracy of quotation dataPlatform Availability99.9% uptime SLA for the pipelineUser Adoption50+ monthly active business users in SAP Analytics Cloud Read More Technology Blog Posts by SAP articles
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