Everything about SAP migration and Palantir : A basis POV

1.Understanding Palantir:

Palantir is an enterprise platform that helps organizations use their data to run real operations and make decisions. It is not just about reporting or creating dashboards. It is ment to help users take action, track decisions and measure imapact using trusted data.

The core parts of Palantir are :

1. Data layer:

This is where Palantir stores data as datasets (like very large Excel tables) and every data has lineage. That is , Palantir always remembers where data came from, what transformations were applied, what dataset produced what dataset.

2. Object layer (Ontology):

Source:https://blog.palantir.com/how-anyone-can-integrate-sap-data-in-hours-17f23d0bd2e8 

 In this layer, Palantir converts datasets into:

Objects (Customer, Material, Asset, Employee, Purchase Order)

Links (Customer to Orders, Vendor to Deliveries, Asset to Maintenance)

Actions (approve, update, assign, escalate, create)

This Ontology creates a digital model of the organization, thus enabling  business users to understand the data in business context rather than just a raw tables.

The typical use case of Palantir involves time-bound projects that support decision-making processes, such as inventory optimization. alert monitoring and resolution, sales territory planning, supply chain risk management.

2.Palantir architecture:

Palantir’s standard architecture is built on three integrated platforms:

1. Foundry

2. AIP

3. Apollo

All these together are positioned as an Enterprise Operating System.

Foundry is the core data operations layer for data management, transformation logic, Ontology development, analytics, and workflow building.AIP (AI Platform) adds the generative AI layer, enabling secure access to LLMs and providing tooling for agents, automations, AI apps, and governance through evaluation frameworks. (For more information on AIP, refer to URL https://www.palantir.com/docs/foundry/architecture-center/aip-architecture/ )Apollo runs underneath as the continuous delivery and infrastructure orchestration platform, enabling large-scale, zero-downtime upgrades across the entire service mesh.

Conceptually, Palantir describes AIP and Foundry as a set of nine capability areas spanning the Ontology system, data and workflow services, analytics, automations, and product delivery. These capabilities are supported by shared foundational components such as storage, compute, networking, security, governance, and workspace, all powered by Apollo. The combined stack is positioned as the backbone for operational, AI-enabled applications across industries like healthcare, aviation, utilities, and defense.

3.Palantir and data connectivity:

Foundry positions itself as more than a standard ETL or ELT tool. It is designed as an enterprise-grade data integration backbone that reduces the long-term cost of integration by combining connectivity, transformation, pipeline management, and governance into a single operating layer. The goal is to take data from complex environments and continuously deliver curated, operationally trusted datasets, not just move data into storage. A key part of this is Data Connection which is foundry’s application for synchronizing data from external systems into Foundry for use across the data integration, modeling, and Ontology layers. Data Connection also supports outbound connectivity, enabling writeback to external systems via exports and Webhooks.

 At the connectivity layer, Foundry supports structured, semi-structured, and unstructured sources, and multiple ingestion patterns such as batch, micro-batch, streaming, and CDC. A “source” in Foundry represents a connection to an external system such as Postgres, S3, Linux file systems, SAP, or REST APIs. Connectivity can be established directly when the source is reachable from Foundry, or through a Foundry agent when sources are hosted in a separate private network such as on-prem. Agents are downloadable programs installed inside the customer network and managed from Data Connection. They provide controlled network access to private systems and securely ingest data into Foundry using restricted tokens. Once a source is created, teams configure syncs (most commonly batch syncs) through the Data Connection UI, schedule runs, and write the results into versioned Foundry datasets with full lineage and auditing.

 For more information refer to URL https://www.palantir.com/docs/foundry/hyperauto/v1-getting-started/ 

5.Connecting SAP and Palantir

6.Sample demo screen shots of integrating SAP data with Palantir’s HyperAuto to extract SAP ECC data into a S3 bucket

Source: https://blog.palantir.com/how-anyone-can-integrate-sap-data-in-hours-17f23d0bd2e8

NOTE: I have only provided the screen shots with high level explanation so that the readers could get a look and feel of how SAP data gets extracted into Palantir.  I would strongly recommend the interested readers to read it in depth at above blog for complete understanding

Below are the high-level steps involved:

The upcoming steps and screenshots demonstrates how HyperAuto accelerates SAP integration by allowing us to explore SAP tables visually, select only what we need, and automatically generate ingestion syncs and transformation pipelines. In this example, a data engineer at a manufacturing firm builds a supply chain view by ingesting SAP data, cleaning and integrating it in Foundry, and exporting the curated datasets to an S3 bucket. 

