From SAP BW to Azure Synapse and Power BI: A Practical Integration Blueprint for 2026

Bringing SAP Business Warehouse (SAP BW) data into Azure Synapse Analytics and Power BI delivers a single source of truth, faster analytics, and the flexibility to scale. Whether you’re still on classic BW or running BW/4HANA, you can design a robust pipeline that preserves SAP logic, supports delta loads, and powers modern self-service BI.

This guide walks you through architecture options, step‑by‑step implementation, best practices, and common pitfalls—so you can move from concept to production with confidence.

Why integrate SAP BW with Azure Synapse and Power BI?

• Unify SAP and non‑SAP data in one lakehouse and semantic layer
• Scale analytics cost‑effectively with cloud storage and compute separation
• Preserve BW deltas and hierarchies while modernizing your BI stack
• Enable near real-time insights where needed
• Improve governance, security, and lineage across the data lifecycle

The reference architecture at a glance

1) Sources: SAP BW (InfoProviders, ADSOs, CompositeProviders, BEx queries)

2) Extraction patterns:

• SAP BW Open Hub for file-based exports
• ODP-based delta extraction (ODP_BW)
• OData for lightweight scenarios
• SAP HANA (when BW runs on HANA and specific scenarios fit)

3) Landing zone: Azure Data Lake Storage Gen2 (Parquet/Delta)

4) Transformation: Synapse Spark or SQL (with medallion-style layers)

5) Serving: Synapse Serverless/Dedicated SQL, or Delta Lake for Power BI

6) Consumption: Power BI (Import, DirectQuery, or Composite)

7) Governance: Catalog, lineage, security, and quality checks

Tip: If you’re orchestrating many sources, a metadata-driven approach reduces maintenance. See this practical guide to metadata‑driven ingestion in Azure Data Factory.

Choosing the right extraction pattern

1) SAP BW Open Hub (file-based)

• What it is: BW’s native outbound mechanism to export data (typically CSV/flat files).
• When to use: Stable nightly batches, high volumes, predictable deltas managed in BW, and minimal change to SAP.
• How it works:
• Configure Open Hub Destinations targeting a file share/FTP/SFTP
• Land files in ADLS Gen2 (via secure transfer)
• Auto-discover and ingest with Synapse/ADF copy activities
• Pros: Reliable, leverages BW’s delta logic, scalable
• Cons: Batch‑oriented; requires file handling, schema drift management

2) ODP (Operational Data Provisioning) for BW (ODP_BW)

• What it is: SAP’s modern framework for delta‑capable extraction, exposing BW objects via ODP contexts.
• When to use: Incremental loads with low latency, reduced operations vs file drops.
• How it works:
• Use Synapse/ADF SAP CDC (ODP) connectors to fetch deltas
• Land incrementally into Bronze (raw) with change flags
• Pros: True delta, less file plumbing, good for near real-time
• Cons: Requires connectivity setup and careful error handling

3) OData (service-based)

• What it is: Service endpoints for SAP objects and queries.
• When to use: Smaller datasets, lookups, prototyping, or specific BEx query exposure.
• Pros: Quick to start, no file management
• Cons: Not ideal for large volumes or complex transformations

4) SAP HANA extraction

• What it is: Querying underlying HANA tables/views (when BW is on HANA).
• When to use: Specific read patterns where HANA views already reflect your logic.
• Pros: Performance and pushdown
• Cons: Can bypass BW semantics if not carefully mapped; deltas need extra design

Landing and modeling in Azure Synapse

Use a medallion-style data layout

Adopt Bronze/Silver/Gold layers to balance agility and governance:

• Bronze: Raw landed data (files or ODP deltas) in Parquet/Delta, minimal changes
• Silver: Cleaned, conformed, type‑safe, with deduplicated deltas
• Gold: Business-ready star schemas optimized for BI

If this is new to your team, this overview of Medallion architecture is helpful for planning. (Optional link if you prefer keeping to 2–3 total links, you can skip this; below we keep the total to three.)

