Organizations that rely on SAP BW (Business Warehouse) often reach a point where traditional reporting feels limiting-especially when teams want faster exploration, broader data blending, and scalable performance for advanced analytics. Azure Synapse Analytics helps by combining data integration, lakehouse processing, and SQL analytics so you can extend BW data into a modern analytics stack without overloading BW with ever-growing reporting demand.

Below is a practical, production-minded approach to integrating SAP BW with Azure Synapse-including architecture patterns, integration options, security considerations, common pitfalls, and examples you can adapt.

Why Connect SAP BW to Azure Synapse?

SAP BW remains a backbone for curated, governed enterprise reporting. But businesses increasingly want:

• Faster analytics at scale (especially for large historical datasets)
• Self-service BI with richer data blending (CRM, web analytics, product telemetry, etc.)
• AI/ML readiness (feature engineering and model training at scale)
• Cost and performance optimization compared to scaling legacy stacks

With Synapse, you can extend BW data into an analytics ecosystem that supports:

• Enterprise data warehousing (dedicated SQL pools)
• Lakehouse-style processing (Spark + Data Lake)
• Frequent refresh pipelines (depending on source capabilities and delta support)
• Downstream BI tools (Power BI and others)

This is the common “SAP BW reporting in Azure” pattern: keep BW as a governed source of truth, then operationalize broader analytics in Synapse-especially when you need to blend BW with non-SAP data.

High-Level Architecture: What “Good” Looks Like

A typical, production-friendly SAP BW → Azure Synapse setup looks like this:

Reference architecture (diagram described in words)

1. SAP BW (on-prem or private network)

Your InfoProviders/DSOs/queries remain the authoritative source.

1. Connectivity layer (Self-hosted Integration Runtime + private networking)

A Self-hosted Integration Runtime (SHIR) runs inside the network boundary that can reach BW. No public inbound exposure is required.

1. Ingestion/orchestration (Azure Data Factory or Synapse Pipelines)

Pipelines schedule extracts, handle retries, log row counts/control totals, and write to the lake.

1. Landing zone (ADLS Gen2 “bronze”)

Raw, immutable extracts land as Parquet (or CSV temporarily), partitioned by load date/time.

1. Curated lake layers (ADLS Gen2 “silver/gold”)

Transformations standardize keys, de-duplicate deltas, apply business rules, and create analytics-ready tables.

1. Serving layer (Synapse SQL serverless or dedicated SQL pool)

• Serverless SQL queries curated Parquet/Delta directly for flexibility
• Dedicated SQL pool hosts performance-critical star schemas/aggregations for high concurrency BI

1. Consumption (Power BI / other tools)

Datasets and semantic models are built on the curated layer-not on raw landing data.

This architecture supports the most common “SAP BW Azure integration” outcomes: reliable ingestion, governance-friendly storage, and BI performance without copying BW logic blindly.

Integration Options: How to Move Data from SAP BW to Synapse

There’s no single “best” method-your choice depends on volume, latency, governance, and BW configuration. In practice, many teams start with one approach and evolve to another as requirements mature.

Azure Data Factory (ADF) / Synapse Pipelines (Most common production pattern)

ADF (or the Synapse Pipelines experience) is frequently used to extract from SAP BW and land data into ADLS Gen2, then load/serve through Synapse.

Why this works well:

• Scheduling and orchestration are built-in
• Monitoring, retries, alerts, and dependency management
• Straightforward integration with ADLS Gen2 and Synapse SQL/Spark
• Supports secure hybrid connectivity via SHIR when BW is on-prem/locked down

> Tip: Treat ADF as your “data movement and operational control plane,” and Synapse as your “processing and serving plane.”

OData-based extraction (useful, but be selective)

If BW exposes OData endpoints (often via SAP Gateway/services), OData can be effective for targeted extraction.

Best for:

• Smaller datasets
• Narrow domain pulls (specific entities or slices)

Trade-offs:

• Large extracts can hit throttling/paging complexity
• Service/schema changes require strong change management
• Not ideal for high-volume historical backfills

File-based or staging exports (transitional, not the end state)

Some teams export from BW into files (CSV, etc.) and then load into ADLS/Synapse.

Pros:

• Fast to pilot
• Works when connectors/APIs are constrained

Cons:

• Harder to automate reliably
• Higher risk of manual errors and missed loads
• Typically becomes fragile as volume and frequency grow

Third-party SAP connectivity tools (when you need enterprise-grade extraction)

Depending on your ecosystem, third-party solutions can help with:

• More robust ODP-style extraction patterns
• Metadata/lineage acceleration
• Specialized delta/CDC-like approaches (where source supports it)

This can be valuable when governance demands richer lineage, or when native options can’t meet operational SLAs.