Connect SAP to Foundry using the official SAP-certified connector and ensure the SAP source is available in the Data Connection app.

 Step 1-2:

a. Access the SAP source in Data Connection: In Data Connection, locate your SAP source (typically listed with type magritte-sap-source) and open it.

b. Explore SAP data in HyperAuto: Launch Explore and create syncs. HyperAuto reads SAP tables on-demand (including custom Z tables) and provides a “shopping cart” style selection flow. This approach helps avoid unnecessary heavy queries and reduces risk of disruption to the ERP system.

Step 3:

a. Start with a pre-packaged workflow: From the Workflows tab, select a pre-built workflow such as Supply Chain Disruption. HyperAuto automatically adds the required SAP tables and relationships needed for the workflow and prepares logic for the use case.

b. Add extra tables (customization): Extend the workflow by selecting additional tables. Example: add MARM (Units of Measure for Material) from the Modules tab.

Step 4-5:

a:Discover relationships without knowing SAP schema: HyperAuto helps explore linked tables visually. It also translates technical SAP table/field names into human-readable descriptions (like an automatic data dictionary). Use table relations to discover and add missing dependencies (example: add the Clients table related to Material Descriptions).

b: Configure syncs: Review the selected tables and configure ingestion settings. Choose ingestion strategies such as:

Snapshot (full extract)Append / incremental (delta ingestion)

Step 6-10:

a.Then confirm by selecting Ingest & Integrate Data → Create syncs.

b.Auto-generate pipeline code (PySpark): HyperAuto generates:

ingestion sync configurationtransformation pipelinesPySpark code for cleaning and integration

All code is version-controlled via Git and created through a pull request workflow, enabling review and controlled deployment.

Step 12-15:

a.Run raw ingests: Using the SDDI/HyperAuto flow, run the syncs to ingest raw SAP data and supporting metadata.

b.Review pipeline lineage: Open the pipeline graph in Data Lineage to visualize dependencies between raw datasets and transformed outputs.

Step 16-18:

a.Build the cleaning and integration pipeline: Select all datasets and trigger a build. HyperAuto runs the generated transformations to produce clean, denormalized, analytics-ready datasets.

b.Validate the final datasets:Preview the cleaned datasets and review the transformation logic that produced them.

Step 19-21: 

a.Create an S3 data connection source

In Data Connection, create a new S3 source, select an agent for the export, and configure bucket details. Save it in a project folder with correct permission.

Step 22-29:

a.Configure an export task: Create a Data Connection Task of type export-s3-task, define the output path, and add the finalized datasets as inputs. Configure outputs for tracking export results.

b.Run the export (or schedule it):Build the export task to push curated datasets to S3. This can later be automated on a schedule or triggered by upstream dataset updates.

At the end of the workflow, we will have:

SAP data ingested and integrated using HyperAutoCleaned, denormalized datasets ready for analyticsData exported to S3 (or optionally used directly in Foundry tools)

This creates an extensible data foundation that can be enriched with other enterprise systems and advanced data types (time-series, geospatial, etc.) for broader operational intelligence.

7.Palantir’s SAP migration narrative

Palantir positions SAP migrations (especially ECC to S/4HANA) as slow and expensive largely because teams work in silos. Extraction, transformation, validation, business SMEs, and compliance often operate independently. When validation fails, organizations can lose weeks doing “data archaeology” to identify root causes and coordinate fixes across teams.

Palantir’s claim is that AIP shifts migration from a linear, phase-by-phase process into a context-connected, end-to-end workflow where data understanding, mapping, transformation, and validation are linked. Validation becomes continuous rather than a final gate. SMEs can describe fixes in plain language, and AI propagates those corrections through the pipeline quickly. This is often explained using the “octopus” analogy, meaning multiple specialized AI capabilities coordinated by a single contextual brain that understands legacy structures, target requirements, business rules, and compliance.

It claims to complete an ECC to S/4 HANA conversion within 2 weeks of time.

8.Palantir’s position on testing

In practical terms, Palantir claims that when testing fails, we do not lose weeks coordinating across teams. The platform preserves end-to-end context such as source, mapping logic, transformation, business rule, and target expectation. This helps identify the root cause faster. Fixes can then be applied by SMEs and automatically propagated through the pipeline.

