Sap Datasphere

SAP Datasphere Skill

Related Skills

• sap-dependency-security: Use for secure dependency policy, lockfile hygiene, and exact MCP server pins when managing large connector or integration projects with package-managed tooling

Table of Contents

• Overview
• Quick Reference
• Core Components
• Object Types
• Data Builder
• Graphical Views
• SQL Views
• Tables
• Flows
• Task Chains
• Business Builder
• Analytic Models
• Connections
• Space Management
• Data Access Control
• Monitoring
• CLI Reference
• Data Products & Marketplace
• Catalog & Governance
• Content Transport
• Common Issues
• Bundled Resources
• Documentation Links

Overview

SAP Datasphere is SAP's cloud-native data warehouse solution on SAP Business Technology Platform (BTP). It serves as the data foundation within SAP Business Data Cloud (BDC), SAP's unified data and analytics platform that also includes SAP Analytics Cloud, SAP HANA Cloud, SAP Databricks, and curated data products. See references/business-data-cloud.md for the BDC architecture and how Datasphere fits within it.

This skill provides comprehensive guidance for data acquisition, preparation, modeling, administration, and integration.

When to Use This Skill

Use this skill when:

• Creating data warehouses on SAP BTP
• Building analytic models for SAP Analytics Cloud
• Setting up data flows, replication flows, or transformation flows
• Configuring connections to SAP or third-party systems
• Managing spaces, users, and access controls
• Implementing real-time data replication
• Monitoring data integration tasks

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Quick Reference

Core Components

Object Types

Views:

• Graphical View: Visual data modeling with drag-and-drop
• SQL View: SQL-based view definitions
• Analytic Model: Analytics-optimized semantic layer

Tables:

• Local Table: Data stored in Datasphere
• Remote Table: Virtual access to external data
• Local Table (File): Object store-based storage

Flows:

• Data Flow: ETL transformations
• Replication Flow: Data replication from sources
• Transformation Flow: Delta-aware transformations

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Data Builder

Graphical Views

Create views visually by dragging sources and adding transformations.

Supported Operations:

• Join: Inner, Left Outer, Right Outer, Full Outer, Cross
• Union: Combine multiple sources
• Projection: Select/rename columns
• Filter: Row-level filtering
• Aggregation: Group by with aggregates
• Calculated Columns: Derived values

Best Practices:

• Use input parameters for dynamic filtering
• Apply data access controls for row-level security
• Enable persistence for frequently accessed views
• Use lineage analysis to understand dependencies

For detailed graphical view operations, see references/graphical-sql-views.md.

SQL Views

Create views using SQL or SQLScript.

-- Basic SQL View
SELECT
    customer_id,
    customer_name,
    SUM(order_amount) AS total_orders
FROM orders
GROUP BY customer_id, customer_name

SQLScript Support:

• Table variables
• Scalar variables
• Control flow (IF, WHILE, FOR)
• Exception handling

For SQL/SQLScript reference, see references/graphical-sql-views.md.

Data Flows

ETL pipelines for data transformation and loading.

Operators:

• Source: Remote/local tables, views
• Target: Local tables
• Join, Union, Projection, Filter, Aggregation
• Script: Python custom logic
• Calculated Columns

Execution:

• Manual run or scheduled via task chains
• Delta capture for incremental loads
• Input parameters for runtime configuration

For data flow details, see references/data-acquisition-preparation.md.

Replication Flows

Replicate data from source systems to Datasphere or external targets.

Supported Sources:

• SAP S/4HANA (Cloud/On-Premise)
• SAP BW/4HANA
• SAP ECC
• ABAP-based systems
• Cloud storage (S3, Azure Blob, GCS)
• Kafka/Confluent
• SFTP

Supported Targets:

• SAP Datasphere (local tables)
• Apache Kafka
• Google BigQuery
• Cloud storage providers
• SAP Signavio

Load Types:

• Initial Load: Full data extraction
• Delta Load: Changed data only
• Real-Time: Continuous replication

For replication flow configuration, see references/data-acquisition-preparation.md.

Transformation Flows

Delta-aware transformations with automatic change propagation.

Key Features:

• Automatic delta detection
• Target table management
• Graphical or SQL view as source
• Run modes: Start, Delete, Truncate

For transformation flow details, see references/data-acquisition-preparation.md.

Task Chains

Orchestrate multiple tasks in sequence or parallel.

Supported Tasks:

• Data flows
• Replication flows
• Transformation flows
• Remote table replication
• View persistence
• Open SQL procedures
• API tasks
• BW Bridge process chains

Features:

• Parallel execution branches
• Input parameters
• Email notifications
• Nested task chains
• Scheduling (simple or cron)

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Data Modeling

Analytic Models

Create analytics-ready semantic models for SAP Analytics Cloud.

