27 Oct 2026

SAP BW to Databricks Migration: Strategy, Challenges & a Proven Path to the Lakehouse

Quick Summary

SAP BW to Databricks migration involves transferring data while rebuilding data models, ABAP routines, transformations, process chains, and reporting logic. A successful migration requires a clear inventory of existing SAP BW objects, the right data integration approach, conversion of business logic into PySpark or Spark SQL, and thorough validation to preserve data accuracy and reporting consistency.

SAP Business Warehouse has served as the reporting backbone for SAP-centric enterprises for more than two decades. However, rising infrastructure and maintenance costs, changing analytics requirements, and the need to combine SAP data with non-SAP sources are prompting organizations to reassess their data platforms.

For organizations running SAP NetWeaver BW 7.5, mainstream maintenance is scheduled through December 2027, with optional extended maintenance through December 2030. Maintenance dates and options vary by SAP product and version, so organizations should verify the roadmap for their specific environment.

SAP BW to Databricks migration is one potential modernization path. Databricks enables organizations to combine SAP data with CRM, e-commerce, IoT, and other enterprise data on a Lakehouse platform that supports data engineering, analytics, and machine learning.

However, a straightforward table copy is rarely sufficient. SAP BW contains business logic embedded in InfoObjects, transformations, ABAP routines, delta processing, hierarchies, and BEx queries. If you don't reconstruct and validate these elements, the migrated data may be technically accessible but produce inconsistent business reports.

This guide explains why organizations consider SAP BW migration, which workloads to assess, how to choose a migration approach, what challenges to expect, and how to follow a phased migration strategy that protects data quality and business continuity.

Why Organizations Migrate SAP BW to Databricks

Organizations usually consider SAP BW modernization when the existing platform no longer meets their cost, integration, analytics, or operational requirements.

1. Maintenance and modernization planning

Maintenance timelines can influence the decision to retain, upgrade, or modernize an SAP BW environment. Organizations should assess the exact product version, available maintenance options, dependencies, and internal roadmap before choosing a target platform.

A migration decision should be based on business and technical requirements—not on a maintenance date alone.

2. Infrastructure costs and specialist skills

SAP BW environments may require specialized HANA infrastructure, platform administration, and SAP BW or ABAP expertise. Maintaining complex legacy transformations and reporting dependencies can also make enhancements time-consuming.

A modern platform may simplify parts of the data engineering process, but cost outcomes depend on workload design, cloud consumption, licensing, optimization, and the systems that remain in operation. Establish a baseline before estimating potential savings.

3. Combining SAP and non-SAP data

Many organizations need to analyze SAP ERP data alongside CRM records, digital interactions, supply-chain feeds, e-commerce transactions, and external datasets.

Databricks can bring these sources together in a governed Lakehouse environment, enabling broader analytics across business domains.

4. Supporting advanced analytics and AI

Organizations may want to use SAP business data for forecasting, anomaly detection, inventory optimization, customer analytics, and machine learning.

A Lakehouse architecture can provide a shared foundation for data engineering, analytics, and AI workloads. The quality of these use cases still depends on data quality, business definitions, access controls, and appropriate model validation.

5. Open data formats and platform flexibility

Delta Lake uses Parquet-based storage and adds capabilities such as transactional consistency. This can support interoperability and reduce dependence on proprietary warehouse storage formats, although platform services, governance, and workload dependencies still need to be considered.

6. SAP and Databricks integration options

SAP Business Data Cloud and Databricks provide data-sharing capabilities for supported SAP data products. In eligible scenarios, these options can reduce the need for traditional data replication.

They do not automatically replace every SAP BW extraction, transformation, reporting, or migration requirement. Organizations must assess which data products are available and whether the integration meets their needs.

Which SAP BW Workloads Migrate to Databricks?

Each SAP BW component serves a different purpose and requires a suitable target design.