Palantir’s testing model is central to its positioning:

Testing is not a separate phase at the end.Validation is continuous and embedded throughout the migration pipeline.Data quality, mapping correctness, and compliance checks update continuously as transformations run.

9.My point of view on SAP migration and Palantir usage:

Based on my understanding, I believe Palantir might be of use for selective data migration for SAP environment  to certain extent than for typical brownfield.

a. Why this aligns more with SDT than brownfield

Palantir’s strengths are largely aligned to a Selective Data Transition (SDT) or data transformation-led migration model, where complexity is dominated by:

data harmonizationmapping rulestransformation logicrepeated reconciliation and validation cycles

In a true brownfield conversion, the core work is fundamentally different. The focus is on:

system conversion using SUM/DMOtechnical readiness and remediationcustom code adaptationintegration compatibilitydowntime optimizationcontrolled cutover execution

Since brownfield conversions typically do not involve large-scale remapping, consolidation, or business-unit redefinition of data, Palantir’s key value propositions (continuous transformation, SME-driven mapping, and AI-based validation loops) are not the primary effort drivers.

b. Where Palantir could be strongest

Palantir’s value proposition could becomes far more credible in SDT scenarios, where organizations are not just converting ECC technically but actively reshaping their data, organizational structures, and business processes while moving to S/4HANA.

In these programs, migration is not a single system conversion event. It is an extended transformation journey where enterprises often:

run legacy and target environments in parallelmigrate in wavesconsolidate business unitsremap master datacontinuously validate outcomes

In this context, Palantir’s narrative around contextual awareness, continuous validation, and SME-driven rule definition aligns with real SDT challenges.

c. Summary

Palantir is a non-SAP platform (Foundry and AIP) focused on integration, semantic modeling (Ontology), analytics, and AI-driven operational workflows.It prepares S/4-ready datasets outside SAP using governed pipelines for mapping, transformation, and continuous validation.It is not a replacement for SAP-native brownfield conversion tooling like SUM/DMO.It can significantly accelerate SDT or bluefield-style programs by:reducing discovery timeenabling AI-assisted mappingminimizing reconciliation cycles through continuous, context-aware testing

10. Sample use cases Palantir highlights 

Palantir also positions its architecture as delivering value beyond migration by enabling enterprises to build operational applications mid-stream. For example, a customer returns processing app can be built during migration with Palantir with below steps:

It pulls customer data from ECC.It enriches it with intelligence from other integrated systems.It writes validated transactions into S/4HANA.This is coordinated through Palantir’s unified data layer (Ontology).

Palantir also highlights the flexibility of deploying AI in ways that augment human expertise rather than replacing it, with appropriate levels of automation based on task complexity and risk tolerance. For example, with Palantir AIP a procurement lead could type, “for New London products, swap overseas suppliers for the highest-volume domestic alternative before S/4 upload.” The pipeline reexecutes, validation metrics update, and the change is implemented without filing a ticket, scheduling a sprint, or involving external consultants.

This supports the idea of migration as business continuity plus continuous improvement rather than a disruptive cutover, and again fits more naturally with SDT than pure brownfield conversion.

11.Relationship between Palantir and SAP Business Data Cloud (BDC)

Palantir and SAP Business Data Cloud (BDC) are connected through a strategic partnership intended to combine SAP’s governed, zero-copy business data foundation with Palantir Foundry’s Ontology and AIP.

The joint positioning is:

SAP BDC provides a trusted SAP semantic layer and harmonized data products.Palantir extends this into an enterprise-wide ontology that fuses SAP and non-SAP data.Together they enable AI-driven operational intelligence, simulation, and execution.

12. Palantir and archive data access

Palantir can support archive data access by acting as a unified governed layer that pulls data from SAP archives and legacy sources and exposes it in a searchable, analytics-ready way. This is useful for reporting, audit, and compliance after ECC decommissioning.

However:

Palantir does not replace SAP’s archiving mechanism itself.The value lies in consuming archived and legacy data through connectors, blending it with non-SAP sources, and organizing it in the Ontology so users can query it like live business entities.

Hope you enjoyed reading!

Do not forget to click on LIKE if you enjoyed this blog!

