An SAP BW migration is the process of moving your SAP Business Warehouse (BW 7.5) environment – including all data models, transformation logic, and reporting structures – to a modern target platform before SAP ends mainstream maintenance on December 31, 2027. Extended maintenance is available at a premium until 2030, but staying past that means no new capabilities, growing security exposure, and rising costs to keep an aging system alive.

• Mainstream maintenance for SAP BW 7.5 ends December 31, 2027
• Extended maintenance runs until 2030 at an additional cost of roughly 2% of license value per year
• Over 60% of SAP transformation projects exceed both budget and schedule (Horváth 2025 study)
• Only about 15% of SAP migrations finish on time and on budget
• New SAP capabilities now flow exclusively into BW/4HANA, SAP Datasphere, and SAP Business Data Cloud

An SAP BW migration is fundamentally a knowledge and logic re-engineering problem, not just a data move. SAP’s modern platforms are SQL-first, meaning every ABAP start routine, end routine, and field-level transformation rule must be understood, matched to the business question it answers, and rebuilt in a new language. Most BW environments contain 10 to 20 years of organic growth, thousands of undocumented objects, and business logic created by people who have since left the organization.

• Decades of custom ABAP logic embedded in transformations and routines
• Thousands of InfoProviders, DataStore Objects, and MultiProviders – often poorly documented
• Complex dependencies between data flows, reports, and front-end tools like SAC or Power BI
• Business-critical KPIs that must produce identical results after migration
• The classic star schema and InfoCube model is architecturally incompatible with modern cloud platforms

ABAP routines in SAP BW transformations – start routines, end routines, and field-level rules – do not run natively on modern cloud platforms like Snowflake, Databricks, or SAP Datasphere. They must be extracted, analyzed for the business intent they encode, and then translated into a target-compatible language such as SQL, Python, or PySpark. Ignoring this step is one of the most common migration mistakes, leading to inconsistent KPIs and missing business logic in the new system.

• ABAP code is not executable on cloud-native platforms
• Manual ABAP reverse-engineering is slow, error-prone, and expensive
• AI-assisted tools can automate ABAP-to-SQL/Python/PySpark conversion and validate results against the source logic
• A typical rule of thumb puts the cost of manually translating a single code line at around €20
• Savings of 50–60% on manual effort are achievable through automation

Lift-and-shift is a migration approach where existing SAP BW objects are transferred 1:1 into a new target system without redesign or scope reduction. While it offers the fastest start, it carries the highest long-term cost and risk because all legacy debt – unused reports, redundant data flows, undocumented logic, and duplicate processes – migrates with it. In pay-per-use cloud models, this redundancy becomes an ongoing, multiplying expense.

• In grown SAP BW landscapes, 40–70% of objects are typically barely used
• Cloud storage and compute costs scale with data volume and complexity
• Migrating unused objects costs twice: once during migration, then continuously in cloud resources
• No governance or monitoring means the same data sprawl quickly returns
• On-premises costs are fixed; cloud costs are variable and consumption-based – redundancy makes them skyrocket

Lift-and-shift transfers existing SAP BW objects 1:1 to the target, brownfield modernization preserves and selectively optimizes existing logic, greenfield rebuilds the data model from scratch based on current business requirements, and hybrid combines existing BW logic with newly designed data structures. The right approach depends on the quality and business value of the existing BW environment, as well as the target architecture and migration goals.

Most organizations benefit from a brownfield or hybrid approach when existing logic still has value but is buried under years of complexity and redundancy. One Data recommends greenfield when the existing model no longer reflects how the business operates.

Cloud platforms use consumption-based billing where every query, every stored byte, and every compute cycle is metered. On-premises SAP BW costs are fixed and predictable regardless of data volume, but once you move to the cloud, carrying legacy redundancy – unused reports, duplicate data flows, and orphaned objects – causes compute and storage costs to escalate exponentially. Without governance, this cost spiral accelerates as new data sprawl accumulates on top of migrated legacy waste.