SAP BW component	Potential Databricks equivalent	Migration consideration
InfoCubes, DSOs, and Advanced DSOs	Delta tables and Lakehouse data models	Preserve keys, history, grain, and data relationships.
InfoObjects and master data	Conformed dimension tables	Retain attributes, texts, compound keys, and time-dependent values.
Hierarchies	Parent-child or flattened hierarchy tables	Preserve versions, validity periods, and reporting roll-ups.
Transformations, DTPs, and ABAP routines	PySpark or Spark SQL transformations	Rebuild logic and test edge cases.
Process chains	Databricks workflow orchestration, such as Lakeflow Jobs	Recreate dependencies, schedules, retries, and monitoring.
BEx queries and CompositeProviders	SQL views, semantic models, and BI reports	Reproduce key figures, variables, filters, and aggregation behavior.
BW analysis authorizations	Unity Catalog permissions and applicable row or column controls	Test access by user role and business context.

These are design mappings, not automatic one-to-one conversions. The appropriate target depends on each object's function, the existing implementation, and the reporting requirements.

What Actually Has to Move?

A complete SAP BW migration inventory should cover five layers.

1. Data extraction and source systems

Identify DataSources, ODP extractors, source tables, full-load requirements, delta mechanisms, and dependencies on SAP ECC or S/4HANA.

Before designing pipelines, confirm the supported extraction or sharing method, licensing terms, source-system impact, and data freshness requirements.

2. Data models and semantics

Inventory InfoProviders, InfoObjects, master data, hierarchies, SID and dimension relationships, and historical validity rules.

These structures often encode business meaning that a raw table copy alone cannot preserve.

3. Transformation logic

Document transformations, formulas, ABAP routines, currency and unit conversions, exception handling, and delta processing.

Capture both the technical implementation and the business rule it represents.

4. Orchestration and security

Map process chains, scheduling, dependencies, failure recovery, and analysis authorizations.

The target platform must preserve the intended processing sequence and prevent users from accessing data outside their permitted scope.

5. Reporting and downstream consumers

Identify BEx queries, Analysis for Office workbooks, SAP BusinessObjects reports, extracts, and other applications that depend on BW.

For every important report, document its key figures, filters, prompts, aggregation behavior, and business owner.

Choosing the Right SAP BW to Databricks Migration Approach

Not every organization needs the same migration strategy. Consider these approaches during discovery.

Approach	When it may fit	Benefits	Considerations
Full migration	The organization has a clear target-state plan and is ready to retire the legacy BW workloads in scope.	Supports a more direct transition to the target platform.	Requires extensive dependency management, testing, cutover planning, and coordination.
Phased migration	Workloads can be separated by business domain, such as finance, sales, or supply chain.	Limits the scope of each wave and allows lessons from early migrations to inform later work.	Temporary coexistence and cross-platform dependencies need active management.
Hybrid architecture	Some BW workloads must remain in place while new analytics are developed on Databricks.	Allows modernization to progress without replacing every workload at once.	Requires clear ownership, integration patterns, consistent definitions, and a retirement plan where applicable.
SAP Business Data Cloud integration	Required SAP data products are available through supported sharing capabilities and fit the intended use case.	Can provide access to supported data products without traditional replication.	Availability, semantic coverage, permissions, licensing, freshness, and platform requirements must be verified.

How to choose: Start with business outcomes, the existing BW footprint, the required reporting capabilities, and the target operating model. A pilot involving one representative business domain can help test the chosen approach before scaling.

Common SAP BW Migration Challenges and How to Solve Them

The following challenges should be included in the migration plan rather than discovered during final validation.

BW component	Common challenge	Recommended approach
Data extraction	Obtaining data reliably while respecting licensing and source-system constraints.	Confirm a supported extraction or sharing route and define full-load and incremental-load behavior.
InfoObjects and master data	Losing compound keys, multilingual texts, attributes, or historical values.	Build conformed dimensions with documented keys, text tables, and validity dates.
Hierarchies	Incorrect historical roll-ups or hierarchy relationships.	Model parent-child relationships and preserve version and validity information.
ABAP routines	Hidden derivations, lookups, and filters.	Inventory routines, document their intent, rewrite them as set-based transformations, and add unit tests.
Delta and request management	Duplicate, missing, or incorrectly processed changes.	Define incremental processing, checkpoints, watermarks, and restart-safe logic.
Currency and unit conversion	Differences between BW and target calculations.	Reproduce conversion rules and reference data, then reconcile results across representative periods.
Non-cumulative key figures	Incorrect inventory or other balance calculations.	Design suitable snapshot or balance logic and validate against BW.
BEx queries	Different totals caused by restricted key figures, variables, or exception aggregation.	Rebuild the reporting logic and compare results for equivalent filters and periods.
Analysis authorizations	Users gaining or losing access incorrectly.	Map access rules to target governance controls and test by role before go-live.

