TL;DR
SAP data integration is the recurring movement of SAP data from ECC, S/4HANA, BW or HANA into an analytics platform, with its business meaning intact. Pick the supported SAP interface first, then the pattern, and choose the tool last. Batch extraction suits daily reporting, replication suits fast-changing operational data, and federation reads data where it already lives. For the destinations, Fabric mirrors SAP data through Datasphere, while Databricks and Snowflake can receive SAP data products by zero-copy sharing from Business Data Cloud. A June 2026 SAP patch blocks third-party ODP calls over RFC, so SAP now points customers to Datasphere, Business Data Cloud and ODP OData. Prove one business domain end to end, reconciled to SAP totals, before you scale the feed.
Key Takeaways Choose the supported SAP extraction route before you compare tools, because the interface decides what any tool can do reliably. SAP no longer permits third-party use of ODP APIs, and a June 2026 patch blocks ODP-RFC calls unless a customer opts out until the end of 2026. Batch, replication and federation solve different needs, so match the pattern to freshness, source load and delete handling. Raw SAP tables lose currencies, fiscal periods and hierarchies on the way out, so plan the semantic layer as part of the pipeline. Fabric, Databricks, Snowflake and BigQuery all have SAP-supported routes, though some are still announced rather than generally available. Pilot one domain, reconcile it to SAP totals and test recovery before you add more sources. Watch on YouTube
Kanerika | Transforming Data Management and Reporting for Phoenix Recycling Group | Data Integration
See how Kanerika pulled scattered operational data into one integrated reporting layer, the same silo problem SAP teams face when finance, supply chain and sales data live in separate systems.
The 18% Problem in SAP Estates
Fewer than one in five organizations in SAPinsider’s community have every system connected with real-time data flow. The figure comes from SAPinsider’s Enterprise Integration for SAP report , based on a survey of its community from May to August 2025. Just 18% reported a fully integrated estate, and 9% still work with almost entirely siloed systems.
The rest sit in between, and manual integration is still common or heavily relied upon in 42% of respondents’ organizations. Only 17% run predominantly SAP solutions, so the finance, supply chain and sales numbers people need sit across SAP and non-SAP systems at once.
Closing that gap got harder in 2026. On June 9, SAP shipped security patches that block unrestricted ODP-RFC calls from non-SAP applications, and third-party pipelines that relied on that route began failing once the patch was applied. So the first question in SAP integration is now which route SAP still supports, and only then which tool runs it.
What Is SAP Data Integration?
SAP data integration is the recurring movement of SAP data, or shared access to it, into a warehouse, lakehouse or BI platform. In most cases, the sources are SAP ECC, SAP S/4HANA, SAP BW or SAP HANA. The goal is analysis-ready data that still carries its business meaning, such as company codes, fiscal periods and currencies.
In practice, that means a feed that runs every day, or every few minutes, for years. By contrast, a one-off spreadsheet export does not count, and neither does a single migration weekend.
Integration vs Migration vs Application Integration
Three different jobs get called SAP integration, and each needs different tools. First, data integration feeds analytics on a schedule or continuously. Second, data migration moves data once, usually into S/4HANA, and then stops, which is the project our guide to SAP data migration covers.
Finally, application integration is the third job. It connects processes, so an order created in a web shop appears in SAP within seconds. SAP positions SAP Integration Suite for that work, as an iPaaS that links applications, data and processes across SAP and third-party systems.
These boundaries matter because buyers often compare tools built for different jobs. Our guides to data migration vs data integration and enterprise application integration draw the same lines.
Why the SAP Source Decides the Method
An SAP system is not a plain database with friendly table names. For example, a sales order lives in tables such as VBAK and VBAP, and its meaning comes from the application logic around them. Reading the database directly therefore skips that logic, which is why SAP-aware interfaces such as extractors, CDS views and OData services matter.
Similarly, the source release changes the options. S/4HANA exposes released CDS views and APIs, while ECC relies more on classic extractors and table access. BW and BW/4HANA, on the other hand, already hold modeled data, so they are often the quickest source for reporting.
Timing matters as well. SAP committed to mainstream maintenance for core Business Suite 7 applications until the end of 2027. Optional extended maintenance runs to 2030 and S/4HANA is covered until 2040, so any feed built on ECC today should plan to switch sources later.
Why SAP Integration Matters: Use Cases and Challenges
SAP integration projects usually start with one report that takes far too long to build. The use cases behind those reports are easy to recognize.