Components:

• Fact: Contains measures (quantitative data)
• Dimension: Categorizes data (master data)
• Measure: Quantifiable metrics
• Hierarchy: Navigation structures
• Variable: Runtime parameters

Creating an Analytic Model: 1. Add a fact source (view or table) 2. Add dimension associations 3. Define measures with aggregation 4. Configure variables for filtering 5. Set data access controls

For detailed modeling guidance, see references/data-modeling.md.

Dimensions

Categorize and filter analytical data.

Types:

• Standard: Basic categorical data
• Time: Calendar-based filtering
• Fiscal Time: Custom fiscal calendars
• Text Entity: Multilingual labels

Features:

• Hierarchies (level-based, parent-child)
• Time dependency (SCD Type 2)
• Compound keys
• Associated text entities

Measures

Quantifiable values for analysis.

Types:

• Simple: Direct aggregation
• Calculated: Derived from other measures
• Restricted: Filtered aggregation
• Currency Conversion: Dynamic conversion
• Unit Conversion: Dynamic conversion
• Count Distinct: Unique value count
• Non-Cumulative: Point-in-time values

Aggregation Types:

• SUM, MIN, MAX, COUNT, AVG
• Exception aggregation for non-additive scenarios

For measure configuration, see references/data-modeling.md.

Business Builder

Create business-oriented semantic models.

Objects:

• Business Entity: Reusable dimension/fact definitions
• Fact Model: Combines business entities
• Consumption Model: Analytics-ready model
• Authorization Scenario: Row-level security

For Business Builder details, see references/data-modeling.md.

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Connectivity

Connection Types

SAP Datasphere supports 40+ connection types.

SAP Systems:

• SAP S/4HANA Cloud/On-Premise
• SAP BW/4HANA (Model Transfer)
• SAP BW Bridge
• SAP ECC
• SAP HANA (Cloud/On-Premise)
• SAP SuccessFactors
• SAP Fieldglass
• SAP Marketing Cloud
• SAP Signavio

Cloud Platforms:

• Amazon S3, Athena, Redshift
• Google Cloud Storage, BigQuery
• Microsoft Azure Blob, Data Lake, SQL Database
• Microsoft OneLake

Databases:

• Oracle
• Microsoft SQL Server
• Generic JDBC

Streaming:

• Apache Kafka
• Confluent

Other:

• Generic OData, HTTP, SFTP
• Adverity, Precog
• SAP Open Connectors

For connection configuration, see references/connectivity.md.

Connection Features

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Administration

Spaces

Logical containers for data and objects.

Configuration:

• Storage allocation (disk + in-memory)
• User access and roles
• Priority and statement limits
• Workload management

Operations:

• Create, copy, delete spaces
• Export/import space data
• Command-line management (datasphere CLI)

For space management, see references/administration.md.

Users and Roles

Standard Roles:

• DW Administrator
• DW Space Administrator
• DW Integrator
• DW Modeler
• DW Viewer

Scoped Roles:

• Space-specific permissions
• Custom privilege combinations

Authentication:

• SAP Cloud Identity Services
• Custom SAML IdP
• OAuth 2.0 clients

For user management, see references/administration.md.

Monitoring

Capabilities:

• Capacity monitoring (storage, memory, compute)
• Audit logs (database operations)
• Activity logs (object changes)
• Task logs (flow executions)

Database Analysis:

• Create analysis users for debugging
• Monitor HANA views
• Stop running statements

For monitoring details, see references/administration.md.

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Data Integration Monitor

Remote Tables

Operations:

• Replicate data (full/delta/real-time)
• Partition data loads
• Create statistics
• Monitor queries

Real-Time Replication

Features:

• Continuous change capture
• Pause/resume capability
• Automatic recovery
• Watermark tracking

View Persistence

Options:

• Scheduled refresh
• On-demand refresh
• Partition management
• Memory optimization

For monitoring details, see references/data-integration-monitor.md.

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CLI Reference

Datasphere CLI Overview

The datasphere CLI enables command-line administration and automation.

Installation:

npm install -g @sap/datasphere-cli

Authentication:

# Interactive login
datasphere config auth login

# Service key (CI/CD)
datasphere config auth login --service-key-path ./key.json

Core Commands:

CI/CD Integration:

# Export and import workflow
datasphere objects export --space DEV --output-file package.zip
datasphere objects import --space PROD --input-file package.zip --overwrite

For complete CLI reference, see references/cli-commands.md.