A Proven, Phased SAP BW to Databricks Migration Methodology

A discovery-led, evidence-based process makes the migration easier to estimate, test, and govern.

Phase 1: Discovery and assessment

Inventory InfoProviders, InfoObjects, transformations, ABAP routines, process chains, BEx queries, authorizations, data volumes, and dependencies.

Use usage statistics and dependency analysis to identify high-value workloads and objects that may no longer be required.

Deliverable: A dependency-aware inventory and initial migration scope.

Phase 2: Rationalization

Identify unused or redundant objects, simplify staging layers where appropriate, and decide how much historical data must be retained.

Rationalization can reduce migration effort, but relevant business owners should approve decommissioning decisions.

Deliverable: An agreed migration backlog and scope.

Phase 3: Extraction and data-sharing strategy

Select a supported method for each source. Evaluate applicable SAP extraction options, SAP Datasphere, certified partner tools, and SAP Business Data Cloud sharing capabilities where relevant.

Confirm licensing, connectivity, delta handling, data freshness, source-system load, and security requirements before implementing pipelines.

Deliverable: An approved source-access and ingestion design.

Phase 4: Target Lakehouse design

Map acquisition, harmonization, and reporting layers to an appropriate Databricks architecture. Define bronze, silver, and gold responsibilities, conformed dimensions, hierarchy models, Unity Catalog governance, and migration waves.

The medallion architecture is a useful design pattern, but the layers should reflect actual data quality and consumption requirements rather than being created for naming consistency alone.

Deliverable: A target architecture, data model, and governance design.

Phase 5: Data and logic conversion

Load historical data and rebuild transformations, ABAP routines, and incremental processing as appropriate PySpark or Spark SQL logic.

Automate repeatable mappings where possible, but treat complex business rules individually. Add tests for null values, duplicate records, effective dates, currency conversion, and exceptional cases.

Deliverable: Converted pipelines and tested transformation logic.

Phase 6: Semantic layer and reporting

Recreate BEx key figures, variables, filters, currency logic, and aggregation behavior using governed views, semantic models, or BI-layer definitions.

Reconnect the required reporting tools and confirm that business users can reproduce the results they rely on.

Deliverable: Validated reporting models and consumer connections.

Phase 7: Validation, parallel run, and cutover

Compare migrated data and report outputs against SAP BW using equivalent selections, filters, and periods. Run both environments in parallel for a period appropriate to the business cycle, including a full month-end close where relevant.

Obtain business sign-off, confirm recovery and rollback plans, and decommission legacy components only when dependencies and acceptance criteria have been satisfied.

Deliverable: Signed-off migration results and a controlled transition plan.

The Technical Detail That Decides Success

Rewriting ABAP routines as set-based logic

ABAP routines can contain lookups and row-by-row derivations that must be translated carefully into Spark operations.

For example, consider an SAP BW end routine that assigns a customer's sales region and calculates a USD amount.

Illustrative SAP BW ABAP logic:

LOOP AT RESULT_PACKAGE ASSIGNING <result_fields>.
  READ TABLE lt_customer INTO ls_customer
       WITH KEY customer = <result_fields>-customer
       BINARY SEARCH.

  IF sy-subrc = 0.
    <result_fields>-sales_region = ls_customer-region.
  ENDIF.

  <result_fields>-amount_usd =
    <result_fields>-amount * <result_fields>-fx_rate.
ENDLOOP.

Illustrative Databricks PySpark equivalent:

from pyspark.sql import functions as F

customers = (
    spark.table("silver.dim_customer")
    .select("customer", "region")
)

sales = (
    spark.table("silver.sales_items")
    .join(customers, "customer", "left")
    .withColumnRenamed("region", "sales_region")
    .withColumn(
        "amount_usd",
        F.col("amount") * F.col("fx_rate")
    )
)

sales.write.mode("overwrite").saveAsTable(
    "gold.sales_items_enriched"
)

The example replaces a row-by-row lookup with a join and column expression. It is illustrative, not a universal drop-in conversion. Before production use, verify join cardinality, duplicate customer keys, null handling, data types, rounding, exchange-rate rules, and the intended write strategy.

