Compare SAP data ingestion tools
Choose an SAP-to-Databricks ingestion tool by comparing deployment, cost, initial loads, change capture and target formats. The tables below bring these decisions together, with links to tool guides and supporting documentation.
Start with the extraction method: application-level replication reads through SAP interfaces, while database-level replication captures changes closer to the underlying tables.
These tables summarize our existing ingestion tools comparison. Support depends on the connector and SAP release; “Not specified” means the comparison does not establish support. For ODP-based approaches, review the SAP ODP interface restrictions when evaluating the extraction path.
1. Environments and setup
Compare the target cloud and the software your team will operate. SaaS manages the service for you; agents, ABAP add-ons and on-premises applications still need installation and maintenance.
| Name | Environment | Run options | Installation needed | Independent of SAP SLT |
|---|---|---|---|---|
| Azure DataFactory | Azure only | SaaS + agent | Documentation needs to be installed on an on-premises computer or on a virtual machine (VM). | Depends on the extraction path (SLT is recommended and needed for SAP tables extraction) |
| SNP Glue | Documentation, Documentation, GCP | on-prem | installed as ABAP add-on on ERP server (no additional hardware required) | Yes (but can work with SLT if already in place) |
| Qlik Replicate | Documentation, Documentation, Documentation | SaaS + agent, on-prem | full app installation (on-prem version) | Yes (see Documentation) |
| SAP Data Intelligence | Azure, AWS, GCP | SaaS, on-prem | full app installation (on-prem version, Kubernetes) | Not specified |
| AWS AppFlow | Documentation only | SaaS | No (fully SaaS) | Depends on the extraction path |
| Cloud Data Fusion | Documentation only | SaaS | No (fully SaaS) | Depends on the extraction path |
| Asapio | Azure, AWS | on-prem | Yes | Yes |
| SAP Datasphere | Azure, AWS, GCP | SaaS | Yes | Yes |
2. Costs
Compare licensing with the cost of running the complete pipeline: extraction, agents or servers, cloud storage, data transfer and Databricks processing. Use the pricing links or request a quote for your source count and data volume.
| Name | Pricing model / quote | Infrastructure to budget for |
|---|---|---|
| Azure DataFactory | Pipeline and runtime pricing | Self-hosted integration runtime and any required SAP replication infrastructure |
| SNP Glue | Pipeline / table tiers; request a quote | ABAP add-on capacity on the SAP system |
| Qlik Replicate | Request a vendor quote | Replication server or agent infrastructure |
| SAP Data Intelligence | Confirm licensing for your deployment | Platform capacity and infrastructure for self-managed deployments |
| AWS AppFlow | Flow runs and data processing | Target storage and downstream processing |
| Cloud Data Fusion | Instance and pipeline execution pricing | Pipeline execution resources and target storage |
| Asapio | Pipeline / table tiers; request a quote | SAP-side installation and target services |
| SAP Datasphere | Request a capacity and replication quote | Service capacity, outbound transfer and target storage |
3. Historical SAP data extraction
The initial load moves existing SAP records into the lakehouse. Check whether you can filter by company code, fiscal year or another business key, and how you will restart an interrupted load.
| Name | Historical data load (primary system) | Filtered historical load |
|---|---|---|
| Azure DataFactory | via the SAP Application Layer | No (Documentation) |
| SNP Glue | utilize standard SAP select options | Not specified |
| Qlik Replicate | Yes | Yes |
| SAP Data Intelligence | Not specified | Not specified |
| AWS AppFlow | ODP via oData only (slower) | Not specified |
| Cloud Data Fusion | Not specified | Not specified |
| Asapio | Yes | Yes |
| SAP Datasphere | Yes | Yes |
4. Continuous SAP data extraction
After the initial load, compare how each tool captures changes. Validate inserts, updates and deletes for the specific SAP tables, extractors, CDS views or BW objects you need.
| Name | Real-time support | Extractors support | BW data extraction support | New records (deltas) processing for CDSViews/HANA Calculation views | Application layer extraction | Database layer extraction |
|---|---|---|---|---|---|---|
| Azure DataFactory | No (batches only, every 5 minutes) | Yes | Yes (via ODP) | Yes | Yes | Yes (via SLT and ODP) |
| SNP Glue | Yes (near-real time) | Yes | Yes | Yes (via ODP) | Yes | Yes (via triggers) |
| Qlik Replicate | Yes (log-based + Documentation) | Yes | Yes (via Documentation) | Yes (for the ODP connector only?) | Yes | Yes (via CDC, or via triggers for HANA) |
| SAP Data Intelligence | Yes | Yes | Yes | Yes | Yes | Yes |
| AWS AppFlow | No (micro-batches only) | Yes | Yes (via ODP) | Not specified | Yes (ODP via OData) | No (ODP only) |
| Cloud Data Fusion | Yes (via SLT) | Yes | Yes (via ODP) | Not specified | Yes | Yes (via SLT) |
| Asapio | Yes | No | No | Yes | Yes | Yes (via triggers) |
| SAP Datasphere | Yes | Yes | Yes | Yes | Yes | Yes |
5. Writing data to Databricks
Check both the landing format and the work required in Databricks. Writing files to cloud storage is only part of the pipeline: changes must also be applied to the target tables.
| Name | Schema conversion | DeltaLake support |
|---|---|---|
| Azure DataFactory | Yes | Yes (Documentation) |
| SNP Glue | Yes | Yes |
| Qlik Replicate | Yes (Documentation)/databricks_delta_lakehouse_data_types_target.htm) | Yes (Documentation)/databricks_delta_lakehouse_target.htm) |
| SAP Data Intelligence | No | No (custom connector needed) |
| AWS AppFlow | Not specified | No (parquet or CSV only) |
| Cloud Data Fusion | Not specified | Not specified |
| Asapio | Not specified | Yes |
| SAP Datasphere | Yes | Yes |
Plan your ingestion pipeline
Land the initial load in cloud storage or Delta tables, then process incoming changes into your Databricks tables. For file-based ingestion, define how to identify records, apply updates and deletes, and recover from a failed batch without duplicating data.
Shortlist tools against a representative SAP source, then validate load duration, delta latency, schema changes and operating costs in a proof of concept. Use the linked tool guides to explore each option against your SAP landscape and Databricks requirements.