​  1.Understanding Palantir:Palantir is an enterprise platform that helps organizations use their data to run real operations and make decisions. It is not just about reporting or creating dashboards. It is ment to help users take action, track decisions and measure imapact using trusted data.The core parts of Palantir are :1. Data layer:This is where Palantir stores data as datasets (like very large Excel tables) and every data has lineage. That is , Palantir always remembers where data came from, what transformations were applied, what dataset produced what dataset.2. Object layer (Ontology):Source:https://blog.palantir.com/how-anyone-can-integrate-sap-data-in-hours-17f23d0bd2e8  In this layer, Palantir converts datasets into:Objects (Customer, Material, Asset, Employee, Purchase Order)Links (Customer to Orders, Vendor to Deliveries, Asset to Maintenance)Actions (approve, update, assign, escalate, create)This Ontology creates a digital model of the organization, thus enabling  business users to understand the data in business context rather than just a raw tables.The typical use case of Palantir involves time-bound projects that support decision-making processes, such as inventory optimization. alert monitoring and resolution, sales territory planning, supply chain risk management.2.Palantir architecture:Palantir’s standard architecture is built on three integrated platforms:1. Foundry2. AIP3. ApolloAll these together are positioned as an Enterprise Operating System.Foundry is the core data operations layer for data management, transformation logic, Ontology development, analytics, and workflow building.AIP (AI Platform) adds the generative AI layer, enabling secure access to LLMs and providing tooling for agents, automations, AI apps, and governance through evaluation frameworks. (For more information on AIP, refer to URL https://www.palantir.com/docs/foundry/architecture-center/aip-architecture/ )Apollo runs underneath as the continuous delivery and infrastructure orchestration platform, enabling large-scale, zero-downtime upgrades across the entire service mesh.Conceptually, Palantir describes AIP and Foundry as a set of nine capability areas spanning the Ontology system, data and workflow services, analytics, automations, and product delivery. These capabilities are supported by shared foundational components such as storage, compute, networking, security, governance, and workspace, all powered by Apollo. The combined stack is positioned as the backbone for operational, AI-enabled applications across industries like healthcare, aviation, utilities, and defense.3.Palantir and data connectivity:Foundry positions itself as more than a standard ETL or ELT tool. It is designed as an enterprise-grade data integration backbone that reduces the long-term cost of integration by combining connectivity, transformation, pipeline management, and governance into a single operating layer. The goal is to take data from complex environments and continuously deliver curated, operationally trusted datasets, not just move data into storage. A key part of this is Data Connection which is foundry’s application for synchronizing data from external systems into Foundry for use across the data integration, modeling, and Ontology layers. Data Connection also supports outbound connectivity, enabling writeback to external systems via exports and Webhooks. At the connectivity layer, Foundry supports structured, semi-structured, and unstructured sources, and multiple ingestion patterns such as batch, micro-batch, streaming, and CDC. A “source” in Foundry represents a connection to an external system such as Postgres, S3, Linux file systems, SAP, or REST APIs. Connectivity can be established directly when the source is reachable from Foundry, or through a Foundry agent when sources are hosted in a separate private network such as on-prem. Agents are downloadable programs installed inside the customer network and managed from Data Connection. They provide controlled network access to private systems and securely ingest data into Foundry using restricted tokens. Once a source is created, teams configure syncs (most commonly batch syncs) through the Data Connection UI, schedule runs, and write the results into versioned Foundry datasets with full lineage and auditing.Beyond ingestion, for transformation, Foundry adds production-grade pipeline management through tools like Pipeline Builder, which provides point-and-click pipeline creation, Git-style change management, scheduling, and health checks so only compliant datasets reach production. Data quality and governance are treated as first-class features through role-based and marking-based controls, end-to-end lineage, and built-in diagnostics when issues occur. Finally, Data Connection supports outbound patterns such as dataset exports, outbound Webhooks for triggering external workflows from Foundry applications, and inbound event listeners (beta) for low-latency event feeds from systems that cannot directly integrate with standard Foundry APIs. This combination makes Foundry a bidirectional integration layer that not only brings data in, but also