Preserve SAP BW semantics in the gold layer

SAP BW's value lies partly in the business definitions attached to the data. Compound keys, time-dependent attributes, hierarchies, currency conversions, and key figure calculations all affect how results are interpreted.

Model these rules explicitly in the target data and semantic layers. Document key figure definitions and assign business owners to approve important calculations.

Map the layered scalable architecture to the medallion architecture

Many SAP BW landscapes separate data acquisition, harmonization, and reporting. Databricks bronze, silver, and gold layers can provide a useful target pattern:

Bronze: Land source data with appropriate metadata and ingestion history.
Silver: Clean, standardize, validate, and relate the data.
Gold: Publish business-ready models, key figures, and aggregates for reporting and analytics.

Design the mapping around workload needs, data quality, lineage, and reprocessing, not as a mechanical one-to-one conversion.

Reconcile BEx query outputs, not just table counts

Matching row counts does not guarantee equivalent business results. BEx queries may apply currency conversion, restricted or calculated key figures, exception aggregation, and other logic at query time.

Validate the outputs users actually consume, using equivalent selections, time periods, filters, and authorization contexts. Record differences, resolve them, and obtain business sign-off before cutover.

Can You Connect SAP BW Data to Databricks Without a Full Migration?

Yes, in some scenarios. Organizations can retain SAP BW or the underlying SAP transactional systems while making selected data available to Databricks for analytics. The suitable approach depends on the source version, required data, available connectors, licensing, and operational constraints.

Possible approaches include:

Supported SAP extraction: Extract selected data through an approved connector or integration method and load it into the target platform.
SAP Datasphere or related SAP data services: Consider these where they fit the existing architecture and required data products.
SAP Business Data Cloud sharing: Where supported data products and the required platform configuration are available, SAP Business Data Cloud and Databricks can use data-sharing capabilities to access data without the conventional process of creating another physical copy. See the SAP documentation for SAP Databricks and the Databricks documentation on sharing SAP data.

These options are not interchangeable. SAP Business Data Cloud sharing applies to supported data products and configured environments; it should not be assumed to expose every legacy BW object or eliminate all extraction and transformation work.

Before selecting an approach, evaluate data freshness, semantic coverage, access permissions, licensing, lineage, operational monitoring, and whether historical data or custom BW logic still needs to be migrated.

How Much Time and Budget Should You Plan for?

There is no universal timeline or budget for an SAP BW to Databricks migration. The estimate depends on the complexity of existing data models, ABAP routines, reports, data volumes, security requirements, and testing needs.

A Practical Estimation Process

Start by inventorying SAP BW objects and grouping workloads by complexity and dependencies. Run a pilot migration to measure the actual effort required for data extraction, conversion, testing, and validation. Use these findings to estimate migration phases, including parallel operations, training, cutover, and decommissioning the legacy system.

A discovery-based estimate provides a more reliable budget and timeline than relying on data volume or InfoCube counts alone.

SAP BW to Databricks Migration Readiness Checklist

Use this checklist before starting implementation.

Inventory InfoProviders, InfoObjects, ABAP routines, process chains, BEx queries, and dependencies.
Identify unused objects and obtain approval for rationalization.
Confirm supported source extraction or data-sharing options and applicable licensing.
Define the target data architecture, semantic layer, and governance model.
Document key figures, currency and unit conversions, hierarchies, and access rules.
Select a representative pilot and define measurable acceptance criteria.
Create automated data-quality and transformation tests.
Reconcile report outputs against BW using equivalent filters and periods.
Plan parallel running, business sign-off, rollback, and cutover.
Confirm ownership and decommissioning criteria for legacy objects.