Storage and formats

• Prefer Parquet/Delta for performance and ACID transactions (Delta Lake)
• Partition by time (0CALMONTH) or business keys used in filters
• Keep schema drift under control by enforcing schemas at Silver

Spark vs SQL in Synapse

• Spark (PySpark/Scala): Complex transforms, Delta Lake management, large-scale processing
• SQL (Serverless/Dedicated): Lightweight transformations, serving, and BI-friendly views

Serverless vs Dedicated SQL pools

• Serverless: Pay‑per‑query, ideal for ad‑hoc, exploration, and cost control
• Dedicated: Predictable performance for heavy, concurrent BI; supports materialized views and indexes

Mapping BW semantics to a star schema

BW constructs don’t map 1:1 to modern dimensional models. Consider the following:

• InfoProviders/ADSOs → Fact tables
• InfoObjects (0MATERIAL, 0CUSTOMER, etc.) → Dimensions
• Key figures → Measures with consistent data types and units
• Hierarchies (e.g., 0PROFIT_CTR) → Parent‑child tables, modeled as separate hierarchy tables and joined in Power BI (or flattened for performance)
• Currency/unit conversion → Handle centrally; store exchange rates (TCUR*) and run conversions during Silver/Gold transforms
• Time and fiscal calendars → Align 0FISCVARNT and fiscal periods with a robust date dimension

Power BI: semantic layer and performance patterns

Connection modes

• Import: Best report interactivity; pair with incremental refresh for large datasets
• DirectQuery: Up‑to‑date data; depends on Synapse performance and data model design
• Composite: Mix Import (aggregations) with DirectQuery (detail drill‑through)

Techniques that move the needle

• Incremental Refresh: Only process new partitions
• Aggregations: Summary tables at higher grains for fast queries
• Star Schemas: Avoid snowflakes and large many‑to‑many joins where possible
• DAX Simplification: Precompute heavy calculations in Synapse Gold layer
• Row-Level Security: Implement dynamic RLS in Power BI; ensure filters align with partitioning

Orchestration, observability, and reliability

• Pipelines: Use Synapse Pipelines or ADF for scheduling, retries, and dependencies
• Metadata-driven controls: One configuration table to rule landing paths, formats, partitions, and deltas
• Quality gates: Validate row counts, duplicates, nulls, and key relationships before promoting data
• Lineage: Track sources to BI artifacts for auditability and faster debugging

A comprehensive tutorial on building robust pipeline frameworks is this guide to metadata‑driven ingestion in Azure Data Factory.

Security and governance you shouldn’t skip

• Network: Private endpoints, VNET integration, and Managed Identity
• Secrets: Azure Key Vault for credentials and signing keys
• Data access: Principle of least privilege; ABAC/RBAC for lake and SQL
• PII/Compliance: Classify sensitive attributes and mask at serve layers where needed
• Lineage and policies: Consider a practical blueprint like data governance with DataHub and dbt to complement or coexist with Microsoft Purview

Reconciliation: proving SAP and Azure match

• Control totals: Compare BW Open Hub export counts vs Bronze counts
• Measure checks: Validate totals by primary business dimensions (e.g., material, company code, month)
• Hierarchy integrity: Validate node membership counts and rollups
• Delta accuracy: Confirm “before/after” for ODP slices; simulate replays on a test branch

Cost management essentials

• Serverless SQL: Use Parquet/Delta to minimize scanned bytes and cost
• Partition pruning: Partition by date/fiscal periods used in reports
• Caching and aggregations: Reduce repeated heavy queries
• Right‑size compute: Autoscale Spark pools; pause Dedicated pools when idle (if feasible in your operating model)
• Storage lifecycle: Tier older Bronze files to Cool/Archive, keep Gold hot

Implementation roadmap (example 8–10 weeks)

• Weeks 1–2: Discovery and design (scope InfoProviders, delta logic, target KPIs)
• Weeks 3–4: Connectors and landing (Open Hub/ODP, Bronze)
• Weeks 5–6: Silver/Gold modeling, hierarchies, currencies, and tests
• Weeks 7–8: Power BI semantic model, RLS, incremental refresh, performance tuning
• Weeks 9–10: Governance, lineage, cost controls, handover and hypercare

2026 outlook: Synapse, Fabric, and the lakehouse

Microsoft Fabric is maturing fast and many teams are evaluating a move from Synapse to Fabric’s OneLake and Direct Lake capabilities. Before you decide, compare feature fit, migration path, and performance trade‑offs using this deep dive on Azure Synapse vs. Microsoft Fabric. For many organizations, 2026 will be the year to standardize on a lakehouse with a clear semantic layer strategy—whether that’s Synapse, Fabric, or a hybrid transition.