operationalizes insights by pushing results back into the enterprise landscape.4.Palantir’s HyperAuto and SAP:Palantir also extends integration beyond basic syncing through HyperAuto, which programmatically generates pipelines for ERP and CRM data, rapidly normalizing and harmonizing it into curated datasets that can feed the Ontology and power downstream analytics, workflows, and AI use cases. HyperAuto is Palantir’s source-to-value automation layer designed to accelerate how enterprise data is extracted, standardized, and made usable in Foundry and the Ontology. HyperAuto V1 (also known as SDDI or Bellhop) was the first generation of this capability and is still supported for several source types such as Salesforce, Oracle NetSuite, and SAP, although SAP customers are strongly advised to use HyperAuto V2, which is specifically released and optimized for SAP.Architecturally, HyperAuto automates the journey from raw source systems to curated, Ontology-ready data using three building blocks: secure connectors for high-scale data transfer, guided source exploration for rapid discovery and bulk sync creation, and automatic pipeline generation that transforms raw extracts into curated Foundry datasets and Ontology object types. HyperAuto analyzes ERP metadata to generate robust, extensible pipelines that denormalize and reconstruct business objects through automatically created transformations, so that the teams do not need deep expertise in proprietary ERP schemas. The generated pipelines typically include source-specific preprocessing, standardized cleaning libraries, core enrichment steps such as renaming and de-duplication, and derived elements like join tables and time-series datasets. HyperAuto can also generate the Ontology automatically, enabling users to immediately search, analyze, and build operational applications on top of SAP and non-SAP data.In addition, HyperAuto can publish the integrated and curated data to external cloud platforms such as Google BigQuery, Snowflake, AWS Redshift, and others, or push it into the Foundry Ontology in just a few clicks, where data is organized into easy-to-understand semantic objects so business users can self-serve and make operational decisions. For more information refer to URL https://www.palantir.com/docs/foundry/hyperauto/v1-getting-started/ 5.Connecting SAP and PalantirPalantir uses the Palantir Foundry Connector 2.0 for SAP Applications, which is:An SAP-certified ABAP add-onInstalled in SAP using SAINTExposes SAP data through HTTPS (SICF services)Works via the SAP application layer (not direct DB accessFoundry defines the extraction logic (tables, filters, schedules), and the sync jobs are executed by Foundry’s Data Connection services. SAP security remains SAP security, because Foundry calls SAP using an SAP user with SAP authorizations.Supported SAP extraction objects include:SAP application tablesCDS viewsBW InfoProviders / BEx queriesExtractors (ODP-enabled)SLT (ODQ queues)Function modules/BAPIsInstallation steps in SAP (At high level):Components:SAP Add-on (ABAP) installed using SAINTFoundry Agent installed in customer network and managed via Foundry Data ConnectionSAP Prerequisites:The source must be SAP NW 7.4 SP5 or 7.5 systems. If the source is below 7.4 SP5 a remote agent pattern required.Generally supported deployment patterns for data extraction are:Direct to SAP ECC/S/4/BWVia SAP SLT Replication Server (ODQ queues / CDC)Remote via Gateway (RFC based)At SAP level:For Palantir add-on installation follow below steps:Login to client 000Run SAINTLoad package: FOUNDRY-SAPCONN-INST-SP00SPXX.SARSelect components: PALANTIR and PALCONNExecute install (dialog for preparation, background for remaining phases)Run Post installation wizard through t-code  /n/PALANTIR/POST_INST . It will automatically below tasks.Activate SICF servicesGenerate roles and assign to Foundry SAP userMaintain default parameters (/n/PALANTIR/PARAM)Check ICM settings and generate test URLsHealth checks (connector, authorization, SLT, RFC, jobs)At Palantir Foundry level:In Foundry Data Connection, create SAP source using YAML:type: magritte-sap-source
url: https://<host>:<port>/sap/palantir
usernamePassword: <username>:{{password}} Followed by this, sync between source and Foundry must be configured. Create syncs for required object types (tables/CDS/BEx/SLT/functions). Example:type: magritte-sap-source-adapter
sapType: tcode