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Data Products & Marketplace

Creating Data Products

Package curated data for internal or external consumption:

1. Plan: Define purpose, target consumers, contents 2. Prepare: Create views/models, set semantic usage, document 3. Configure: Set visibility, access controls, terms 4. Publish: Make available in marketplace

Product Components:

• Core assets (views, models, entities)
• Documentation and sample queries
• Governance metadata (owner, quality score, SLA)

Data Marketplace

Discover and consume published data products:

• Search: Find by category, provider, quality
• Request Access: Submit justification, await approval
• Consume: Use in views or SAC stories

For complete marketplace guidance, see references/data-products-marketplace.md.

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Catalog & Governance

Data Catalog Features

Centralized discovery and governance:

• Asset Discovery: Search all data objects with metadata
• Glossary: Standardized business term definitions
• Data Quality: Automated quality rules and scoring
• Lineage: Trace data from source to consumption
• Classification: Sensitivity levels and compliance tags

Governance Workflow

Create Object → Add Metadata → Link Terms → Quality Check → Approve → Publish

Roles:

• Data Owner: Business accountability
• Data Steward: Quality and metadata management
• Data Custodian: Technical implementation

For detailed governance guidance, see references/catalog-governance.md.

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Data Access Controls

Implement row-level security.

Types:

• Single Values: Simple value matching
• Operator and Values: Complex conditions
• Hierarchy: Node-based filtering
• Hierarchy with Directory: Hierarchical permissions

Application:

• Apply to views or analytic models
• Based on user attributes
• Import from SAP BW Analysis Authorizations

For security configuration, see references/data-access-security.md.

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Content Transport

Move content between tenants.

Methods:

• Export/Import packages
• SAP Cloud Transport Management
• CSN/JSON file export

Package Contents:

• Views, tables, flows
• Connections (metadata only)
• Spaces configuration

For transport procedures, see references/content-transport.md.

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Common Errors and Solutions

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Bundled Resources

Reference Documentation

Core Data Builder: 1. `references/data-acquisition-preparation.md` - Data flows, replication flows, transformation flows, and table management 2. `references/graphical-sql-views.md` - Graphical views, SQL views, E-R models, and intelligent lookups 3. `references/data-modeling.md` - Business Builder entities, analytic models, dimensions, measures, and hierarchies

Connectivity & Integration: 4. `references/connectivity.md` - All 40+ connection types including SAP systems, cloud providers, and streaming platforms 5. `references/data-integration-monitor.md` - Task scheduling, monitoring, real-time replication, and delta mechanisms

Administration & Security: 6. `references/administration.md` - Tenant management, space configuration, user roles, and elastic compute nodes 7. `references/data-access-security.md` - Row-level security, DAC configurations, and authorization scenarios 8. `references/content-transport.md` - Package export/import, transport management, and tenant migration

CLI & Automation: 9. `references/cli-commands.md` - Complete CLI reference, authentication, CI/CD integration patterns

Marketplace & Governance: 10. `references/data-products-marketplace.md` - Creating and consuming data products, provider workflows, pricing 11. `references/catalog-governance.md` - Data catalog, glossary, quality rules, lineage, classification

Best Practices & Updates: 12. `references/best-practices-patterns.md` - Architecture patterns, naming conventions, performance optimization, checklists 13. `references/whats-new-2025.md` - 2025 archive: Q1-Q4 2025 features, Generic HTTP, REST API tasks, deprecations 14. `references/whats-new-2026.md` - 2026 Datasphere/BDC features, including Q1-Q2 updates and 2026.11 items such as HANA Cloud calculation-view semantic onboarding, replication-flow scheduling, runtime-settings transport, SAP Snowflake, and SAPPHIRE announcements

Platform Context: 15. `references/business-data-cloud.md` - SAP Business Data Cloud architecture, Databricks integration, data products vs. marketplace, BDC Connect

MCP Integration: 16. `references/mcp-tools-reference.md` - Complete MCP tool reference, 45 tools across 8 categories, API documentation, authentication patterns 17. `references/mcp-use-cases.md` - 8 illustrative source-material use cases with personas and planning assumptions

MCP Reference Routing

Search MCP references before loading them. Use rg -n "<space|view|connection|task|lineage|marketplace|governance|admin>" references/mcp-*.md to locate the exact tool or scenario, then open only that excerpt.

• Use references/mcp-tools-reference.md for tool names, inputs, and direct tenant-operation boundaries.
• Use references/mcp-use-cases.md only when choosing a workflow pattern or reviewing illustrative impact assumptions; do not load the full use-case guide for ordinary tool lookup.
• If MCP access is unavailable, fall back to the matching CLI/reference command and mark tenant checks pending.

