SAP data warehouse modernization
SAP BW contains years of enterprise knowledge: finance definitions, supply-chain measures, customer hierarchies, operational history, ABAP routines, reporting logic, and carefully maintained data flows. Business meaning lives across InfoObjects, InfoProviders, transformations, DTPs, process chains, queries, routines, and dependencies.
A semantics-aware migration preserves the rules that matter, tests representative data flows, and establishes reconciliation before production cutover. Some assets may move, others need redesign or retirement, and some may fit SAP Business Data Cloud or a hybrid path better.
Select a migration path
For legacy BW or BW/4HANA, assess DataSources, extractors, Open Hub, ODP providers, replication tools, and approved connectors. Verify exposed objects, delta and delete behavior, release requirements, authorizations, SAP Notes, and licensing.
Depending on source release and domain, consider released extractors, ABAP CDS views with extraction annotations, ODP, SLT, connectors, or approved replication. This may reduce reliance on BW staging but requires rebuilding transformations and business logic previously supplied by BW.
SAP BDC Connect for Databricks can share eligible SAP Business Data Cloud data products through Delta Sharing in the supported architecture. This is not arbitrary direct extraction from a legacy on-premises BW system. Confirm product coverage, semantics, availability, and commercial terms.
Retain BW reporting while selected datasets or new use cases move to Databricks. Define source of truth, freshness SLAs, access boundaries, reconciliation, ownership, and retirement criteria.
It is the assessment and modernization of SAP Business Warehouse data structures, extraction, transformation, reporting models, and orchestration for Databricks. Scope can include BW on HANA and BW/4HANA; InfoObjects, InfoCubes, DSOs/ADSOs, MultiProviders, CompositeProviders, DataSources, extractors, Open Hub, transformations, DTPs, InfoPackages, process chains, ABAP routines, BEx queries, hierarchies, currencies, units, history, deltas, and downstream reports.
The goal is not to copy the object catalogue into new names. Design target data products that preserve the required grain, master data, key-figure meaning, delta timing, and reporting contracts.
Bring SAP-derived finance, procurement, sales, inventory, and supply-chain data together with CRM, web, IoT, logistics, and external data while preserving business keys, fiscal periods, units, and hierarchies.
Publish Bronze, Silver, and Gold datasets with governed dimensions, facts, business metrics, and quality rules for reuse across teams and applications.
Reimplement suitable BW transformations in PySpark or Spark SQL with testing and source control. Routines dependent on SAP APIs, runtime context, exits, or business functions may need to remain in SAP or use an approved service.
Use governed SAP-derived data for analytics and suitable AI workloads. Migration alone does not ensure data quality, permissions, semantic definitions, or AI readiness.
Compare Databricks compute, storage, network, SAP licensing, connectors, engineering, BI, dual-running, and operations. Treat cost reduction as a hypothesis to measure.
Extraction from legacy BW, ingestion from SAP application sources, SAP BDC sharing, and hybrid coexistence are distinct patterns. Validate release, source object, interface, licensing, extraction permissions, semantics, and target before selecting.
For legacy BW or BW/4HANA, assess DataSources, extractors, Open Hub, ODP providers, replication tools, and approved connectors. Verify exposed objects, delta and delete behavior, release requirements, authorizations, SAP Notes, and licensing.
Depending on source release and domain, consider released extractors, ABAP CDS views with extraction annotations, ODP, SLT, connectors, or approved replication. This may reduce reliance on BW staging but requires rebuilding transformations and business logic previously supplied by BW.
SAP BDC Connect for Databricks can share eligible SAP Business Data Cloud data products through Delta Sharing in the supported architecture. This is not arbitrary direct extraction from a legacy on-premises BW system. Confirm product coverage, semantics, availability, and commercial terms.
Retain BW reporting while selected datasets or new use cases move to Databricks. Define source of truth, freshness SLAs, access boundaries, reconciliation, ownership, and retirement criteria.
ODP can provide extraction and replication with ODQ delta mechanisms for supported providers. Availability depends on source release, provider configuration, annotations, and interface. Confirm exact requirements with SAP before design approval.
The source service describes metadata inspection, object analysis, ABAP routine mapping, extraction, Delta Lake modeling, reconciliation, and Databricks orchestration. The specific automation coverage is not independently verified; confirm supported objects and manual exceptions in a representative proof of concept.
Preserve technical/business keys, texts, language, compounding, validity, units, currencies, and navigation.
Preserve grain, dimensions, key figures, history, and reporting behavior; not a file-format conversion.
Assess activation, inbound/active/change-log behavior, request management, keys, and history.
Rebuild union/join/filter semantics; check cardinality, duplicates, field mappings, and aggregation.
Validate join cardinality and null behavior.
Preserve rule order, routine behavior, filters, full/delta mode, package and error semantics.
Map dependencies, triggers, retries, recovery, schedules, and operational ownership.
Preserve variables, key figures, filters, exception aggregation, and authorization behavior.
Preserve versions, validity, node semantics, and roll-up behavior.
Maintain consumer contract, protocol, and delivery requirements.
