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SAP BW Data Product Generator: Bridging Traditional Data Warehousing with Modern Cloud Analytics

Written by roman on . Posted in Analytics & Business Intelligence.

Enterprise data teams face a critical challenge: how to unlock the wealth of information stored in SAP Business Warehouse systems while embracing modern cloud analytics platforms. The SAP BW Data Product Generator emerges as a transformative solution, enabling organizations to bridge the gap between traditional data warehousing and cutting-edge analytics ecosystems like Databricks and Snowflake.

This innovative tool fundamentally changes how enterprises approach data integration, moving beyond complex ETL processes to create seamless data products that serve both legacy and modern consumption patterns. For businesses heavily invested in SAP infrastructure, the BW Data Product Generator represents a strategic pathway to modernization without sacrificing existing investments.

Key Strategic Benefits

  • Simplified data replication from BW to cloud environments
  • Zero-copy consumption enabling secure external platform integration
  • Automated data product creation reducing manual configuration overhead
Embrace Component-Based Data Architecture

Traditional data warehousing architectures create monolithic systems that struggle to adapt to modern analytics demands. The BW Data Product Generator introduces a component-based approach that transforms rigid data structures into flexible, reusable data products.

This architectural shift matters because it addresses the fundamental challenge of data accessibility. Instead of forcing business users to work within the constraints of legacy systems, the tool creates bridge components that expose SAP data in formats compatible with modern analytics platforms.

Real-world implementation: Organizations can now create focused data products from their BW InfoProviders—whether InfoCubes, DataStore Objects, or Composite Providers—that serve specific business use cases while maintaining centralized governance and security controls.

graph TB
    A[SAP BW System] --> B[BW Data Product Generator]
    B --> C[SAP Datasphere]
    C --> D[LocalTable Files]
    D --> E[Data Products]
    E --> F[Databricks]
    E --> G[Snowflake]
    E --> H[SAP Analytics Cloud]
    E --> I[Other Analytics Platforms]
    
    A --> A1[InfoCubes]
    A --> A2[DataStore Objects]
    A --> A3[InfoObjects]
    A --> A4[Composite Provider]
    A --> A5[MultiProvider]
    A --> A6[Query-as-InfoProvider]
    
    style A fill:#0f4c75
    style B fill:#3282b8
    style C fill:#bbe1fa
    style E fill:#1b262c
    style F fill:#ff6b6b
    style G fill:#4ecdc4
    style H fill:#45b7d1
Strategic Implementation Framework
  • Assessment Phase: Catalog existing InfoProviders and identify high-value datasets suitable for cloud consumption
  • Subscription Design: Create focused data subscriptions that balance data completeness with performance requirements
  • Process Integration: Embed data replication into existing BW Process Chains for seamless orchestration
Implement Performance Optimization Through Object Store Architecture

Performance optimization in modern data architectures requires moving beyond traditional database-centric approaches. The BW Data Product Generator leverages SAP Datasphere’s managed object store (HDLFS) to deliver superior query performance while reducing infrastructure complexity.

This approach fundamentally changes how data access patterns work. Instead of network-intensive database queries, the system enables direct file-based access through HANA Cloud SQL-on-file technology, dramatically improving response times for analytical workloads.

Advanced Performance Strategies
  • Delta Processing Implementation: Configure incremental updates for InfoProviders supporting delta functionality to minimize processing windows
  • Intelligent Filtering: Apply field selection and filter conditions during subscription creation to reduce data transfer volumes

The performance impact extends beyond raw speed metrics. Organizations experience significant improvements in analytical query response times when consuming LocalTable (File) objects, thanks to optimized data layouts and intelligent caching mechanisms built into the object store architecture.

sequenceDiagram
    participant BW as SAP BW System
    participant DPG as BW Data Product Generator
    participant DS as SAP Datasphere
    participant OS as Object Store (HDLFS)
    participant DB as Databricks
    participant Consumer as Analytics Consumer
    
    BW->>DPG: Create Subscription
    DPG->>BW: Select InfoProviders
    DPG->>BW: Apply Filters & Field Selection
    BW->>DPG: Extract Data
    DPG->>DS: Create LocalTable (File)
    DS->>OS: Store Data in Object Store
    DS->>DS: Create Data Product
    DS->>DB: Share via DeltaShare
    DB->>Consumer: Zero-Copy Access
    Consumer->>OS: Query Data Directly
    
    Note over DPG,DS: Metadata Preservation
    Note over DS,DB: Security & Governance
    Note over OS,Consumer: SQL-on-File Access
Prioritize Security Through Zero-Trust Data Sharing

Security considerations become paramount when extending enterprise data beyond traditional boundaries. The BW Data Product Generator implements a zero-trust security model that maintains data sovereignty while enabling innovative consumption patterns across multiple platforms.

Traditional data sharing approaches often compromise security by creating data copies outside organizational control. The DeltaShare protocol used by the BW Data Product Generator revolutionizes this approach by keeping actual data within the SAP Business Data Cloud environment while providing secure, governed access to external platforms.