Step-by-Step: A Practical Implementation Blueprint

Step 1: Define Reporting and Analytics Requirements

Before building anything, clarify:

• Which BW objects? (InfoProviders, DSOs, CompositeProviders, queries)
• Required freshness: daily, hourly, near-real-time?
• Data volume: historical backfill + expected daily deltas
• Consumers: finance, sales ops, supply chain, product analytics

A simple rule: if a dataset isn’t used weekly (or tied to a KPI), don’t pipeline it first.

Step 2: Choose Your Extraction Strategy (Full + Delta)

A reliable pattern is:

• Initial full load into ADLS Gen2 (raw/bronze)
• Incremental loads (delta pattern) on a schedule
• Reconciliation checks (row counts, control totals, key aggregates)

For deltas, prefer SAP-native delta mechanisms when available. If you must use a watermark approach (timestamps/IDs), design explicitly for late-arriving changes and backdated corrections.

Step 3: Land Data in ADLS Gen2 (Raw Zone)

Best practices:

• Store as Parquet whenever possible (compression + column pruning)
• Partition by ingestion date/load cycle for manageability and performance
• Keep an immutable raw copy for traceability and replay

Sample folder structure:

• /raw/sapbw//ingest_date=YYYY-MM-DD/run_id=/part-*.parquet
• /curated/sapbw///…

Step 4: Transform in Synapse (Curated Zone)

Common transformations include:

• Standardizing date/time, currency, and unit-of-measure fields
• Conforming master data (customers, materials, cost centers)
• De-duplicating deltas (latest-record wins or business-key logic)
• Building analytics models (star schema, KPI tables)

If BI usage is heavy, build:

• Fact tables (sales, inventory movements, GL postings)
• Dimension tables (customer, product, org structure, calendar)

Step 5: Serve Data for BI (Synapse SQL)

You can serve data via:

• Serverless SQL for flexible querying of lake data
• Dedicated SQL pools for consistent performance at scale and complex BI workloads

To optimize BI performance:

• Publish curated views/tables for BI consumption (stable column names, documented logic)
• Avoid exposing raw structures to analysts
• Use aggregated tables/materialized views for high-traffic dashboards

Concrete Example: ADF Pipeline + Configuration (Adaptable Template)

Here’s a practical, “what it looks like” example many teams implement for SAP BW to ADLS Gen2 to Synapse.

Pipeline: pl_sapbw_sales_daily

Activities (high level):

1. Lookup: Get last successful watermark

• Reads from a control table (e.g., dbo.IngestionWatermark) storing entity_name, last_loaded_timestamp, last_run_status.

1. Copy Data: SAP BW → ADLS Gen2 (bronze)

• Source: SAP BW connector (via SHIR if needed)
• Sink: ADLS Gen2 Parquet
• Writes to a path like:

/raw/sapbw/sales/ingest_date=@{utcnow('yyyy-MM-dd')}/run_id=@{pipeline().RunId}/

1. Notebook / Spark job: Bronze → Silver

• Deduplicate, standardize types, enforce schema, apply delta merge rules.

1. SQL script: Silver → Gold (serving tables)

• Builds fact/dim tables, aggregates, and BI-friendly views.

1. Stored procedure: Update watermark + log metrics

• Writes: row counts, total amounts, min/max dates, duration, and status.

Example “controls” you should capture per run

• source_extract_start_time, source_extract_end_time
• rows_extracted, rows_written_bronze, rows_in_silver, rows_in_gold
• control_total_amount (e.g., sum of document currency)
• schema_version (or hash of columns/types)
• pipeline_run_id

This turns your integration into an auditable system rather than “a job that usually works.”

Mini Case Study (Illustrative, Real-World Pattern with Numbers)

A global manufacturing team wanted to reduce BW reporting load and speed up cross-system analytics (SAP + CRM + IoT).

Scope (initial MVP):

• 6 subject-area tables (sales orders, deliveries, billing, customer master, material master, calendar)
• Historical backfill: ~3 years
• Total data landed: ~1.2 TB in Parquet (bronze + curated layers)
• Refresh frequency: hourly deltas for sales/billing; daily for master data

Results after moving analytics consumption to Synapse:

• Dashboard freshness improved from next-day to ~60–75 minutes end-to-end (including transforms and quality checks)
• Power BI model size reduced by ~35% after switching to curated Parquet + aggregated gold tables (less duplication, better column pruning)
• BW query load during business hours dropped noticeably (teams shifted ad-hoc exploration to Synapse), improving BW responsiveness for core finance reporting

What made it work:

• A clear bronze/silver/gold separation
• Strict reconciliation checks (row counts + control totals) before publishing gold
• A dedicated “semantic contract” per dataset (definitions, grain, keys, refresh SLA)

(If you want, you can replace these numbers with your own environment’s volumes and SLAs-this pattern is what matters operationally.)