Plugin Components

This plugin includes 3 specialized agents, 5 slash commands, and validation hooks:

Agents (in agents/):

• datasphere-modeler - Data Builder tasks, views, flows, analytic models
• datasphere-integration-advisor - Connectivity, replication, data integration
• datasphere-admin-helper - Space management, security, monitoring

Commands (in commands/):

• /datasphere-space-template - Generate space configurations
• /datasphere-view-template - Generate view templates (graphical/SQL)
• /datasphere-connection-guide - Step-by-step connection setup
• /datasphere-cli - CLI command reference and examples
• /datasphere-mcp-tools - SAP Datasphere MCP tool reference and usage guidance

Hooks (in hooks/):

• PreToolUse validation for SQL/SQLScript code quality
• PostToolUse suggestions for persistence and optimization

MCP Integration

This skill includes a connection recipe for the SAP Datasphere MCP Server (@mariodefe/sap-datasphere-mcp). The bundled MCP config uses the approved exact pin 1.2.1, governed by sap-dependency-security and validated by npm run validate:mcp-security; package evidence records 1.4.0 as an upgrade candidate. Treat MCP behavior and live tenant tool counts as pending until your harness loads the server and verifies the available tools.

MCP Tools

The MCP server enables:

• Direct Queries: Execute SQL and smart queries on live data
• Metadata Access: Inspect tables, views, and analytic models
• User Management: Create, update, delete database users
• Catalog Search: Find assets by name or column
• Connection Testing: Verify connectivity and tenant info
• Data Profiling: Analyze column distributions

See /datasphere-mcp-tools command for complete tool list.

Authentication

OAuth 2.0 Client Credentials with automatic token refresh.

Required environment variables:

• DATASPHERE_BASE_URL
• DATASPHERE_CLIENT_ID
• DATASPHERE_CLIENT_SECRET
• DATASPHERE_TOKEN_URL

Performance

Performance depends on tenant size, network latency, payload size, permissions, and server version. Treat any example timings or batch limits in MCP reference material as illustrative until measured in the target tenant and recorded as evidence.

File Structure

plugins/sap-datasphere/
├── .claude-plugin/
│   └── plugin.json
├── .mcp.json                         # MCP server configuration
├── agents/
│   ├── datasphere-modeler.md
│   ├── datasphere-integration-advisor.md
│   └── datasphere-admin-helper.md
├── commands/
│   ├── datasphere-space-template.md
│   ├── datasphere-view-template.md
│   ├── datasphere-connection-guide.md
│   ├── datasphere-cli.md
│   └── datasphere-mcp-tools.md       # MCP tools reference
├── hooks/
│   └── hooks.json
└── skills/
    └── sap-datasphere/
        ├── .claude-plugin/
        │   └── plugin.json
        ├── SKILL.md
        ├── README.md
        └── references/
            ├── data-acquisition-preparation.md
            ├── data-modeling.md
            ├── graphical-sql-views.md
            ├── connectivity.md
            ├── administration.md
            ├── data-integration-monitor.md
            ├── data-access-security.md
            ├── content-transport.md
            ├── cli-commands.md
            ├── data-products-marketplace.md
            ├── catalog-governance.md
            ├── best-practices-patterns.md
            ├── whats-new-2025.md
            ├── whats-new-2026.md
            ├── business-data-cloud.md
            ├── mcp-tools-reference.md    # MCP technical reference

Documentation Links

• SAP Help Portal: https://help.sap.com/docs/SAP_DATASPHERE
• Source Repository: https://github.com/SAP-docs/sap-datasphere
• SAP Community: https://community.sap.com/topics/datasphere
• API Reference: https://api.sap.com/package/saaborddatasphere

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SAP Datasphere Skill

Portable SAP Datasphere skill for AI coding assistants, packaged with 3 specialized agent role guides, 5 command templates, validation hooks, and 17 reference documents covering data warehouse creation, analytic modeling, data integration, CLI automation, data marketplace, governance, and SAP Business Data Cloud.

Capability Index

Overview

SAP Datasphere is SAP's cloud-native data warehouse solution on SAP Business Technology Platform (BTP) and the data foundation of SAP Business Data Cloud (BDC). This plugin provides comprehensive guidance for building enterprise data warehouses with SAP Datasphere, including 2026 features such as task chain ports, replication flow enhancements, semantic onboarding from HANA Cloud calculation views, SAP Snowflake integration, and BDC data products.