Finance close and margin reporting that combines SAP ledgers with planning and CRM data. Order-to-cash lead time, from quote to delivery, across sales and fulfillment systems. Inventory visibility across plants, warehouses and distributors. Procurement spend analysis that joins SAP purchasing data with supplier risk feeds. Customer views that blend SAP orders with non-SAP service, marketing and web data. The challenges, however, repeat just as reliably. The data model is complex, extraction can slow a busy production system, and SAP authorizations do not travel with the data. Hybrid estates also add networks and gateways, and licensing applies on the SAP side as well as the destination side.
Each of those points, in turn, shapes the method you choose. That is why Kanerika’s data integration services begin with a source audit rather than a connector.
Master Data Integration
Master data integration is a narrower job inside the same program. It keeps customer, material and supplier records consistent across SAP and the non-SAP systems that use them. That way, one supplier does not become three in the warehouse. SAP has dedicated master data products for this, and the analytics feed should read the governed version instead of building its own.
Get this right first, because every downstream join depends on it. Clean keys keep reconciliation simple, while messy keys turn every dashboard into a negotiation. Our guide to data standardization covers the matching rules in more detail.
SAP Data Integration Methods
Six patterns cover almost every SAP feed. They differ in freshness and in the load they put on the source, and most of all in how they handle deleted records.
Kanerika Service
Data Integration Services
Kanerika audits which SAP interface each feed uses, replaces ODP-RFC dependencies with supported routes, and builds governed pipelines into Fabric, Databricks or Snowflake.
Explore Data Integration Method How It Works Freshness Source Impact Deletes Fits When Full batch extract Copies a whole table or view on a schedule Hours to a day High during the run Caught by comparing full copies Small tables, daily reporting Watermark incremental Pulls rows changed since a timestamp Minutes to hours Low to medium Missed unless flagged Append-heavy data with reliable change dates ODP-based delta SAP’s Operational Data Provisioning framework delivers changes from extractors and CDS views Minutes to hours Low Handled by the delta ECC, S/4HANA and BW sources, through a supported channel Log or trigger replication Captures changes as they happen, as SLT does Seconds to minutes Low to medium Captured Operational dashboards on fast-moving tables API or OData access Calls published services for selected objects On request Per call Depends on the service Narrow, well-defined objects Federation or zero-copy sharing Reads data in place, or shares a data product without copying it Live at query time Moves load to query time Not applicable Curated data products in Datasphere or BDC
ODP deserves a sentence of its own because it causes so much confusion. In short, it is a framework inside SAP ABAP systems rather than a product. It supplies deltas to SAP’s own tools and, until recently, to many third-party connectors as well.
Batch and Incremental Extraction
Batch is the simplest pattern and still the right one for many finance reports. It is also predictable and easy to audit, and it is kind to the source when it runs off-peak. Our comparison of ETL vs ELT covers where the transformation should happen.
Incremental loads cut the volume. However, a timestamp watermark has a blind spot. Microsoft’s guidance on SAP change data capture says watermarking is a limited workaround that does not identify deleted records as changes. So if a cancelled order stays in your warehouse, revenue stays overstated until someone runs a full reload.
CDC and Replication
Change data capture sends inserts, updates and deletes as they happen. The hard part is the handoff between the initial full load and the first delta. For instance, rows get duplicated or lost there if checkpoints are not set correctly.
Watch on YouTube
QAD to Microsoft Fabric: Keep QAD ERP Data Current with Open Mirroring
A short Kanerika walkthrough of keeping ERP data current in Microsoft Fabric with open mirroring, the same continuous-merge idea behind Mirroring for SAP.
Replication tools such as SLT capture changes at the source and pass them on with little delay. Fast capture on its own does not give you a fast dashboard, though, and the four-clock rule below explains why.
Federation and Zero-Copy Sharing
By design, federation leaves the data in SAP and queries it in place. SAP describes SAP Datasphere as supporting batch, streaming, replication and federation patterns while keeping business semantics. Zero-copy sharing goes one step further and publishes a governed data product that a partner platform reads with no pipeline in between.
Both approaches cut copies and storage. However, both also tie query performance to the source side, so they suit curated data products better than heavy exploration. Our guide to data fabric explains the wider pattern.