Security, Governance, and Compliance Considerations

When moving SAP BW data into Azure, security and governance must be designed-not bolted on.

Identity and Access

• Use Azure AD-based access control for Synapse and Data Lake
• Enforce least-privilege access (especially between bronze vs gold)
• Separate environments (dev/test/prod) with independent credentials and key vaults

Data Protection

• Encrypt at rest (ADLS/Synapse) and in transit
• Mask or tokenize sensitive fields (PII, payroll, banking details)
• Define retention policies and deletion rules (raw vs curated may have different requirements)

Auditability and Lineage

• Track pipeline runs, source extraction times, and row counts/control totals
• Document dataset definitions (“semantic contracts”)
• Maintain a data catalog (tooling varies-just don’t let the catalog become optional)
• For deeper traceability, consider formal data pipeline auditing and lineage patterns as you scale domains and consumers

Practical Tips to Avoid Common Pitfalls

Don’t replicate everything from BW

Start with high-value subject areas. Replicating BW wholesale often creates cost and complexity without ROI.

Expect schema drift

SAP structures evolve. Put versioning and validation in the pipeline (fail fast when columns/types shift).

Treat BW semantics as first-class work

BW queries may encode business logic (restricted key figures, calculated key figures, hierarchies). If you extract raw data, plan where that logic will live in Azure (Spark transformations, SQL views, or a semantic layer).

Keep raw and curated separate

Raw is for traceability; curated is for performance and usability. Mixing them leads to messy BI and fragile pipelines.

Validate numbers before BI sees them

Finance and supply chain reporting is unforgiving. Automate reconciliation checks and publish only when controls pass—ideally with automated data validation and testing baked into every pipeline run.

Example Use Cases: What You Can Unlock

Enterprise KPI Dashboards (Finance + Operations)

Curated facts/dimensions in Synapse enable dashboards that combine:

• SAP BW financials
• Operational metrics (IoT, logistics)
• Customer metrics (CRM)

Advanced Forecasting and AI/ML

Synapse supports model training using:

• Historical BW facts
• External drivers (market indicators, weather, promotions)

Cross-System Analytics

Blend BW data with:

• Web/app analytics
• Support tickets
• Product telemetry
• Marketing campaign performance

FAQ: SAP BW to Azure Synapse Integration

1) What is the best way to connect SAP BW to Azure Synapse?

For many organizations, the most maintainable approach is Azure Data Factory (or Synapse Pipelines) to extract from SAP BW, land data in ADLS Gen2, then transform and serve it through Synapse SQL/Spark.

2) Do I need a Self-hosted Integration Runtime (SHIR)?

If SAP BW is on-premises or not publicly accessible, then yes-SHIR is commonly required so Azure pipelines can securely reach BW without opening inbound access.

3) Should I load SAP BW data directly into Synapse SQL tables or land it in a data lake first?

Landing in ADLS Gen2 first is usually the better long-term pattern. It supports traceability, replay/backfills, and multiple downstream uses (BI, data science, governance).

4) Can I do near-real-time reporting from SAP BW into Synapse?

Often teams start with batch loads (hourly/daily). Lower-latency is possible, but requires careful planning around delta handling, BW system impact, and operational overhead.

5) How do I handle SAP BW hierarchies and calculated key figures?

You can:

• Recreate logic in Synapse (views, transformation jobs)
• Materialize curated KPI tables (“gold layer”)
• Standardize definitions in a semantic layer to keep metrics consistent

6) What data format is best for storing SAP BW extracts in Azure?

Parquet is typically preferred for analytics performance and cost efficiency (compression + faster reads), and works well with Synapse serverless SQL and Spark.

7) How do I ensure data quality when replicating BW data?

Use automated checks such as:

• Row-count comparisons
• Control totals (sum of amounts, counts by key)
• Duplicate detection
• Schema validation

…and log everything per pipeline run.

8) Is Azure Synapse a replacement for SAP BW?

In many organizations, Synapse is an analytics extension rather than a direct replacement. Some workloads migrate gradually, while BW continues to serve governed enterprise reporting depending on SAP strategy and business needs.

9) What’s the difference between serverless SQL and dedicated SQL pools for reporting?

• Serverless SQL: flexible querying of lake data with minimal setup
• Dedicated SQL pools: consistent performance and concurrency for heavy BI workloads, with more provisioning and management

10) How long does a typical SAP BW to Synapse integration take?

A focused MVP (one subject area, batch refresh, curated model) can be delivered in weeks. Enterprise rollouts with governance, reconciliation, and multiple domains typically take months.