When to Use

This skill activates when working with:

• SAP Datasphere tenant setup and configuration
• Data Builder (graphical views, SQL views, tables, flows)
• Business Builder (business entities, consumption models)
• Analytic models for SAP Analytics Cloud
• Data integration (replication, transformation, task chains)
• Connection configuration (40+ connection types)
• Space and user administration
• Data access controls and security
• Content transport between tenants
• CLI automation and CI/CD integration
• Data products and marketplace
• Catalog and governance

Plugin Components

Agents:

• datasphere-modeler - Data Builder tasks, views, flows, analytic models
• datasphere-integration-advisor - Connectivity, replication, data integration
• datasphere-admin-helper - Space management, security, monitoring

Commands:

• /datasphere-space-template - Generate space configurations
• /datasphere-view-template - Generate view templates
• /datasphere-connection-guide - Step-by-step connection setup
• /datasphere-cli - CLI command reference
• /datasphere-mcp-tools - SAP Datasphere MCP tool reference and usage guidance

Hooks:

• PreToolUse validation for SQL/SQLScript quality
• PostToolUse suggestions for optimization

MCP Integration

This plugin includes a Claude-compatible MCP connection recipe for the SAP Datasphere MCP Server. MCP use is optional and live-tenant behavior remains pending until the user configures credentials and verifies tool availability in their harness.

Setup

1. Install MCP Server:

   npm install -g @mariodefe/sap-datasphere-mcp@1.2.1

2. Configure Environment Variables: Create .env file in your project:

   DATASPHERE_BASE_URL=https://your-tenant.eu10.hcs.cloud.sap
   DATASPHERE_CLIENT_ID=your-oauth-client-id
   DATASPHERE_CLIENT_SECRET=your-oauth-client-secret
   DATASPHERE_TOKEN_URL=https://your-tenant.authentication.eu10.hana.ondemand.com/oauth/token

3. Get OAuth Credentials:

• Go to SAP BTP Cockpit → Subscriptions → SAP Datasphere
• Create OAuth2 Client (grant type: Client Credentials)
• Required scopes: READ, WRITE (based on needs)

Available Tools

When the approved MCP server is installed and verified, the documented server version is expected to expose 45 MCP tools across 8 categories. Confirm the actual tool list in your harness before relying on a specific count:

• Foundation: Connection testing, tenant info, space discovery
• Catalog: Asset search, column analysis
• Analytics: Query analytic models, smart queries
• ETL: Relational queries, bulk extract
• User Management: CRUD operations for DB users
• Metadata: Table/view/model structure inspection
• Search: Semantic and quick find
• Space Management: Space details and permissions

Use /datasphere-mcp-tools command to see all available tools.

Usage in Agents

In Claude-compatible clients, all three agents can be granted relevant MCP tools. In other harnesses, use the same agent files as role guidance and run MCP tools only if your client has been configured for them:

• datasphere-modeler: Queries, metadata, data preview
• datasphere-integration-advisor: Connection testing, space discovery
• datasphere-admin-helper: User management, permissions

Commands Using MCP

• /datasphere-mcp-tools - List all MCP tools

Illustrative Use Cases

See mcp-use-cases.md for 8 illustrative source-material use cases. Time and ROI examples are planning assumptions, not repository-verified productivity outcomes:

• The Monday Morning Health Check: 45 min/day saved for Data Operations Managers
• Data Lineage Inspection: 3 hours saved per investigation for Data Engineers
• Pre-Analytics Data Quality Audit: 2 hours saved per project for Data Analysts
• The Onboarding Speedrun: 2 days saved for New Data Engineers
• The Marketplace Shopping Spree: 1 hour saved per evaluation for Analytics Team Leads
• The Security Audit: 4 hours saved per audit for Data Governance Managers
• The Performance Troubleshooter: 1.5 hours saved per incident for Data Platform Engineers
• Cross-Functional Collaboration: 3 hours saved per issue for Business Analysts + Data Engineers

Illustrative ROI model: $159,100+/year for mid-sized teams. Treat this as planning material, not validated repository evidence.

