SAP data integration

SAP Data Ingestion Accelerator for Microsoft Fabric

Automate SAP data extraction, metadata mapping, and enterprise data loading into OneLake.

Establish a repeatable, governed path from SAP sources to Microsoft Fabric. Discover the objects your business needs, choose a source-appropriate extraction method, preserve SAP data meaning, and validate each load before relying on it for analytics.

Request an SAP ingestion demo

Connector support, incremental behavior, and automation scope depend on the verified SAP source and Fabric configuration.

What Is the SAP Data Ingestion Accelerator?

It is an Office Solution AI Labs solution intended to automate and standardize parts of SAP data discovery, metadata mapping, schema preparation, and ingestion into Microsoft Fabric. The supplied product content describes Fabric pipeline and Dataflow Gen2 generation, supported incremental patterns, and audit-oriented operational logging. Exact coverage should be verified for the selected SAP product, release, source object, connector, and security configuration.

Ingestion capabilities

Standardize repeatable work while keeping source-specific requirements and engineering review explicit.

SAP source and metadata discovery

Inventory supported SAP systems, tables, custom objects, extractors, CDS views, fields, keys, relationships, and dependencies.

Schema mapping

Translate source metadata into proposed Fabric schemas while documenting types, precision, scale, keys, and exceptions for review.

SAP data type handling

Plan conversions for dates, times, decimals, identifiers, currencies, units, and language-dependent values without discarding business context.

Full and incremental load patterns

Evaluate full snapshots and supported delta approaches for each source object, including recovery, replay, delete handling, and idempotency.

Fabric ingestion assets

Prepare reusable Fabric pipeline and Dataflow Gen2 patterns, with parameters, destinations, schedules, and error handling reviewed for the environment.

OneLake and Lakehouse organization

Structure source-aligned landing data and downstream processing in a way that supports traceability, quality checks, and analytics.

Operational audit and reconciliation

Track run identifiers, timestamps, load mode, row counts, schema changes, failures, retries, and validation results without logging credentials.

Assess the right SAP-to-Fabric integration path

One connector should not be assumed to cover every source system or object. Confirm current support, prerequisites, and licensing before implementation.

SAP ECC and S/4HANA

Assess the release, deployment, source objects, supported connector route, custom tables, authorization, and change-capture requirements.

SAP BW and BW/4HANA

Evaluate BW connector, Open Hub, and other supported routes according to the provider, data object, and intended use.

SAP HANA

Confirm the HANA version, object type, connector activity, client prerequisites, network, authentication, and source workload.

CDS, ODP, and OData

Check whether the specific view or provider is exposed, released, accessible, and supports the required filtering or delta behavior.

SAP Datasphere and replication

Review supported replication or mirroring paths, prerequisites, licensing, destination, and operational responsibilities.

Partner and SLT options

Validate exact source coverage, change semantics, deployment requirements, licensing, and the partner or replication operating model.

Preserve SAP data semantics

Successful extraction alone does not ensure analytical correctness. Keep the context needed to interpret source values and validate transformations with representative data.

Currency and quantities

Keep amount values with their currency keys and quantities with their unit keys. Conversions need agreed rules, context, and reconciliation.

Dates and timestamps

Define parsing, time-zone assumptions, daylight-saving behavior, and invalid-value handling, then test boundary cases.

Identifiers and language

Preserve leading zeros in identifiers and retain language keys for text data until a display-language rule is explicitly applied.

Five-stage SAP data ingestion delivery

Move from a representative source assessment to a monitored, validated ingestion foundation.

Stage 1
Discover and prioritize
Inventory SAP systems, objects, owners, volumes, interfaces, data classifications, and high-value data domains.
Stage 2
Design connectivity and extraction
Select a supported extraction method per object, define least-privilege access, and agree on full, delta, recovery, and performance requirements.
Stage 3
Map and prepare Fabric assets
Document source-to-target schemas, configure reusable ingestion assets, parameterize environments, and establish landing conventions.
Stage 4
Transform and validate
Preserve SAP semantics, apply data-quality rules, reconcile keys and business totals, and test incremental, restart, and replay behavior.
Stage 5
Operationalize and expand
Establish monitoring, alerts, support ownership, runbooks, capacity baselines, and a phased backlog for additional SAP data products.

Common questions

SAP data ingestion accelerator FAQs

What is the SAP Data Ingestion Accelerator for Fabric?

It is an Office Solution AI Labs solution intended to standardize and automate parts of SAP data discovery, metadata mapping, schema preparation, and ingestion into Microsoft Fabric. Exact automation coverage depends on the SAP source, object, connector, and environment.

Which SAP systems can be assessed?

The assessment can consider SAP ECC, S/4HANA, BW, BW/4HANA, HANA, Datasphere, CDS views, extractors, and custom objects. Compatibility must be verified for the specific source release, object, interface, and prerequisites.

Can SAP data be ingested directly into Microsoft Fabric?

Fabric supports several SAP integration paths, including SAP Table, BW, HANA, OData, Datasphere, mirroring, and partner solutions. The appropriate route depends on the source object and required data semantics.

Does the SAP Table connector provide change data capture?

Do not assume snapshot extraction provides CDC. Confirm the current connector behavior and use a separately supported incremental or replication approach when ongoing change capture is required.

How are SAP currency, units, and identifiers handled?

Preserve amounts with currency keys, quantities with unit keys, and identifiers as strings where leading zeros are meaningful. Test transformations against representative data and approved business totals.

Can Fabric pipelines and Dataflows Gen2 be generated?

The supplied product content describes generation of Fabric pipelines and Dataflows Gen2. Confirm which assets and configuration are generated in the current product version, and review them before deployment.

How is SAP source-system performance protected?

Agree on supported extraction methods, filters, batch sizes, concurrency, and scheduling with SAP Basis and application owners. Monitor source utilization during representative tests.

What information is needed for an ingestion assessment?

Useful inputs include SAP product and release, deployment model, representative source objects, interface availability, approximate volumes, required freshness, security constraints, and Fabric environment readiness. Do not send credentials or confidential system data through this form.