Assess start, end, field-level, and expert routines, as well as exits, BAdIs, custom classes, and function modules. Internal tables and LOOP AT logic may sometimes be modeled with Spark DataFrames, but distributed execution, ordering, package semantics, SAP APIs, and procedural state must be reviewed.
ABAP routines are not generally convertible by syntax substitution. Preserve results and edge cases—not surface syntax—and retain SAP-side logic where that is the appropriate design.
Type mapping is a design input, not a universal conversion rule. Validate values and their business meaning.
| SAP type / concept | Potential target | Validate |
|---|---|---|
| CHAR | STRING | Padding, trimming, case, and encoding. |
| NUMC | STRING or numeric type | Leading zeros and key semantics; numeric conversion can change identifiers. |
| CURR | DECIMAL(p,s) plus currency key | Currency-specific decimals, conversion type, rate date, and rounding. |
| DEC / QUAN | DECIMAL(p,s) plus unit where relevant | Precision, scale, unit reference, and rounding. |
| DATS | DATE or validated string | Initial values, invalid dates, and calendar semantics. |
| TIMS | TIME-compatible representation or string | Time of day is not a timestamp; validate timezone and invalid values. |
| RAW / binary | BINARY | Encoding and byte preservation. |
| MANDT | Explicit client key | Client isolation, filtering, and access rules. |
| LANG | Language key dimension or attribute | Language-dependent texts and fallback behavior. |
SAP BW reporting semantics can span InfoObjects, attributes, texts, navigational attributes, compounded characteristics, and hierarchy structures. For example, flattening a time-dependent product hierarchy into a current-state label could change historical reporting.
Design historical loading separately from ongoing deltas. Define retention, request/activation state, full or selective extraction, partitioning, source impact, restartability, and reconciliation. For deltas, verify initialization, ODQ retention and monitoring when used, inserts/updates/deletes, late arrivals, reinitialization, idempotency, and consistency windows.
Confirm provider support and SAP release requirements for ODP or CDS-based extraction. Delta support depends on provider configuration and source landscape.
Delta Lake does not automatically reproduce BW activation, request management, semantic query behavior, or authorizations. Design those capabilities explicitly and preserve source traceability and required history.
Source-aligned SAP records, extraction metadata, load timestamps, source keys, change indicators, historical snapshots where required, and source traceability.
Validated types, deduplicated and conformed entities, master data/text relationships, approved currency/unit normalization, business transformations, and quality checks.
Finance, procurement, sales, inventory, and supply-chain facts; conformed dimensions and hierarchies; governed metrics and consumption-ready products.
Phase 1
Confirm BW/BW/4HANA release, database, hosting, connected systems, InfoObjects, InfoProviders, ADSOs, InfoCubes, CompositeProviders, transformations, DTPs, process chains, ABAP routines, BEx queries, sources, extractors, ODP/Open Hub outputs, usage, owners, data volumes, history, licensing, and permissions. Identify obsolete, duplicated, and inactive assets.
Potential deliverables: BW object inventory, dependency and lineage map, custom-code assessment, source/extraction matrix, reporting inventory, migration backlog, risk register, and prioritized roadmap.
Phase 2
Choose supported extraction, application-source ingestion, SAP BDC sharing, or hybrid coexistence. Define catalogs, schemas, ownership, access controls, Bronze/Silver/Gold products, object mappings, routine patterns, historical/delta strategy, orchestration, BI semantics, tests, acceptance, and cost assumptions.
Potential deliverables: Target architecture, source-to-target mapping, data-product model, ABAP modernization specification, ingestion/delta design, governance model, validation/cutover plan, and cost assumptions.
Phase 3
Configure approved SAP interfaces; load and validate history; establish monitored deltas; implement reviewed PySpark/Spark SQL; rebuild master data, hierarchies, semantic logic, Delta tables, views, Lakeflow orchestration, quality checks, error handling, version control, and tests.
Potential deliverables: Ingestion pipelines, Bronze/Silver/Gold assets, reviewed transformations, ABAP exception register, master-data and hierarchy models, orchestration jobs, and technical test evidence.
Phase 4
Compare equivalent snapshots, counts, keys, balances, currencies, units, aggregates, hierarchies, texts, BEx queries, reports, delta changes, performance, permissions, retries, and recovery. Resolve discrepancies and obtain business sign-off.
Potential deliverables: Reconciliation report, financial/operational control totals, report evidence, benchmarks, defect/exception register, security tests, and business acceptance.
Phase 5
Deploy production jobs, monitoring, alerts, support ownership, runbooks, and recovery. Transition consumers, run parallel processing if needed, freeze and reconcile source snapshots, cut over with rollback controls, retire BW assets only after approval, then optimize.
Potential deliverables: Production data products and pipelines, runbooks, monitoring, cutover/rollback evidence, retirement approval, and optimization backlog.
SAP-centric enterprise warehouse vs. data engineering, analytics, AI, and data platform.
InfoObjects, InfoProviders, ADSOs, CompositeProviders vs. Delta tables, SQL views, and curated data products.
BW transformations, ABAP routines, DTPs vs. PySpark, Spark SQL, and supported pipelines.