Keywords

Product Terms: sap datasphere, data warehouse cloud, dwc, sap btp data warehouse, datasphere tenant, datasphere space, sap business data cloud

MCP Integration: mcp, model context protocol, oauth, live tenant, real-time data, direct queries, mcp tools, mcp server, datasphere mcp, tenant interaction

Data Builder: data builder, graphical view, sql view, sqlscript, local table, remote table, data flow, replication flow, transformation flow, task chain, e-r model, intelligent lookup, rest api task

Business Builder: business builder, business entity, fact model, consumption model, authorization scenario

Analytic Modeling: analytic model, dimension, fact, measure, hierarchy, calculated measure, restricted measure, currency conversion, unit conversion, time dimension, fiscal calendar, secondary structure

Connectivity: datasphere connection, cloud connector, data provisioning agent, sap s4hana connection, bw4hana connection, hana cloud connection, aws connection, azure connection, gcp connection, kafka connection, odata connection, jdbc connection, generic http connection, microsoft fabric, databricks, snowflake, mongodb

Administration: datasphere administration, space management, user management, role management, elastic compute node, monitoring, audit log, workload management, scim api

CLI: datasphere cli, datasphere command line, datasphere config, datasphere spaces, datasphere objects, datasphere tasks, datasphere marketplace, ci cd automation, service key authentication

Integration: data integration, real-time replication, delta replication, cdc, data persistence, view analyzer, scheduling, view persistence, partition strategy

Security: data access control, row-level security, dac, single values dac, hierarchy dac, column level security, dynamic masking

Marketplace: data products, data marketplace, data provider, data consumer, data sharing, data monetization

Catalog & Governance: data catalog, glossary, business terms, data quality, data lineage, impact analysis, data classification, data steward, data owner

Transport: content transport, export package, import package, csn json, cloud transport management

2025–2026 Features: generic http connection, rest api task, sap business data cloud, bdc, analytic model enhancements, elastic compute auto-scaling, task chain ports, auto-retry, technical user scheduling, sap databricks, sap snowflake, data composer

Errors: datasphere deployment failed, connection timeout, replication error, out of memory, permission denied, circular dependency

File Structure

plugins/sap-datasphere/
├── .claude-plugin/
│   └── plugin.json
├── .mcp.json                         # MCP server configuration
├── agents/
│   ├── datasphere-modeler.md
│   ├── datasphere-integration-advisor.md
│   └── datasphere-admin-helper.md
├── commands/
│   ├── datasphere-space-template.md
│   ├── datasphere-view-template.md
│   ├── datasphere-connection-guide.md
│   ├── datasphere-cli.md
│   └── datasphere-mcp-tools.md       # MCP tools reference
├── hooks/
│   └── hooks.json
└── skills/
    └── sap-datasphere/
        ├── .claude-plugin/
        │   └── plugin.json
        ├── SKILL.md
        ├── README.md
        └── references/
            ├── data-acquisition-preparation.md
            ├── data-modeling.md
            ├── graphical-sql-views.md
            ├── connectivity.md
            ├── administration.md
            ├── data-integration-monitor.md
            ├── data-access-security.md
            ├── content-transport.md
            ├── cli-commands.md
            ├── data-products-marketplace.md
            ├── catalog-governance.md
            ├── best-practices-patterns.md
            ├── whats-new-2025.md
            ├── whats-new-2026.md
            ├── business-data-cloud.md
            ├── mcp-tools-reference.md    # MCP technical reference
            └── mcp-use-cases.md          # MCP real-world use cases

Documentation Sources

• SAP Help Portal: https://help.sap.com/docs/SAP_DATASPHERE
• GitHub Repository: https://github.com/SAP-docs/sap-datasphere
• SAP Community: https://community.sap.com/topics/datasphere
• API Reference: https://api.sap.com/package/sapdatasphere
• CLI Documentation: https://help.sap.com/docs/SAP_DATASPHERE/d0ecd6f297ac40249072a44df0549c1a
• Best Practices: https://pages.community.sap.com/topics/datasphere/best-practices-troubleshooting

Coverage

This plugin provides comprehensive coverage of SAP Datasphere with:

Version

• Plugin Version: 3.0.0
• Skill Version: 2.2.0
• Last Verified: 2026-06-11
• SAP Datasphere Version: 2026.12 (June 2026)

License

GPL-3.0

Administration Reference

Source: https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Administering

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Table of Contents

1. Tenant Configuration 2. Spaces and Storage 3. Users and Roles 4. Identity and Authentication 5. Monitoring 6. Elastic Compute Nodes 7. Data Provisioning Agent 8. System Maintenance

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Tenant Configuration

Creating a Tenant

SAP BTP Service Instance: 1. Access SAP BTP Cockpit 2. Navigate to Subaccount 3. Create SAP Datasphere service instance 4. Configure initial sizing

Plan Options:

Configuring Tenant Size

Capacity Parameters:

• Storage (GB)
• In-memory (GB)
• Compute units

Sizing Recommendations:

System Information

Display System Info:

• System > About
• View tenant ID
• Check version
• Monitor capacity usage

SAP HANA Configuration

Enable Script Server: 1. System > Configuration 2. Enable SAP HANA Cloud Script Server 3. Required for Python, R, AFL