Enterprise Security Framework
  • Role-Based Access Control: Implement granular access policies through Datasphere’s native security framework for different user personas
  • Comprehensive Audit Trails: Maintain complete visibility into data access patterns and consumption activities across all platforms

Security vulnerabilities typically emerge at system integration points. The BW Data Product Generator addresses this challenge through encrypted data transmission, certificate-based authentication, and field-level filtering capabilities that enable selective data exposure without compromising sensitive information.

graph LR
    A[SAP BW Data] --> B[BW Data Product Generator]
    B --> C[Encrypted Transmission]
    C --> D[SAP Datasphere]
    D --> E[Security Layer]
    E --> F[Data Sharing Cockpit]
    F --> G[DeltaShare Protocol]
    G --> H[External Platforms]
    
    E --> E1[Role-Based Access]
    E --> E2[Field-Level Filtering]
    E --> E3[Audit Logging]
    E --> E4[Certificate Authentication]
    
    H --> H1[Databricks]
    H --> H2[Snowflake]
    H --> H3[Other Analytics Tools]
    
    style A fill:#0f4c75
    style D fill:#3282b8
    style E fill:#ff6b6b
    style G fill:#4ecdc4
    style H fill:#45b7d1
Adopt Modern Development Workflows with Automated Data Product Creation

Modern development workflows demand automation, collaboration, and agile development practices. The BW Data Product Generator transforms traditional data warehouse development from manual, error-prone processes into streamlined, automated workflows that support rapid analytics development.

The efficiency gains are substantial: development teams can create subscription-based data products directly from familiar BW editors—SAP GUI for BW 7.5 systems or Fiori UI for BW/4HANA environments—automatically generating the necessary artifacts in Datasphere without complex manual configuration.

Workflow Optimization Strategy

Implementing modern development workflows requires careful consideration of organizational change management and technical implementation patterns. The BW Data Product Generator supports various deployment scenarios, from simple one-time snapshots for historical data migration to sophisticated delta processing workflows for real-time analytics requirements.

The collaborative aspects extend to cross-functional teams working with both traditional BI tools and modern analytics platforms. Data engineers establish data products through familiar BW interfaces, while data scientists gain access to the same datasets through Databricks or other connected platforms, eliminating traditional silos between analytical user communities.

timeline
    title Implementation Timeline
    section Assessment Phase
        Week 1-2    : InfoProvider Inventory
                   : Performance Baseline
        Week 3-4    : Use Case Identification
                   : Technical Requirements
    section Pilot Phase
        Week 5-6    : Subscription Creation
                   : Process Chain Integration
        Week 7-8    : Security Configuration
                   : Performance Testing
    section Production Phase
        Week 9-10   : Delta Processing Setup
                   : Monitoring Implementation
        Week 11-12  : User Training
                   : Go-Live Support
    section Optimization Phase
        Week 13-14  : Performance Tuning
                   : Scaling Strategy
        Week 15-16  : Future Enhancements
                   : Roadmap Planning

Technical Architecture Deep Dive

Understanding the technical foundations of the SAP BW Data Product Generator helps organizations make informed decisions about implementation strategies and operational considerations. The tool represents a sophisticated integration between traditional data warehousing technologies and modern cloud-native architectures.

Supported InfoProvider Types

The BW Data Product Generator supports comprehensive InfoProvider types, ensuring broad compatibility with existing SAP landscapes:

  • Base Providers: InfoCubes, DataStore Objects (Classic and Advanced), and InfoObjects for master data management
  • Composite Structures: Composite Providers and MultiProvider configurations for complex data relationships
  • Query-Based Objects: Query-as-InfoProvider for pre-aggregated analytical datasets
graph TD
    A[SAP BW InfoProviders] --> B[Base Providers]
    A --> C[Composite Structures]
    A --> D[Query-Based Objects]
    
    B --> B1[InfoCubes]
    B --> B2[DataStore Objects Classic]
    B --> B3[DataStore Objects Advanced]
    B --> B4[InfoObjects Master Data]
    
    C --> C1[Composite Provider]
    C --> C2[MultiProvider]
    
    D --> D1[Query-as-InfoProvider]
    
    B1 --> E[BW Data Product Generator]
    B2 --> E
    B3 --> E
    B4 --> E
    C1 --> E
    C2 --> E
    D1 --> E
    
    E --> F[SAP Datasphere]
    F --> G[LocalTable Files]
    
    style A fill:#0f4c75
    style E fill:#3282b8
    style F fill:#bbe1fa
    style G fill:#1b262c
Platform Requirements and Availability

Implementation requires specific SAP platform versions and deployment models to ensure optimal performance and support:

  • SAP BW 7.50 SP24 or higher – Available through SAP Note Transport-based Correction Instruction (TCI)
  • SAP BW/4HANA 2021 SP4 or higher – Includes integrated Fiori UI for enhanced user experience
  • SAP Business Warehouse private cloud edition – Exclusive deployment requirement for optimal integration

Important: The BW Data Product Generator is exclusively available for SAP Business Warehouse private cloud edition systems deployed in SAP’s private cloud as stand-alone installations. This restriction ensures optimal integration with SAP Business Data Cloud infrastructure and maintains enterprise-grade security and performance standards.