Real-Time vs Batch: Freshness Has Four Clocks
Teams often ask for real-time SAP data and then measure only the extraction step. In reality, what users see depends on four clocks in a row. Those are change capture in SAP, the pipeline load, the transformation layer and the BI refresh.
As a result, the slowest clock always wins. A replication stream that lands changes in seconds still shows yesterday’s numbers if the semantic model refreshes once a night. So agree a freshness target for each report, then size every clock to meet it.
The 2026 ODP Rule Change: What Broke and What Still Works
The ODP change is the biggest shift in SAP data integration in years, and it had a long fuse. SAP Note 3255746 sets out the rules. Because SAP Notes sit behind a login, though, the history below follows vendor summaries from Matillion and Theobald Software .
2022. SAP said third parties could use ODP APIs, but that they were unsupported and meant for internal use.February 2, 2024. SAP stated that customers and third-party applications are no longer permitted to use ODP APIs to reach SAP ABAP sources, and warned it may restrict and audit that use.July 7, 2024. SAP advised customers to use SAP Datasphere for replication into third-party tools.June 9, 2026. SAP security patches began blocking unrestricted ODP-RFC calls from non-SAP applications.End of 2026. The temporary opt-out expires, and SAP says a later note will re-enforce the block with no further opt-out.What Failure Looks Like
The symptom, in most cases, is abrupt. A Qlik support article describes a Qlik Talend Cloud task with an SAP ODP source. After the patch, the task fails during unload with an “Unpermitted Caller” error (RSAR 051). Microsoft also warns on its SAP CDC page that the patch blocks third-party ODP RFC calls, including calls from its own ADF SAP CDC connector.
The temporary opt-out uses a report called RODPS_REPL_SECUREACCESS_OPTOUT, and customers run it at their own risk. Treat it as time to migrate rather than as a fix.
What Still Works
Still, the restriction is narrower than the headlines suggest. In SAPinsider’s reading , it does not ban RFC as a protocol. Table and CDS view extraction, BAPIs, function modules and DeltaQ remain usable. Only the ODP data replication API over RFC is restricted in SAP-to-non-SAP scenarios.
For these flows, SAP recommends Business Data Cloud and ODP OData, according to the same SAPinsider article. Even so, confirm any vendor’s reading with SAP before you bet a production feed on it.
Your First Move
To begin, run the check in SAP Note 3439624, which SAPinsider says provides a self-assessment for ODP-RFC usage. Next, list every pipeline by the interface it uses and flag each ODP-RFC dependency. Then plan each replacement before the opt-out runs out.
SAP Data Integration Tools
These tools fall into two groups, SAP’s own and everyone else’s. Neither group has a single best tool, because the right choice follows from the interface and pattern you have already picked.
SAP-Native Tools
Tool What It Does Typical Use SAP Datasphere Batch, streaming, replication and federation with business semantics, as part of SAP Business Data Cloud The route SAP recommends for SAP data into third-party platforms SAP Data Services Replicate, transform and load, plus data quality functions to cleanse, match and consolidate Batch ETL with heavy cleansing SAP Landscape Transformation Replication Server (SLT) Real-time replication from SAP or third-party systems to SAP HANA, deployed on premises Low-latency table replication SAP HANA smart data integration (SDI) The standard ETL tool in SAP HANA, a hybrid of Data Services and SLT for batch and real-time work Landscapes that already run SAP HANA SAP Integration Suite iPaaS for applications, events and processes across SAP and third-party systems Process integration, not analytics feeds
SAP PRESS’s overview of SAP’s integration tools also lists SAP Cloud Integration for data services and SAP Replication Server.
In addition, Business Data Cloud wraps several of these pieces together. SAP lists SAP Analytics Cloud, SAP Datasphere, SAP Business Warehouse, SAP Databricks and SAP HANA Cloud as its components, with curated data products on top. Our guide to SAP Business Data Cloud covers its architecture in depth.
Cloud-Platform and Third-Party Connectors
The destination platforms also ship their own SAP connectors. For example, Microsoft Fabric has mirroring for SAP and Data Factory connectors, Azure Data Factory has an SAP CDC connector, and Google has the BigQuery Connector for SAP. Specialist replication vendors such as Fivetran, Qlik, Informatica, Theobald and Matillion fill the gaps.
For every one of them, one question now comes before features and price. Which SAP interface does it use, and does SAP support that use after 2026? A connector built on ODP-RFC needs a migration plan, however good the rest of it is.