obj: ZTEST_ALV(For more information please refer https://www.palantir.com/docs/foundry/sap/install-sap)6.Sample demo screen shots of integrating SAP data with Palantir’s HyperAuto to extract SAP ECC data into a S3 bucketSource: https://blog.palantir.com/how-anyone-can-integrate-sap-data-in-hours-17f23d0bd2e8NOTE: I have only provided the screen shots with high level explanation so that the readers could get a look and feel of how SAP data gets extracted into Palantir.  I would strongly recommend the interested readers to read it in depth at above blog for complete understanding Below are the high-level steps involved:The upcoming steps and screenshots demonstrates how HyperAuto accelerates SAP integration by allowing us to explore SAP tables visually, select only what we need, and automatically generate ingestion syncs and transformation pipelines. In this example, a data engineer at a manufacturing firm builds a supply chain view by ingesting SAP data, cleaning and integrating it in Foundry, and exporting the curated datasets to an S3 bucket. Connect SAP to Foundry using the official SAP-certified connector and ensure the SAP source is available in the Data Connection app. Step 1-2:a. Access the SAP source in Data Connection: In Data Connection, locate your SAP source (typically listed with type magritte-sap-source) and open it.b. Explore SAP data in HyperAuto: Launch Explore and create syncs. HyperAuto reads SAP tables on-demand (including custom Z tables) and provides a “shopping cart” style selection flow. This approach helps avoid unnecessary heavy queries and reduces risk of disruption to the ERP system. Step 3:a. Start with a pre-packaged workflow: From the Workflows tab, select a pre-built workflow such as Supply Chain Disruption. HyperAuto automatically adds the required SAP tables and relationships needed for the workflow and prepares logic for the use case.b. Add extra tables (customization): Extend the workflow by selecting additional tables. Example: add MARM (Units of Measure for Material) from the Modules tab.Step 4-5:a:Discover relationships without knowing SAP schema: HyperAuto helps explore linked tables visually. It also translates technical SAP table/field names into human-readable descriptions (like an automatic data dictionary). Use table relations to discover and add missing dependencies (example: add the Clients table related to Material Descriptions).b: Configure syncs: Review the selected tables and configure ingestion settings. Choose ingestion strategies such as:Snapshot (full extract)Append / incremental (delta ingestion)Step 6-10:a.Then confirm by selecting Ingest & Integrate Data → Create syncs.b.Auto-generate pipeline code (PySpark): HyperAuto generates:ingestion sync configurationtransformation pipelinesPySpark code for cleaning and integrationAll code is version-controlled via Git and created through a pull request workflow, enabling review and controlled deployment.Step 12-15:a.Run raw ingests: Using the SDDI/HyperAuto flow, run the syncs to ingest raw SAP data and supporting metadata.b.Review pipeline lineage: Open the pipeline graph in Data Lineage to visualize dependencies between raw datasets and transformed outputs.Step 16-18:a.Build the cleaning and integration pipeline: Select all datasets and trigger a build. HyperAuto runs the generated transformations to produce clean, denormalized, analytics-ready datasets.b.Validate the final datasets:Preview the cleaned datasets and review the transformation logic that produced them.Step 19-21: a.Create an S3 data connection sourceIn Data Connection, create a new S3 source, select an agent for the export, and configure bucket details. Save it in a project folder with correct permission.Step 22-29:a.Configure an export task: Create a Data Connection Task of type export-s3-task, define the output path, and add the finalized datasets as inputs. Configure outputs for tracking export results.b.Run the export (or schedule it):Build the export task to push curated datasets to S3. This can later be automated on a schedule or triggered by upstream dataset updates.At the end of the workflow, we will have:SAP data ingested and integrated using HyperAutoCleaned, denormalized datasets ready for analyticsData exported to S3 (or optionally used directly in Foundry tools)This creates an extensible data foundation that can be enriched with other enterprise systems and advanced data types (time-series, geospatial, etc.) for broader operational intelligence.7.Palantir’s SAP migration narrativePalantir positions SAP migrations (especially ECC to S/4HANA) as slow and expensive largely because teams work in silos. Extraction, transformation, validation, business SMEs, and compliance often operate independently. When validation fails, organizations can lose weeks doing “data archaeology” to identify root causes and coordinate fixes across teams.Palantir’s claim is that AIP shifts migration from a linear, phase-by-phase process into a context-connected, end-to-end workflow where data understanding, mapping, transformation, and validation are linked. Validation becomes continuous rather than a final gate. SMEs can describe fixes in plain language, and AI propagates those corrections through the pipeline quickly. This is often explained using the “octopus” analogy, meaning multiple specialized AI capabilities coordinated by a single contextual brain that understands legacy structures, target requirements, business rules, and compliance.It claims to complete an ECC to S/4 HANA conversion within 2 weeks of time.8.Palantir’s position on testingIn practical terms, Palantir claims that when testing fails, we do not lose weeks coordinating across teams. The platform preserves end-to-end context such as source, mapping logic, transformation, business rule, and target expectation. This helps identify the root cause faster. Fixes can then be