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Modernizing Enterprise Data: SAP Business Data Cloud, Databricks, and Snowflake — An Implementation Guide

Written by roman on . Posted in Careers.

Target audience: CIOs, tech leads, and digital agencies. This in-depth guide explains how to modernize enterprise data landscapes with SAP Business Data Cloud, integrated lakehouse platforms such as Databricks, and cloud data platforms like Snowflake. It covers architecture, stakeholder benefits, practical implementation advice, and market implications.

1. INTRODUCTION SUMMARY
  • SAP Business Data Cloud provides harmonized data products for analytics, planning, and AI.
  • Integration with Databricks enables zero-copy ML workflows and scalable data engineering.
  • Delta sharing and data cataloging reduce duplication and accelerate time to value.
  • Hybrid support preserves investments in BW while enabling cloud-native capabilities.
  • Adopt a component architecture to ensure scalability, governance, and operational control.
Section 1: Summary of the topic

Main Heading: Why SAP Business Data Cloud is the cornerstone for modern enterprise data fabrics

Why it matters: Consolidates SAP Datasphere, analytics, and BW capabilities into a cloud-native fabric for scalable, maintainable data products that minimize data duplication and reduce TCO.

Real-world takeaway: Enterprises with legacy BW landscapes can gradually modernize while unlocking AI/ML via integrated Databricks and lakehouse capabilities.

quadrantChart
    title Market positioning: Data Platforms
    xAxis Low --> High
    yAxis Low --> High
    quadrantTopLeft "Governance & Semantics"
    quadrantTopRight "AI/ML & Scalability"
    quadrantBottomLeft "Legacy BW & Integration"
    quadrantBottomRight "Cloud Data Platforms & Ecosystem"
    item SAP_BDC: "SAP BDC" 0.7 0.8 quadrantTopRight
    item Databricks: "Databricks" 0.9 0.9 quadrantTopRight
    item Snowflake: "Snowflake" 0.85 0.7 quadrantBottomRight
    item BW: "Existing BW" 0.3 0.4 quadrantBottomLeft
Section 2: Architecture Description

Main Heading: Component architecture for a hybrid Business Data Fabric with SAP BDC, Databricks, and Snowflake

Why it matters: Separating collection, governance, transformation, and consumption enables independent scaling, maintainability, and strong data governance.

Real-world takeaway: Implement a layered architecture where SAP BDC provides harmonized, semantically-rich data products, Databricks provides scalable engineering and ML, and Snowflake serves as an optional cloud data platform for analytical workloads and third-party consumption.

Implementation strategies
  • Design a provisioning layer that harvests metadata and exposes data products via a catalog.
  • Introduce serverless lakehouse (Databricks) for collaborative ML and zero-copy sharing using Delta Sharing.
  • Leverage Snowflake for multi-cloud analytical workloads and as a shared consumption layer.
  • Retain BW/Private Cloud for core transactional reporting during staged migration.
  • Apply centralized policy-driven governance in the data catalog and product lifecycles.

3-5 Specific steps for adopting component architecture:

  1. Conduct a Data Analytics Architecture Assessment to map current BW, Datasphere, and analytics usage.
  2. Prioritize data products and define harmonized semantic models for business-critical domains.
  3. Establish the provisioning layer: data ingestion, cataloging, and metadata harvesting.
  4. Enable Databricks integration for AI/ML with governed delta sharing and collaborative notebooks.
  5. Introduce Snowflake where multi-cloud or third-party ecosystem demands warrant a separate analytical store.
flowchart LR
    subgraph Ingest
      A[SAP & Non-SAP Sources] --> B[Provisioning Layer]
    end
    B --> C[Data Product Generator]
    C --> D[Catalog & Marketplace]
    D --> E[Consumption: SAC, BI, Insight Apps]
    C --> F[Databricks Lakehouse]
    F --> G[AI/ML & Data Engineering]
    C --> H[Snowflake / Any DB]
    H --> I[Third-party Analytics]
    E --> J[Business Users]
    G --> J
    I --> J
Section 3: Strategic Benefits for Stakeholders

Main Heading: Stakeholder value: aligning IT, data science, and business through data products

Why it matters: Enables consistent, trusted data for decision-making while reducing operational overhead and enabling new AI-driven insights.

Key performance indicators: ROI, TTI, TTM
  • Reduce Time-to-Insight (TTI) by providing curated data products and delta sharing to analytics and ML teams.
  • Improve Return-on-Investment (ROI) via reduced data duplication, faster delivery, and lower TCO.
2 key performance optimization strategies:
  1. Implement semantic onboarding and harmonized models to reduce rework and accelerate analytics.
  2. Adopt zero-copy sharing (Delta Sharing) and governed catalogs to eliminate ETL duplication and speed access.
Additional Explanation: Performance metrics should be measured across discovery-to-delivery stages: catalog search-to-use latency, data product freshness, query performance, ML experiment cycle time, and operational costs. Use monitoring tools integrated with the cloud platforms (Databricks metrics, Snowflake usage dashboards, SAP analytics logs) to quantify improvements and guide continuous optimization.

Section 4: Implementation Considerations

Main Heading: Practical implementation: migration pathways and governance guardrails

Why it matters: A phased, governed implementation reduces risk, ensures compliance, and protects existing investments.

Implementation benefits and potential risks
  • Benefit: Preserve BW investments while enabling cloud-native capabilities and AI/ML.
  • Risk: Regulatory and data sovereignty requirements may constrain multi-cloud deployments; plan for compliance.
2 Solution Highlights:

  • Data Product Generator — automates conversion of semantic models into reusable products for consumption.
  • Delta Share integration with Databricks — enables secure, zero-copy sharing for ML and analytics workloads.
Additional Explanation: Implementation scenarios include: hybrid lift-and-shift with BW private cloud for immediate continuity; phased semantic harmonization and data product rollout; and a greenfield approach using SAP BDC with Databricks and Snowflake for new analytics capabilities. For each scenario, define a minimum viable data product (MVDP) to prove value, instrument telemetry, and iterate.

graph TD
    A[Business Domains] --> B[Harmonized Semantic Models]
    B --> C[Data Product Generator]
    C --> D[Catalog & Data Marketplace]
    D --> E[Consumers: SAC, BI, ML Notebooks]
    C --> F[Databricks Delta Sharing]
    C --> G[Snowflake / Analytical Store]
    F --> H[Data Scientists]
    G --> I[External BI Tools]
    E --> J[Decision Makers]
Section 5: Market Impact and Future Implications and Conclusion

Main Heading: Market momentum: why combining SAP BDC, Databricks, and Snowflake matters

Why it matters: Efficiency gains, improved collaboration across roles, and future-proofing with AI/ML enable organizations to remain competitive.

Future-oriented guidance

Explanatory Text: Modern development practices are essential to extract ongoing business value. Recommendations:

  • Adopt product thinking for data — treat curated datasets as reusable products with SLAs and owners.
  • Invest in cross-functional teams — data engineers, scientists, business modelers, and governance roles.
  • Use CI/CD for data pipelines and model deployments; apply feature stores and experiment tracking.
  • Measure success with business KPIs tied to data product adoption and time-to-insight.
  • Plan for incremental migration paths that protect current investments while unlocking cloud scale.

Conclusion: Combining SAP Business Data Cloud with Databricks and Snowflake (where appropriate) delivers a balanced strategy for governance, analytics, and AI. That balance enables enterprises to modernize iteratively, reduce duplication, and accelerate value delivery.

If you’d like, we can perform a Data Analytics Architecture Assessment to map your current estate and design a phased migration plan tailored to your organization.