• Cloud costs are variable and consumption-based, not fixed
• Redundant logic requires more processing power and storage
• Unused reports still consume resources and maintenance effort
• Without a FinOps function or governance model, costs spiral before anyone notices
• Rationalizing before migration is the single most effective cost-control lever

SAP BW metadata is the structural and descriptive information about every object in your BW landscape – including object names, descriptions, dependencies, data lineage, transformation logic, and usage statistics. It provides the foundation for understanding what your BW system does, which objects are relevant, and how data flows through transformations to reporting endpoints. Without metadata analysis, you cannot make informed decisions about what to migrate, consolidate, or retire.

• Metadata reveals the complete “skeleton” of your SAP BW architecture
• Usage statistics show which reports and queries are used in production
• Dependency mapping exposes how objects relate to each other and to front-end tools
• ABAP source code can be extracted alongside metadata for automated conversion
• Metadata analysis replaces months of manual inventory work with automated transparency

A data product is a self-contained, reusable data asset that bundles the underlying data with the business rules that produce it, the definitions that give it meaning, and the quality contracts that ensure it stays reliable. For SAP BW migration, thinking in data products instead of BW objects fundamentally changes how you scope, prioritize, and govern the migration – shifting the focus from technical completeness to business value.

It’s worth distinguishing this from SAP’s definition of data products. SAP data products are semantically aligned data packages designed primarily for self-service consumption within the SAP Business Data Cloud and Datasphere ecosystem. They can include SAP-managed content or custom data combined from SAP and non-SAP sources.

One Data takes a broader, platform-independent approach: a data product is not just a packaged dataset for consumption. It captures the data, business logic, semantic definitions, ownership, lineage, and quality requirements as one reusable asset – independently of where the data is ultimately stored or consumed.

For an SAP BW migration, this means:

• Governance is built-in quality standards, freshness thresholds, ownership, and lineage travel with the data product.
• Business logic becomes reusable: transformation rules and definitions are captured independently of the underlying platform.
• Migration becomes more flexible: the same data product can be deployed to SAP Business Data Cloud today and another environment tomorrow.
• Reuse replaces duplication: teams can consume the same trusted data product instead of rebuilding similar logic in different systems.

The result is a shift from “Which BW objects do we migrate?” to “Which business-ready data products do we need, and where should they live?”

Gartner has recognized One Data as a Sample Vendor for Data Products and Data contracts.

AI models and agents are only as reliable as the data they consume. Deploying AI on top of fragmented, undocumented, or inconsistent does not produce artificial intelligence – it produces artificial hallucinations. The BARC Data, BI and Analytics Trend Monitor for 2026 confirms this: data quality management leads the enterprise priority list at 7.9 out of 10, while generative AI scores only 5.5. Data warehouse modernization sits at 6.1, ahead of AI priorities.

• AI agents interpret data meaning – if context and semantics are missing, results are unreliable
• Data quality and security jointly lead enterprise priorities at 7.9/10 (BARC 2026)
• Data warehouse modernization (6.1/10) outranks generative AI (5.5/10) and agentic AI (4.5/10)
• Modernization demand has been a top-8 priority every year from 2022–2026 – it is structural, not cyclical
• ISG research confirms that AI payoff after a migration depends on well-governed data and integration of SAP with non-SAP sources

SAP ends mainstream maintenance for SAP BW 7.5 on December 31, 2027. Companies running BW on HANA may negotiate an extension, but even that only pushes the deadline to the end of 2030 – at an annual premium of roughly 2% of the license value. After these dates, staying on classic BW means no new capabilities, growing security and compliance exposure, phase-out of Excel-based BEx front ends, and difficulty finding skilled personnel.

• Mainstream maintenance ends: December 31, 2027
• Extended maintenance (at premium): ends December 31, 2030
• All new SAP capabilities flow into BW/4HANA, Datasphere, and Business Data Cloud
• This timeline creates urgency – as the deadline approaches, the value of any accelerating tool multiplies
• Projects run 30% longer than planned on average (Horváth 2025)

The most common target platforms for SAP BW migration are BW/4HANA, SAP Datasphere, SAP Business Data Cloud (BDC), Snowflake, Databricks, and Microsoft Fabric. Within the SAP ecosystem, Datasphere and BDC offer continuity and deep integration. Open platforms like Snowflake and Databricks bring advantages for AI/ML workloads, cross-platform analytics, and platform independence. Many organizations choose hybrid architectures that combine both.