Best Practices for a Low-Risk SAP BW to Databricks Migration

Rationalize before migrating. Avoid carrying forward unused InfoProviders, queries, and redundant staging layers without a business reason.
Confirm the extraction route early. Resolve technical access, licensing, and data freshness before building pipelines.
Document ABAP routines. Record business intent and edge cases before converting code.
Design reusable data models. Centralize conformed dimensions, hierarchies, and approved key figure definitions.
Migrate by business domain. Start with a representative, manageable workload and use the results to refine later waves.
Test business outputs. Compare report-level results rather than relying only on row counts.
Run parallel validation. Continue until the required business cycles and acceptance criteria have been satisfied.
Rebuild governance deliberately. Translate BW access requirements into suitable Unity Catalog permissions and row- or column-level controls.
Recovery plan. Define checkpoints, retry behavior, monitoring, and rollback before production cutover.
Track measurable outcomes. Monitor data quality, reconciliation differences, pipeline reliability, user acceptance, and migration effort.

Related Resources

Explore these official resources to validate the platform capabilities and plan the technical approach:

SAP NetWeaver 7.5 maintenance strategy - Review the maintenance timeline and related product guidance.
SAP Help Portal: Introducing SAP Databricks - Understand SAP Databricks and its integration with SAP Business Data Cloud.
Databricks documentation: Share SAP data - Review the process for accessing supported SAP data products.
Databricks documentation: SAP Business Data Cloud connector - Learn about supported data-sharing capabilities and integration considerations.
DataTerrain ETL Insights Blogs - Explore additional data integration, ETL, and modernization topics.

Check current vendor documentation for product availability, prerequisites, permissions, and licensing before finalizing an architecture.

Key Takeaways

SAP BW to Databricks migration is a re-architecture, not just a data export. You must assess data models, transformations, reporting logic, orchestration, and security.
Preserving business semantics is essential. InfoObject attributes, hierarchies, currency conversion, and BEx key figures influence the numbers business users see.
ABAP routines require deliberate conversion. Document the original business intent before rewriting routines in PySpark or Spark SQL.
Choose the data access method early. Evaluate extraction and data-sharing options for technical compatibility, licensing, governance, and data freshness.
Migration can be full, phased, or hybrid. Not every organization needs to replace its entire SAP BW environment in one step.
Validate business outputs, not just raw data. Reconcile key figures and reports against SAP BW before production cutover.
A discovery-led approach reduces uncertainty. The project should follow inventory, rationalization, target design, conversion, testing, and a controlled cutover.

Conclusion

Migrating from SAP BW to Databricks is a strategic data modernization initiative that requires more than moving records between platforms. Success depends on preserving SAP BW's business semantics, rebuilding transformation and reporting logic, choosing a supported data access approach, and validating results before cutover.

A structured migration process, including discovery, rationalization, extraction planning, Lakehouse design, logic conversion, reporting reconstruction, and validation, helps teams manage complexity and make informed decisions. A phased approach can further reduce risk by letting organizations test the target design and refine migration practices before expanding to additional business domains.

For organizations evaluating SAP BW modernization, the next step is to assess the existing landscape, identify key dependencies, and run a representative pilot with measurable acceptance criteria.

Planning an SAP BW to Databricks migration?

DataTerrain supports data and reporting modernization through assessment, logic conversion, and validation-focused delivery.

Contact DataTerrain to discuss your migration requirements

Frequently Asked Questions

1. Why migrate from SAP BW to Databricks?

Migrating from SAP BW to Databricks helps organizations build a scalable Lakehouse platform, integrate data from multiple sources, and support advanced analytics and AI workloads.

2. Can SAP BW data be migrated to Databricks without losing business logic?

Yes. You can recreate business logic in Databricks using Databricks SQL, PySpark, and governed data models. Validation and reconciliation are essential to ensure accurate results.

3. How do you migrate SAP BW queries and ABAP routines to Databricks?

BEx queries and key figures can be rebuilt using SQL views and semantic models, while ABAP routines can be assessed and rewritten using PySpark or SQL.

4. What are the main challenges in SAP BW to Databricks migration?

Common challenges include complex ABAP transformations, undocumented business rules, BEx query dependencies, data reconciliation, and maintaining report accuracy.

5. How long does an SAP BW to Databricks migration take?

The timeline depends on data volume, transformation complexity, the number of queries and reports, and validation requirements. A detailed assessment helps establish a realistic migration plan.

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