How to Evaluate a Tool
Score each candidate on the interface first, and on SAP’s support position for that call. Then check whether it captures deletes, handles the handoff from initial load to delta and replays from a checkpoint without duplicates. After that, look at throttling controls, how it treats credentials and SAP authorizations, native writes to your lakehouse catalog, and licensing on both sides.
For a broader roundup beyond SAP, see our guide to data integration tools . For one-time moves, the list of SAP data migration tools is the better starting point.
Getting SAP Data Into Fabric, Databricks, Snowflake and BigQuery
A Reference Architecture
In general, a good SAP feed has the same shape on every platform. Data leaves SAP through a supported route, lands raw, gets standardized, and then becomes business models that BI and AI tools read. Many teams use a medallion architecture for those layers, and our guide to enterprise data integration covers the wider design.
Microsoft Fabric
Mirroring for SAP in Microsoft Fabric works in two steps. First, SAP Datasphere replication flows extract an initial snapshot and the changes into an ADLS Gen2 landing container. Fabric then merges them continuously into mirrored tables in OneLake.
Supported sources include SAP S/4HANA, SAP ECC, SAP BW/4HANA, SAP BW and Datasphere itself, and Microsoft lists the feature as generally available. Fabric charges no ingress for mirroring, but SAP Datasphere Premium Outbound Integration pricing applies on the SAP side. For batch work, the Fabric Data Factory connector list also includes SAP BW, BW Open Hub, SAP HANA and SAP Table connectors.
A third route is on the way. SAP and Microsoft announced SAP Business Data Cloud Connect for Microsoft Fabric at Ignite in November 2025 for two-way, zero-copy sharing of SAP data products, and SAP planned general availability for Q3 2026 . Microsoft’s own page still says it is coming later this year, so confirm its status before you design around it.
Databricks
SAP Databricks sits inside Business Data Cloud, and Databricks lists Delta Sharing and unified governance among its SAP integrations. That gives SAP data products a zero-copy path into a Databricks workspace.
Teams outside BDC land SAP data through a supported extractor, MERGE the changes into Delta tables and govern them in Unity Catalog. Kanerika works in both setups as a Databricks partner . When the platform choice is still open, our comparison of Microsoft Fabric vs Databricks helps frame it.
Snowflake and BigQuery
SAP Snowflake is an SAP Business Data Cloud extension with fully managed Snowflake data and AI capabilities. It shares SAP and Snowflake data without copies, which suits teams already standardized on Snowflake .
Google’s BigQuery Connector for SAP covers CDC replication through Pub/Sub, streaming replication and file-based replication to Cloud Storage. Google also documents routes through SLT with SAP Data Services, through SAP Datasphere from S/4HANA, and a cross-cloud connection for Business Data Cloud.
Above all, keep extraction freshness and reporting freshness apart in every one of these designs. A mirrored table can be minutes old while the Power BI semantic model on top refreshes twice a day.
How to Choose Your SAP Data Route
In the end, four facts about your estate decide the route. They are the SAP source, the destination, the freshness target and any current dependency on ODP-RFC.
Situation Recommended Route Why Check First SAP ECC on premises, landing in Fabric Mirroring for SAP through Datasphere SAP-supported, with continuous merge into OneLake Datasphere entitlement and Premium Outbound cost SAP S/4HANA, any destination Released CDS views through Datasphere or ODP OData Holds up across upgrades and the ODP-RFC block Which CDS views exist for your objects BW-heavy reporting Modeled BW objects through Open Hub or BW connectors, in batch The business logic already lives in BW BW roadmap and maintenance dates Near-real-time operations dashboard Log-based replication such as SLT into a lakehouse Capture in seconds BI refresh cadence, the slowest clock SAP-centric analytics on BDC BDC data products shared zero-copy to Databricks, Snowflake or Fabric No pipeline to run Partner connector availability An existing ODP-RFC connector Move it to Datasphere, ODP OData or the vendor’s supported interface The opt-out ends with 2026 The SAP Note 3439624 self-assessment
Bring these inputs to the decision meeting, and the table above becomes a short conversation.
SAP source systems and their releases. The objects and fields each report needs. SAP entitlements you already license. Data volume and daily change rate. A freshness target for each report. Source-load limits and batch windows. Destination platform and catalog. Recovery and audit requirements. With the route fixed, the tool choice gets short, because only a few tools run the chosen route well. The data pipeline architecture then follows from the route instead of the other way round.