Process Chains vs. Lakeflow Jobs and supported pipeline orchestration.
BW metadata, master data, hierarchies, and key figures vs. semantics modeled and governed in target products/layers.
SAP authorizations vs. Unity Catalog and cloud identity controls; mapping is deliberate, not automatic.
Consider for selected SAP and non-SAP data products, engineering, analytics, or AI workloads. Confirm target models can reproduce required BW semantics and controls.
Consider when SAP data products, business semantics, and SAP's modernization path align with strategy and commercial requirements.
Consider sharing eligible SAP BDC data products into an existing Databricks environment; verify required products and sharing prerequisites.
Retain BW reporting while selected domains modernize. Define ownership, freshness, reconciliation, access, and retirement approach.
Potentially reduce dependence on legacy BW staging, but requires supported source interfaces and reconstruction of BW transformations and business logic.
Compare these choices rather than assuming that a complete BW-to-Databricks replacement is always the best outcome.
Define ownership, catalogs, schemas, privileges, discovery, and supported lineage/audit. Separate development, test, and production access.
Map roles and analysis authorizations (for example company code, sales organization, plant, or personnel area) into groups, privileges, views, row filters, or approved controls. Test allowed and denied access.
Identify personal, financial, HR, supplier, customer, and regulated data; apply classification, masking, retention, encryption, and access requirements.
Use approved SAP technical users, service identities, secrets management, network routes, and secure connectivity. Do not put passwords or tokens in notebooks or source control.
Track BW objects, extraction methods, transformation versions, target tables, consumers, approvals, and execution history. Unity Catalog does not automatically reconstruct every upstream SAP dependency.
Benchmark representative workloads instead of promising faster execution by default. SAP extraction can be the bottleneck even when target compute scales; tune source selection, extraction windows, network, and target ingestion separately.
Build scenarios using actual use and consumption. Do not claim that Databricks automatically eliminates SAP licenses; confirm any license changes with SAP and the customer's licensing advisor.
Financial reconciliation must compare equivalent snapshots, currency, unit, fiscal period, and aggregation semantics. A matching row count alone does not prove a report is correct. Record tolerances, exceptions, and business approvals.
Object inventory, schemas, types, key definitions, InfoProvider and dependency coverage.
Row counts, distinct business keys, duplicates, null distributions, historical snapshots, and delta inserts/updates/deletes.
Key figure sums and averages, period/company-code balances, currency- and unit-aware totals, and exception aggregation.
Master data and text coverage, hierarchy roll-ups, time-dependent attributes, BEx variables, filters, and query outputs.
Data freshness, extraction throughput, recovery/restart, pipeline success, access controls, and downstream report contracts.
Modernize general ledger, payables/receivables, profitability, cost-center, controlling, and consolidation data; validate currency, fiscal calendar, posting period, account hierarchy, and balances.
Combine purchase orders, suppliers, invoices, contracts, and sourcing with external spend while preserving vendor keys and organizational dimensions.
Curate orders, deliveries, billing, shipment, customer, and product datasets with document flow, status history, partner functions, and organizational assignments.
Combine production orders, material movements, plant, inventory, quality, and IoT data for yield, throughput, and maintenance analysis.
Integrate procurement, inventory, delivery, logistics, supplier, and external signals for lead-time, demand, and risk analytics.
Handle employee and organization data with strict access, privacy, retention, and effective-date rules.
Unify product, store, pricing, promotion, sales, and inventory data for demand planning and customer analytics.
Define baselines and acceptance criteria before conversion. Object conversion count alone is not success: a migrated object that changes the meaning of a finance KPI is not a successful outcome.
Active/inactive BW objects, InfoProviders, ADSOs, InfoObjects, CompositeProviders, transformations, DTPs, process chains, ABAP code, BEx/report dependencies, sources, volume, frequency, history, criticality, and ownership.
Objects migrated/redesigned/retained/retired, mapping coverage by object type, routine conversion vs. redesign, historical completeness, delta lag/recovery, job runtime, exceptions, and access tests.
Financial/operational reconciliation, report acceptance, freshness/SLA adherence, reusable data-product adoption, duplicate transformation effort, onboarding time, defined-period TCO, user adoption, and support effort.
Confirm supported SAP BW/BW/4HANA releases, object types, metadata sources, extraction methods, ABAP routine categories, BEx behavior, master data and hierarchy coverage, delta/delete handling, BDC Connect scope, security mapping, reconciliation, and human review of generated assets.
No migration-specific video URL was verified for this service. Do not use a demo for another product as SAP BW migration evidence.
Select a finance or sales flow containing an InfoProvider, transformation with ABAP logic, master data/hierarchy, historical and delta extraction, process-chain dependency, and BEx reporting output. Demonstrate target modeling, reconciliation, Unity Catalog controls, recovery, unsupported constructs, and manual work. Use sanitized data.
Begin with a focused assessment of a representative SAP BW domain and confirm the extraction, target, semantics, and acceptance criteria before planning production waves.
Request an SAP BW assessment or discuss a scoped migration demonstration. Do not send credentials or confidential production data through this form.
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