• BW/4HANA: Most conservative; stays close to existing landscape but can carry forward legacy debt. Support through 2040 extends the runway but does not eliminate the need for modernization.
• SAP Datasphere / SAP Business Data Cloud: Brownfield modernization within the SAP ecosystem
• Snowflake: Popular for open cloud data warehousing; flat, wide models; SQL-native
• Databricks: Strengths in AI/ML, Lakehouse architecture, PySpark/Python ecosystem
• Microsoft Fabric: Integrated analytics on Azure; growing in Microsoft-centric organizations
• Hybrid: Most common in practice – SAP-adjacent processes in BDC/Datasphere, analytics and AI workloads on Snowflake or Databricks

No – a blind copy is risky and expensive. Modern cloud Lakehouses expect flat, wide data models, not the rigid pre-aggregated InfoCubes that SAP BW uses. A straight lift-and-shift carries forward redundant and obsolete data, inflates storage and compute costs, and exposes you to two additional traps: ODP extraction constraints (no longer certified for non-SAP consumers) and SAP indirect-usage licensing fees that can trigger penalties when SAP-generated data is read by third-party systems.

• Modern platforms reject rigid pre-aggregated structures in favor of flat, wide models
• ODP (Operational Data Provisioning) is no longer certified for non-SAP consumers
• SAP’s digital access model can trigger licensing fees for data copied to non-SAP systems
• Consolidate, flatten, and selectively migrate only business-critical data before it leaves SAP
• A migration optimization platform can scan, rationalize, and selectively migrate high-value data and logic

SAP’s indirect usage (or digital access) licensing model can trigger fees when SAP-generated data is read by or copied to a non-SAP system – effectively a “data-duplication tax.” This is a significant hidden cost risk in any SAP BW migration to non-SAP targets like Snowflake or Databricks. To limit exposure, consolidate and selectively migrate only business-critical data before it leaves the SAP environment, rather than replicating the full BW footprint.

• Applies when SAP data is accessed by third-party, non-SAP systems
• Can result in surprise audit penalties if not managed proactively
• The risk increases with the volume of data replicated outside SAP
• Rationalizing and flattening data before migration is the primary mitigation strategy
• A Cloud Center of Excellence or FinOps function helps govern these variable costs

ODP (Operational Data Provisioning) is SAP’s traditional interface for extracting data from SAP systems to downstream consumers. SAP has indicated that ODP is no longer certified for non-SAP consumers going forward, which means existing extraction pipelines built on ODP become “condemned infrastructure.” Organizations must redesign toward CDS views (Core Data Services) and modern APIs – an effort that is often large, technically demanding, and unbudgeted.

• ODP was the standard extraction interface for SAP BW data
• It is no longer certified for non-SAP target systems
• Existing ODP-based pipelines require redesign to CDS views and modern APIs
• This redesign effort is frequently unbudgeted and catches organizations off guard
• Migration optimization tools help scope and automate the redesign

Rationalize before you migrate – this is the single most important principle. On-premises costs are fixed, but cloud billing is consumption-based, so carrying legacy redundancy into the cloud makes compute and storage costs skyrocket. The proven approach is to retire unused objects, flatten redundant models, and establish governance (such as a FinOps function or Cloud Center of Excellence) to control variable spending from day one.

• Conduct a usage and dependency analysis before migration to identify waste
• Remove the 40–70% of objects that are typically barely used in grown BW landscapes
• Set up ongoing monitoring dashboards to track data usage and prevent future bloat
• Establish a FinOps function or Cloud Center of Excellence for cost governance
• Plan migration in waves organized around business value, not technical completeness

One Data is a data product platform specifically built for the challenges of complex SAP BW migrations. It functions as a migration optimization layer between your existing BW system and the modern target platform – combining automated BW landscape analysis, data product modeling, AI-assisted ABAP-to-SQL/Python/PySpark conversion, and continuous governance in a single platform. Supported targets include Snowflake, Databricks, Microsoft Fabric, SAP Business Data Cloud, and SAP BTP including HANA.