Usage Scenarios and Business Applications

The BW Data Product Generator enables two primary usage scenarios that address different organizational needs and strategic objectives.

New Consumption Scenarios

The primary use case focuses on enabling new consumption patterns based on existing BW data investments. The most prominent scenario involves zero-copy consumption of BW data in Databricks through DeltaShare protocol, enabling machine learning algorithms to operate on current SAP data without replication delays or security compromises.

Analytical consumption represents another significant use case. LocalTable (File) objects created in the BW space can be shared to consumption spaces in Datasphere, where teams build Views and Analytic Models that combine BW data with information from other sources, including SAP-managed DataProducts.

BW Scenario Replacement

While new consumption scenarios represent the primary use case, the BW Data Product Generator also supports strategic BW modernization initiatives. Organizations can leverage the tool to migrate legacy scenarios from BW to Datasphere, particularly for historical data that no longer requires active maintenance in the BW environment.

For complete data flow replacement scenarios, the BW Data Product Generator can create necessary persistency objects and perform initial loads of historical data. New data flows in Datasphere then combine BW Data Product Generator data with recent information streams, creating hybrid architectures that maximize existing investments while embracing modern capabilities.

Future Roadmap and Strategic Considerations

SAP’s commitment to evolving the BW Data Product Generator includes several planned enhancements that will further simplify implementation and expand capabilities:

  • Mass Object Selection: Automated identification and inclusion of related InfoProviders and master data objects for complete scenario migration
  • InfoArea Hierarchy Preservation: Maintain organizational structures through Datasphere folder hierarchies that reflect BW InfoArea organization
  • Multi-Space Support: Enable data segregation through multiple BW spaces in Datasphere for different organizational units or security requirements
  • Enhanced Process Integration: Deeper integration between BW Process Chains and Datasphere Task Chains for seamless workflow orchestration
mindmap
  root((BW Data Product Generator Future))
    Mass Object Selection
      Complete Scenario Migration
      Master Data Automation
      Dependency Resolution
    InfoArea Hierarchy
      Folder Structure Mapping
      Organizational Alignment
      Navigation Consistency
    Multi-Space Support
      Data Segregation
      Security Boundaries
      Organizational Units
    Process Integration
      BW Process Chains
      Datasphere Task Chains
      Workflow Orchestration
    Platform Expansion
      Snowflake Integration
      Additional Analytics Tools
      API-First Architecture
    Enhanced Security
      Advanced Filtering
      Dynamic Masking
      Compliance Features

Implementation Best Practices

Successful BW Data Product Generator implementation requires careful planning and execution. Organizations should approach deployment with clear understanding of their current data landscape, target architecture, and business objectives.

Phase 1: Assessment and Planning

Begin with comprehensive analysis of existing BW implementation:

  • InfoProvider Inventory: Catalog all eligible InfoProviders, assess data volumes, update frequencies, and business criticality
  • Process Chain Analysis: Identify optimal integration points for subscription execution within existing workflows
  • Performance Baseline: Establish current system performance metrics for post-implementation comparison
Phase 2: Pilot Deployment

Execute controlled pilot with non-critical data to validate concepts and establish operational patterns:

  • Subscription Creation: Develop reusable subscription templates for different InfoProvider types and data patterns
  • Filter Optimization: Implement field selection and filtering strategies to minimize data transfer volumes
  • Security Validation: Test data product sharing mechanisms and access control implementations
Phase 3: Production Scaling

Expand implementation to business-critical datasets with enhanced monitoring and optimization:

  • Delta Processing Implementation: Configure incremental updates for high-frequency data changes
  • Monitoring and Alerting: Establish comprehensive monitoring for subscription execution and data quality
  • Performance Optimization: Fine-tune execution schedules to minimize system impact and maximize efficiency

Conclusion: Transforming Enterprise Data Strategy

The SAP BW Data Product Generator represents more than a technical integration tool—it embodies a strategic approach to modernizing enterprise data architectures while preserving existing investments. By enabling seamless integration between traditional SAP Business Warehouse systems and modern cloud analytics platforms like Databricks and Snowflake, organizations can accelerate digital transformation initiatives while maintaining operational stability.

The key to success lies in thoughtful implementation that balances innovation with operational excellence. Organizations that embrace the component-based architecture enabled by the BW Data Product Generator position themselves to leverage emerging technologies like artificial intelligence and machine learning while preserving the governance and security standards essential for enterprise operations.

As the enterprise data landscape continues evolving toward cloud-native, API-first architectures, the BW Data Product Generator provides a proven path for SAP customers to participate in this transformation without disrupting core business processes. The tool’s current integration with Databricks and planned compatibility with other analytics ecosystems ensures that organizations can adapt to changing technology requirements while maximizing their existing SAP investments.