Ecoservity · Independent Benchmark · January 2024

ADF + USB4SAP: 90% cost reduction on SAP data integration

A $65B global manufacturer. 5 TCODEs and 100 SAP tables to Azure Data Lake. Here is what happened when we replaced the standard ADF pipeline with the USB4SAP plug-in.

90%
Cost reduction
59%
Time reduction
$1.1M
Saved on 5 TCODEs
11 wks
Faster to delivery
SAP NetWeaver Certified Microsoft Certified USPTO Patented In production since 2021

The scenario

This benchmark came from a customer request. A global manufacturer needed to extract SAP operational data to Azure for analytics and AI. We ran a structured cost and time comparison across every project phase.

Customer

  • Global manufacturer, plants in 17 countries
  • Part of a US$65B conglomerate
  • SAP ERP + SAP BW in production

What they needed to extract

  • SAP TCODE MB51 (material movements) with report variant
  • Z-reports (custom customer reports)
  • SAP BW reports
  • Destination: Azure Data Lake (ADLS) and Databricks

Benchmark scope

  • 5 SAP TCODEs and 100 underlying tables
  • MB51 alone accesses 212 DB tables (SQL trace verified)
  • ADF + BTP extract underlying tables only, not the modeled report
  • 10% of tables used as comparison baseline

Why this comparison matters

  • ADF (and BTP) support full table extraction (no native TCODE/report access)
  • USB4SAP streams TCODEs, reports, queries, and tables via one REST API
  • SAP MB51 = 1 business object. ADF sees 212 raw tables. USB4SAP sees MB51.
The root cause of the cost gap: ADF must reverse-engineer SAP business logic table by table. For MB51, that means mapping 212 database tables to reconstruct what SAP already knows as one TCODE. USB4SAP streams MB51 directly: no reconstruction, no custom code, no per-object Build cost.

Phase-by-phase breakdown

Every project phase compared on cost and time. The Build phase is where the architecture difference becomes decisive: 100% of ETL's $974K Build cost is the reverse-engineering problem that USB4SAP eliminates entirely.

Cost comparison (US$): 5 TCODEs / 100 tables
Phase ADF alone ADF + USB4SAP $ saved % saved
1 · Plan $1,920 $640 $1,280 67%
2 · Setup $1,664 $576 $1,088 65%
3 · Build $974,368 $704 $973,664 100%
4 · Go-live $6,566 $2,560 $4,006 61%
5 · Operations $165,111 $107,613 $57,498 35%
Total $1,149,630 $112,093 $1,037,537 90%
Time comparison (weeks): verified on ADF and AWS
Phase ADF alone ADF + USB4SAP Weeks saved % saved
1 · Plan 4 wks 2 wks 2 wks 50%
2 · Setup 6 wks 2 wks 4 wks 80%
3 · Build 7 wks 2 wks 5 wks 73%
4 · Go-live 4 wks 4 wks 0 0%
Total 21 wks 10 wks 11 wks 59%
Time result is identical on Azure ADF and AWS S3 stacks. Go-live phase is unchanged: USB4SAP does not change the hypercare process.
Why Phase 3 is the whole story: $974,368 of ADF's $1,149,630 total is Phase 3 (Build), where engineers manually write code to reverse-engineer SAP business objects into SQL table extractions. USB4SAP's no-code API eliminates this phase entirely: $704 versus $974,368. The cost difference at 5 TCODEs is $973,664. At 400 objects, ADF alone exceeds $20M. USB4SAP stays near-flat because the architecture does not repeat per object.

Why cost scaling is the real value driver

At 5 SAP objects, the saving is significant. At 50 objects, ADF cost begins accelerating exponentially. At 400 objects (a modest enterprise SAP landscape), ADF total cost exceeds $20M. USB4SAP's architecture does not scale per object: the no-code API call is the same whether you access 5 or 5,000 SAP objects.

Total project cost by number of SAP objects extracted

ADF total, ADF ETL component, USB4SAP total, USB4SAP ETL component: at 5, 50, and 400 SAP objects

ADF total cost
ADF ETL component
USB4SAP total cost
USB4SAP ETL component
SAP objects ADF total ADF ETL only USB4SAP total USB4SAP ETL only Gap (total)
5 objects $1.15M $974K $112K ~$0 $1.04M saved
50 objects ~$3.8M ~$1.5M ~$600K ~$0 ~$3.2M saved
400 objects ~$19.8M ~$10M ~$2.7M ~$150K ~$17M saved

What the USB4SAP plug-in adds to ADF

ADF handles data movement. USB4SAP handles SAP. Together they cover what neither can do alone: live, no-code access to SAP's full business object layer, not just underlying database tables.

Capability 1

TCODEs and reports as first-class objects

ADF extracts the 212 raw tables behind MB51. USB4SAP delivers MB51 directly, with the business context, variant, and output already intact. One API call. No reconstruction.

Capability 2

Field-level Change Data Capture (CDC)

ADF requires full table dumps for SAP tables. USB4SAP delivers field-level delta changes out of the box on 850K+ SAP tables. No change pointers activation, no custom ABAP, no batch window.

Capability 3

SAP native security, zero additional infra

USB4SAP inherits 100% of your existing SAP authorization model. No credentials stored outside SAP, no shadow copies, no new security perimeter. Works within ADF's existing orchestration.

Capability 4

BW objects without ETL rebuild

SAP BW reports, BEx queries, ODS, and Data Marts stream via the same REST API. No BW-to-flat-file export, no BW extractor custom build. Destination: any target ADF already supports.

Capability 5

Near-zero Build cost at any scale

ADF's Build cost scales linearly per object: each TCODE or report requires custom connector development. USB4SAP is a no-code API: accessing 400 objects costs the same per-object as accessing 5.

Capability 6

Business-ready semantic models

450K+ SAP-named semantic objects are accessible directly: Purchase Order, not EKKO/EKPO. AI and analytics tools receive data in business language, not raw table fragments requiring reassembly.

Solution architecture: USB4SAP acts as the SAP-side plug-in within an existing ADF pipeline. No replacement of ADF, no parallel infrastructure.
SAP ERP / BW → USB4SAP plug-in → Azure Data Factory → ADLS / Databricks / Synapse → Power BI / Excel / AI agents

Run this benchmark on your SAP landscape

The numbers above are from a project at a specific scope. Cost scaling is non-linear: the larger your SAP object footprint, the more decisive the gap becomes. Talk to us about your specific TCODEs, tables, and destination.