Checklist
Data Integration Checklist for Enterprise Teams
A step-by-step checklist for the source inventory, interface checks, freshness targets and recovery tests that an SAP integration route depends on.
Get the Checklist → Implementing an SAP Data Feed Step by Step
A good rollout proves one domain end to end before it widens. These seven steps tie each decision to an owner and to evidence.
Inventory sources, objects and consumers. List the SAP systems, objects and fields, and the reports that use them. The data architect owns this, and the evidence is a source-to-report map.Confirm supported interfaces and entitlements. Run the ODP self-assessment, check SAP authorizations and confirm Datasphere or connector licenses with the SAP basis team.Define freshness, recovery and reconciliation rules. Agree them per report with the business owner, in writing.Pilot one business domain. Order-to-cash or finance close works well because both have clear SAP totals to test against.Run the initial load and delta handoff. Watch for duplicates, missed deletes and checkpoint gaps where the full load ends.Reconcile to SAP totals and test recovery. Compare ledger and order totals, then replay a failed batch on purpose.Operationalize and expand. Add runbooks, alerts and a schema-change process, then reuse the pattern for the next domain.One boundary is worth stating plainly. The SAP Migration Cockpit loads data into S/4HANA during a migration, and it is not a recurring analytics feed. For step six, our guide to data reconciliation covers the checks in detail.
Keeping SAP Data Trustworthy: Semantics, Security and Failure Modes
Preserve Business Meaning
Raw SAP tables usually arrive without the context that makes them readable. Currencies and units, fiscal periods, organizational hierarchies and text tables all need to come along. Otherwise every report rebuilds them in its own way.
Released CDS views and APIs carry more of that meaning than custom extractor code, and they hold up better across upgrades. The same logic sits behind SAP clean core , and it keeps the integration layer from becoming another custom-code burden. Track where every field came from with data lineage .
Kanerika Service
Data Governance Services
Kanerika rebuilds access rules, lineage and ownership for SAP data once it leaves SAP, so row-level security in the lakehouse matches the roles it replaces.
Explore Data Governance Rebuild Security Downstream
Equally, SAP authorizations do not travel with replicated data. Once a table lands in a lakehouse, row-level access by company code, sales organization or region has to be rebuilt in the destination. Then it needs testing against the SAP roles it replaces, and a data governance program gives that work a clear owner.
Common Failure Modes
Symptom Likely Cause Prevention Duplicate rows after a replay Checkpoint not respected at restart Idempotent MERGE on business keys Cancelled orders still counted Watermark load that misses deletes A delta method that captures deletes, or periodic full reconciliation Totals differ from SAP Currency, unit or fiscal-period logic lost Model semantics in the standardized layer and reconcile daily SAP slows down during loads Extraction in business hours without throttling Off-peak windows and parallelism limits Load breaks after an SAP change Schema change with no change process Schema-change alerts and a review step Dashboards stale despite fast ingestion BI refresh slower than the pipeline Refresh schedules tied to the freshness target “Unpermitted caller” error after patching The tool calls ODP through RFC Move to a supported interface before 2026 ends
What an SAP Data Feed Costs: The Line Items to Check
Nobody can give one honest price for an SAP feed, because the bill has several owners. What you can do is list the line items and get each one confirmed.
Cost Component Sized By Who Confirms SAP-side entitlements, such as Datasphere Premium Outbound Integration Data volume moved out of SAP SAP account team Connector or replication license Sources, rows or connectors Tool vendor Destination compute and storage Load frequency and data volume Platform team Networking and gateways Hybrid connectivity needs Infrastructure team Initial load vs steady state History depth and daily change rate Data engineering lead Monitoring and support Number of pipelines and service levels Operations owner BI capacity Report users and refresh cadence BI platform owner
Price the initial load separately, because it often needs more compute, more source time and more checking than months of steady deltas.
How Kanerika Delivers SAP Integration
Kanerika runs SAP data integration projects in five stages. We assess the sources and the interface each feed uses, including any ODP-RFC exposure, and design a supported route to the destination. After that, we build governed pipelines in medallion layers, reconcile and secure the result, and run it with monitoring and runbooks.
One manufacturing engagement shows the payoff. An edible oil manufacturer and distributor ran SAP for its main transactions across plants nationwide, alongside non-SAP systems. Delayed, inconsistent insight and manual syncing of finance and HR data were costing time and accuracy.