applied by SMEs and automatically propagated through the pipeline.Palantir’s testing model is central to its positioning:Testing is not a separate phase at the end.Validation is continuous and embedded throughout the migration pipeline.Data quality, mapping correctness, and compliance checks update continuously as transformations run.9.My point of view on SAP migration and Palantir usage:Based on my understanding, I believe Palantir might be of use for selective data migration for SAP environment  to certain extent than for typical brownfield.a. Why this aligns more with SDT than brownfieldPalantir’s strengths are largely aligned to a Selective Data Transition (SDT) or data transformation-led migration model, where complexity is dominated by:data harmonizationmapping rulestransformation logicrepeated reconciliation and validation cyclesIn a true brownfield conversion, the core work is fundamentally different. The focus is on:system conversion using SUM/DMOtechnical readiness and remediationcustom code adaptationintegration compatibilitydowntime optimizationcontrolled cutover executionSince brownfield conversions typically do not involve large-scale remapping, consolidation, or business-unit redefinition of data, Palantir’s key value propositions (continuous transformation, SME-driven mapping, and AI-based validation loops) are not the primary effort drivers.b. Where Palantir could be strongestPalantir’s value proposition could becomes far more credible in SDT scenarios, where organizations are not just converting ECC technically but actively reshaping their data, organizational structures, and business processes while moving to S/4HANA.In these programs, migration is not a single system conversion event. It is an extended transformation journey where enterprises often:run legacy and target environments in parallelmigrate in wavesconsolidate business unitsremap master datacontinuously validate outcomesIn this context, Palantir’s narrative around contextual awareness, continuous validation, and SME-driven rule definition aligns with real SDT challenges.c. Summary Palantir is a non-SAP platform (Foundry and AIP) focused on integration, semantic modeling (Ontology), analytics, and AI-driven operational workflows.It prepares S/4-ready datasets outside SAP using governed pipelines for mapping, transformation, and continuous validation.It is not a replacement for SAP-native brownfield conversion tooling like SUM/DMO.It can significantly accelerate SDT or bluefield-style programs by:reducing discovery timeenabling AI-assisted mappingminimizing reconciliation cycles through continuous, context-aware testing10. Sample use cases Palantir highlights Palantir also positions its architecture as delivering value beyond migration by enabling enterprises to build operational applications mid-stream. For example, a customer returns processing app can be built during migration with Palantir with below steps:It pulls customer data from ECC.It enriches it with intelligence from other integrated systems.It writes validated transactions into S/4HANA.This is coordinated through Palantir’s unified data layer (Ontology).Palantir also highlights the flexibility of deploying AI in ways that augment human expertise rather than replacing it, with appropriate levels of automation based on task complexity and risk tolerance. For example, with Palantir AIP a procurement lead could type, “for New London products, swap overseas suppliers for the highest-volume domestic alternative before S/4 upload.” The pipeline reexecutes, validation metrics update, and the change is implemented without filing a ticket, scheduling a sprint, or involving external consultants.This supports the idea of migration as business continuity plus continuous improvement rather than a disruptive cutover, and again fits more naturally with SDT than pure brownfield conversion.11.Relationship between Palantir and SAP Business Data Cloud (BDC)Palantir and SAP Business Data Cloud (BDC) are connected through a strategic partnership intended to combine SAP’s governed, zero-copy business data foundation with Palantir Foundry’s Ontology and AIP.The joint positioning is:SAP BDC provides a trusted SAP semantic layer and harmonized data products.Palantir extends this into an enterprise-wide ontology that fuses SAP and non-SAP data.Together they enable AI-driven operational intelligence, simulation, and execution.12. Palantir and archive data accessPalantir can support archive data access by acting as a unified governed layer that pulls data from SAP archives and legacy sources and exposes it in a searchable, analytics-ready way. This is useful for reporting, audit, and compliance after ECC decommissioning.However:Palantir does not replace SAP’s archiving mechanism itself.The value lies in consuming archived and legacy data through connectors, blending it with non-SAP sources, and organizing it in the Ontology so users can query it like live business entities.Hope you enjoyed reading!Do not forget to click on LIKE if you enjoyed this blog!   Read More Technology Blog Posts by Members articles 

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