• Automated metadata extraction, usage analysis, dependency visualization, and redundancy detection
• AI-assisted ABAP-to-SQL/Python/PySpark conversion with validation against source logic
• Data product candidates derived from existing BW structures and usage patterns
• Target structure generation for Snowflake, Databricks, and SAP Business Data Cloud
• Post-migration governance: data product catalog, lineage, metadata, and documentation
• Gartner mentioned One Data as a sample vendor for data products and data contracts (Hype Cycle for Data Management 2024 and 2025)

The most important criteria go well beyond classic ETL capabilities. Evaluate whether the tool can fully analyze your existing BW landscape (including usage, dependencies, and metadata), how it handles ABAP logic (extracted and translated automatically, or manual?), whether it supports multiple target platforms or locks you into one, and whether post-migration governance – documentation, lineage, ownership – stays usable in the target system.

• Analysis and transparency: Full BW landscape analysis including usage and dependency mapping
• ABAP handling: Automated extraction, translation, and validation – not manual rewrite
• Target platform flexibility: Multi-target support (Snowflake, Databricks, SAP BDC) vs. single-platform lock-in
• Business prioritization: Migration along use cases and data products, not just technical objects
• Post-migration governance: Documentation, lineage, and ownership that persist in the target system
• Automation depth: How much repeatable work is automated vs. remaining manual

One Data replaces the traditional months-long manual consulting study with automated analysis that delivers results in days rather than months. A typical manual SAP BW assessment requires extensive reverse engineering – checking each object for relevance, usage, and dependencies. One Data’s platform automatically scans the entire BW architecture, maps technical debt, visualizes data flows and dependencies, and identifies unused objects – providing the same transparency in a fraction of the time, with 60% less effort and cost on average.

• Traditional approach: months of manual inventory and reverse engineering by consultants
• One Data approach: automated metadata extraction and landscape analysis in days
• Manual ABAP translation: ~€20 per code line, multiplied across thousands or millions of lines
• Automated translation: 50–60% reduction in manual effort, with validated output
• The four-step framework: Analyze → Classify → Transform → Advise

The SAP BW X-Ray is a free assessment package from One Data that combines a fully automated diagnostic tool with expert coaching to provide instant visibility into your SAP BW landscape. In a 6-hour hands-on coaching session, One Data’s experts parameterize the X-Ray tool for your environment, producing a complete “skeleton mapping” that shows exactly what to keep, consolidate, or retire – all without risk, system changes, or commitment.

• Automated skeleton mapping of your entire BW system in hours, not months
• Personalized coaching session to configure the tool for your specific environment
• Identifies redundant reports, unused logic, and consolidation opportunities
• Generates a simplification roadmap based on actual usage patterns
• Sets up ongoing monitoring dashboards to track data usage and prevent future bloat
• Requires only read-only access to SAP BW metadata and ABAP code

SAP BW migration timelines vary significantly – from a few months for focused modernizations to multiple years for large, historically grown landscapes. The decisive factors are scope definition, ABAP complexity, target platform choice, internal capacity, and whether you use a phased wave approach or attempt a big-bang migration. A rigorous upfront analysis makes time and cost estimates far more reliable, and automated tools can compress months of manual work into weeks.

• Small, focused migrations: a few months
• Large, complex landscapes: 2–4 years
• Projects run 30% longer than planned on average (Horváth 2025)
• A clean pre-analysis (metadata, usage, dependencies) makes estimates more reliable
• Wave-based migrations deliver value incrementally and reduce risk vs. big-bang approaches

A semantic migration approach means understanding the business meaning behind your SAP BW objects before moving them – rather than translating code line by line. Instead of treating migration as a technical code-conversion exercise, you capture the business intent each transformation serves, package it into governed data products, and then generate clean, optimized target code from that understanding. This produces a target environment that is cleaner and more trustworthy than the system it replaces.

• Starts from business meaning and intent, not ABAP syntax
• Produces platform-independent data products, not platform-specific code
• AI agents work from captured business intent to generate clean SQL or Python
• Results: compressed timelines, reduced cost, and a cleaner target environment
• The semantic model becomes the organization’s living documentation of its data landscape