Kanerika consolidated the SAP and non-SAP sources, automated the finance and HR integration, and added data lineage in Power BI for security and executive reporting. The client saw 60% less data integration time, a 30% increase in productivity and a 20% improvement in business performance.
Case Study
60% Faster Data Integration for a Manufacturer with SAP
SAP and non-SAP sources consolidated and finance and HR integration automated for an edible oil manufacturer, cutting data integration time by 60%.
Read the Case Study → That work rests on partnerships that match the destinations in this guide. Kanerika is a Microsoft Solutions Partner for Data and AI with the Analytics specialization, a Microsoft Fabric Featured Partner and a Databricks consulting partner. We also hold ISO 9001:2015, ISO 27001 and ISO 27701:2019 certifications, along with SOC 2 Type II and a CMMI Level 3 appraisal.
Our data integration and data engineering teams watch for the same pitfalls on every project. These include hidden ODP-RFC dependencies, missed deletes, currency logic rebuilt per report and BI refreshes slower than the feed. For the wider program, see our guide to SAP modernization , or talk to our team about one SAP domain.
Choosing an SAP Route That Lasts
SAP data integration works when the route comes first. Confirm which SAP interface is supported for your source, because ODP-RFC no longer is for third-party tools and the opt-out ends with 2026.
Then pick the pattern that matches the freshness and delete handling each report needs. Only after that, pick the tool that runs it on Fabric, Databricks, Snowflake or BigQuery. Prove one domain against SAP totals, and the rest of the estate can follow the same path.
Frequently Asked Questions
What is SAP data integration?
SAP data integration is the recurring movement of SAP data, or shared access to it, from systems such as ECC, S/4HANA, BW and HANA into a warehouse, lakehouse or BI platform. It keeps business meaning such as company codes, currencies and fiscal periods intact, so analysts can report on SAP data next to non-SAP sources.
How is SAP data integration different from SAP data migration?
Integration is ongoing, while migration happens once. An integration feed runs every day or every few minutes for years and supplies analytics platforms. A migration moves data into a new system, usually S/4HANA, and then stops. Tools such as the SAP Migration Cockpit serve migrations, not recurring analytics feeds, so the two need separate plans.
What is SAP smart data integration (SDI)?
SAP HANA smart data integration is the standard ETL tool inside SAP HANA, according to SAP PRESS. It combines traits of SAP Data Services and SLT, so it handles both batch and real-time integration. It is available only on SAP HANA systems, which makes it a fit for estates that already run HANA as their platform.
Can I still use ODP to extract SAP data?
Yes, but not through every channel. SAP no longer permits third-party use of ODP APIs, and since June 9, 2026 security patches block unrestricted ODP-RFC calls from non-SAP tools. A temporary opt-out ends with 2026. SAP recommends ODP OData and Business Data Cloud instead, and SAP Note 3439624 provides a self-assessment of current usage.
Which method gives real-time SAP data?
Log-based or trigger-based replication, such as SAP SLT, captures changes within seconds, and Fabric mirroring for SAP merges changes continuously. Fast capture is only one clock, though. Users see fresh data only when the pipeline load, the transformation layer and the BI refresh are also fast enough, so set a freshness target for each report first.
Can Power BI connect directly to SAP?
Yes. The Power BI SAP HANA connector supports Import and DirectQuery modes, and it uses an on-premises data gateway to reach on-premises systems. Direct connections suit a few focused reports. For enterprise reporting across SAP and non-SAP data, an integration layer in Fabric or a lakehouse gives better control over semantics, security and refresh timing.
How do I integrate SAP with Microsoft Fabric?
The main supported route is Mirroring for SAP, which Microsoft lists as generally available. SAP Datasphere replication flows land a snapshot and changes in ADLS Gen2, and Fabric merges them into OneLake. Fabric Data Factory adds batch SAP connectors. SAP planned BDC Connect for Fabric, a zero-copy option, for general availability in Q3 2026.
What tools move SAP HANA data to BigQuery or Databricks?
For BigQuery, Google provides the BigQuery Connector for SAP with CDC, streaming and file-based modes, plus documented routes through SLT, SAP Data Services and SAP Datasphere. For Databricks, SAP Databricks inside Business Data Cloud shares data through Delta Sharing. Teams outside BDC land data through a supported extractor and merge changes into Delta tables.