Common pitfalls (and how to avoid them)

• Ignoring BW deltas: Reconcile delta semantics (deletions, after‑images) before Silver
• Flattening hierarchies too late: Decide early how to model and cache hierarchies for BI
• Currency confusion: Centralize conversion rules; don’t mix converted and non‑converted figures
• Overusing DirectQuery: Start with Import + Aggregations, then add targeted DirectQuery
• Scanning the lake: Partition and file‑size well; avoid small-file explosion

Key takeaways

• Pick the right extraction method for your latency and volume (Open Hub for batch; ODP for deltas; OData for small targets)
• Land to ADLS Gen2 in Parquet/Delta and use a medallion pattern to manage complexity
• Serve through Synapse SQL and design a Power BI model that’s fast by default
• Build in governance, lineage, security, and data quality from day one
• Plan for 2026 with a clear stance on Synapse vs Fabric and a pragmatic migration path

FAQ: SAP BW to Azure Synapse and Power BI

1) Should I connect Power BI directly to SAP BW or go through Synapse?

Direct connections via the SAP BW connector work for smaller datasets or where BW remains the semantic source (BEx queries). However, landing BW data in Synapse allows you to unify SAP and non‑SAP data, optimize costs and performance, and modernize your BI model. Many teams start direct and transition to Synapse as complexity grows.

2) What’s the best way to handle delta loads from BW?

Use BW Open Hub if you rely on BW’s delta logic and can operate in batch windows. If you need fresher data, ODP (ODP_BW) with Azure Data Factory/Synapse connectors provides incremental loads and better control. Whichever you choose, validate deltas rigorously in Silver.

3) How do I preserve BW hierarchies in Power BI?

Export hierarchy relationships (parent‑child) to dedicated tables. In Gold, model them as:

• Parent‑child hierarchies in Power BI; or
• Pre-flattened hierarchy levels for performance

Cache expensive hierarchy rollups as aggregates where reports rely on frequent drill‑downs.

4) How should I manage currency conversions?

Standardize conversion at the Silver/Gold layer. Store exchange rates (e.g., TCUR* logic) and ensure a single, consistent conversion approach. Avoid mixing converted and non‑converted measures in the same model unless clearly labeled.

5) Serverless or Dedicated SQL pools for Power BI?

• Start with Serverless for cost‑efficient exploration and early releases.
• Move hot, high‑concurrency models to Dedicated for predictable performance, materialized views, and indexing.

Composite models with Import aggregations often deliver the best user experience.

6) Can I achieve near real‑time reporting?

Yes, with ODP-based CDC and micro-batches into Bronze/Silver. For high‑churn tables, design idempotent upserts (Delta Lake) and tune Power BI refresh policies. True streaming is rare for BW but micro‑batching every 5–15 minutes is common.

7) How do I secure sensitive SAP data in Azure?

Use private networking, Managed Identity, and Azure Key Vault for secrets. Control access at the lake and SQL layers (RBAC/ABAC). Apply dynamic RLS in Power BI. Classify PII, mask where needed, and document policies and lineage. A blueprint like data governance with DataHub and dbt can accelerate this.

8) What’s the recommended file format and partitioning strategy?

Use Parquet/Delta and partition by the most common filters (e.g., 0CALMONTH, 0FISCYEAR). Keep files in the 128–512 MB range for efficient scans. Avoid too many small files; compact as part of Silver processing.

9) How do I reconcile BW and Synapse data?

Set automated checks:

• Row and hash totals per partition/date
• Aggregates by key dimensions (company, material)
• Delta “before/after” counts for ODP
• Hierarchy node membership validation

Create a sign‑off playbook so teams can trust the cutover.

10) Should we move to Microsoft Fabric in 2026?

It depends on your ecosystem and organizational readiness. Fabric’s OneLake and Direct Lake can simplify BI at scale, but many enterprises will run Synapse and Fabric in parallel during transition. Evaluate using this comparison of Azure Synapse vs. Microsoft Fabric, then plan an incremental migration.

If you’re building this at scale or across many SAP and non‑SAP systems, a metadata‑driven ingestion strategy plus clear governance and lineage will keep your platform adaptable, auditable, and cost‑efficient for the long run.