Enable SQL Data Warehousing: 1. System > Configuration 2. Enable SAP HANA SQL Data Warehousing 3. Allows HDI container deployment

Additional Features

Enable SAP Business AI:

• AI-powered features
• Intelligent recommendations
• Natural language queries

Enable Choropleth Layers:

• Geographic visualizations
• Map-based analytics

OAuth 2.0 Configuration

Client Types:

Creating OAuth Client: 1. System > Security > OAuth 2.0 Clients 2. Create new client 3. Configure client type 4. Note client ID and secret

API Access Configuration:

{
  "clientid": "sb-xxx",
  "clientsecret": "xxx",
  "url": "https://xxx.authentication.xxx.hana.ondemand.com",
  "apiurl": "https://xxx.hana.ondemand.com"
}

Trusted Identity Providers

Add external IdP for authentication: 1. System > Security > Identity Providers 2. Add trusted IdP 3. Configure SAML settings 4. Map user attributes

---

Spaces and Storage

Creating Spaces

Standard Space: 1. Space Management > Create 2. Enter space name 3. Configure storage 4. Assign users

File Space: 1. Space Management > Create File Space 2. Configure object store 3. Set data lake connection

Space Properties

Initial Creation Fields:

General Settings (Read-Only):

• Space Status (newly-created spaces are active)
• Space Type (SAP Datasphere only)
• Created By/On timestamps
• Deployment Status and Deployed On

Optional Configuration:

Deployment: Spaces require deployment after creation and re-deployment after modifications.

Technical Naming Rules (Space ID)

Valid Space IDs:

• UPPERCASE letters, numbers, underscores only
• Maximum 20 characters
• No spaces or special characters

Reserved Prefixes (Avoid):

• _SYS - System reserved
• DWC_ - Datasphere reserved
• SAP_ - SAP reserved

Example: SALES_ANALYTICS_2024

Storage Allocation

Allocate Storage: 1. Open space settings 2. Set disk storage (GB) 3. Set in-memory storage (GB) 4. Save changes

Storage Types:

Space Priorities

Priority Levels: 1. High: Critical workloads 2. Medium: Standard workloads 3. Low: Background tasks

Statement Limits:

• Maximum memory per query
• Query timeout
• Concurrent connections

Space Operations

Copy Space: 1. Space Management 2. Select source space 3. Copy with/without data 4. New space name

Delete Space: 1. Remove all objects 2. Remove all users 3. Delete space

Restore from Recycle Bin: 1. System > Recycle Bin 2. Select deleted space 3. Restore or permanently delete

Command Line Management

datasphere CLI:

# Login
datasphere login

# List spaces
datasphere spaces list

# Create space
datasphere spaces create --name my_space --storage 100

# Delete space
datasphere spaces delete --name my_space

---

Users and Roles

User Management

Creating Users: 1. Security > Users 2. Create user 3. Enter email 4. Assign roles

User Properties:

Role Types

Global Roles:

• Apply across all spaces
• System-level permissions

Scoped Roles:

• Space-specific permissions
• Object-level access

Standard Roles

Role Privileges

System Privileges:

• Lifecycle: Deploy, monitor, transport
• User Management: Create, assign users
• Security: Manage access controls

Space Privileges:

• Create Objects
• Read Objects
• Update Objects
• Delete Objects
• Share Objects

Creating Custom Roles

1. Security > Roles > Create 2. Enter role name 3. Select privileges 4. Assign to users

Scoped Roles

Creating Scoped Role: 1. Security > Roles > Create Scoped 2. Define base privileges 3. Assign spaces 4. Assign users

Scope Options:

• All spaces
• Selected spaces
• Space categories

Role Assignment

Direct Assignment:

• Security > Users > Assign Roles

SAML Attribute Mapping:

• Map IdP attributes to roles
• Automatic role assignment
• Dynamic membership

SCIM 2.0 API

User Provisioning:

POST /api/v1/scim/Users
Content-Type: application/json

{
  "userName": "user@example.com",
  "name": {
    "givenName": "John",
    "familyName": "Doe"
  },
  "emails": [{"value": "user@example.com"}]
}

View Authorizations

By User:

• All roles assigned
• All spaces accessible
• Effective permissions

By Role:

• All users with role
• Permission details

By Space:

• All users in space
• Role breakdown

---

Identity and Authentication

SAP Cloud Identity Services

Bundled IdP:

• Included with SAP Datasphere
• Basic user management
• SAML 2.0 support

Configuration: 1. Access Identity Authentication admin 2. Configure application 3. Set user attributes 4. Enable SSO

Custom SAML Identity Provider

Requirements:

• SAML 2.0 compliant IdP
• Metadata exchange
• Attribute mapping

Setup: 1. Export Datasphere SAML metadata 2. Import to IdP 3. Export IdP metadata 4. Import to Datasphere 5. Configure attribute mapping

SAML Attributes:

Certificate Management

SAML Signing Certificates:

• Update before expiration
• Coordinate with IdP
• Test after update

Database User Password Policy

Policy Settings:

• Minimum length
• Complexity requirements
• Expiration period
• History depth

---

Monitoring

Capacity Monitoring

Monitor:

• Storage usage
• Memory consumption
• Compute utilization

Alerts:

• Configure thresholds
• Email notifications
• Automatic warnings

Audit Logs

Database Audit Logs:

• DDL operations (CREATE, ALTER, DROP)
• DML operations (SELECT, INSERT, UPDATE, DELETE)
• Login/logout events

Configuration: 1. System > Audit 2. Enable audit logging 3. Select event types 4. Set retention period

Delete Audit Logs:

• Manual deletion
• Scheduled cleanup
• Retention-based removal

Activity Logs

Tracked Activities:

• Object creation
• Object modification
• Object deletion
• Deployments

Task Logs

Task Types Logged:

• Data flows
• Replication flows
• Transformation flows
• Task chains

Task Log Properties:

Display Limitations:

• Only first 1,000 rows displayed for performance
• Filters applied to all rows, but only first 1,000 filtered rows shown
• Use filters to find specific data

Decimal Separator Note: Use '.' (period) as decimal separator regardless of regional settings when filtering on memory/CPU columns.

CPU Time Measurement: CPU time measures time used by all threads. If much higher than statement duration, indicates heavy thread usage which can lead to resource bottlenecks.

Log Management:

• View execution history
• Download logs
• Delete old logs

Notifications

Configure Notifications: 1. User profile > Notifications 2. Select event types 3. Choose delivery method

Notification Types:

• Task completion
• Task failure
• System alerts
• Capacity warnings

Database Analysis Users

Create Analysis User: 1. System > Monitoring 2. Create database analysis user 3. Grant analysis privileges 4. Connect with SQL tools

Analysis Capabilities:

• Query monitoring views
• Analyze execution plans
• Debug performance issues

Stop Running Statements:

-- Find running statements
SELECT * FROM M_ACTIVE_STATEMENTS;

-- Cancel statement
ALTER SYSTEM CANCEL SESSION 'connection_id';

SAP HANA Monitoring Views

System Views:

SAP Cloud ALM Integration

Health Monitoring:

• Integration for checking tenant health
• Real-time health status

Job & Automation Monitoring:

• Monitor tasks (except child tasks)
• Integration with SAP Cloud ALM dashboard

SAP HANA Cockpit Integration

Access via "Open SAP HANA Cockpit" links in System Monitor:

• Performance Monitor for real-time CPU/memory utilization
• Database Overview page for HANA analysis
• Admission Control analysis

---

Elastic Compute Nodes

Overview

Elastic compute nodes provide additional processing capacity for intensive workloads.

Creating Elastic Compute Node

1. System > Elastic Compute Nodes 2. Create new node 3. Configure capacity 4. Set warm-up schedule

Node Configuration

Running Elastic Compute

Start Node: 1. Select node 2. Start manually or schedule 3. Wait for warm-up 4. Execute workloads

Assign Workloads:

• Data flows
• Transformation flows
• Specific queries

Resource Purchase

Capacity Units:

• Billed by consumption
• Pre-purchase options
• Monitor usage

---

Data Provisioning Agent

Installation

Requirements:

• Java 11+
• Network access to sources
• Network access to Datasphere

Installation Steps: 1. Download agent from SAP 2. Install on-premise server 3. Configure connection 4. Register with Datasphere

Configuration

Agent Properties:

# Connection settings
datasphere.tenant.url=https://xxx.hana.ondemand.com
datasphere.agent.name=dp_agent_01

# Performance settings
datasphere.threads.max=10
datasphere.batch.size=10000

Adapter Registration

Register Adapters: 1. System > Data Provisioning 2. Select agent 3. Register adapter 4. Configure connection

Supported Adapters:

• ABAP ODP
• HANA SDI
• File adapters
• Database adapters

Agent Monitoring

Status Monitoring:

• Connection status
• Replication status
• Error logs

Log Access: 1. Enable log access 2. View logs in Datasphere 3. Download for analysis

Pause Replication

Pause Agent:

• Maintenance window
• Network issues
• Source system updates

Resume Agent:

• Verify connectivity
• Check queue status
• Resume replication

---

System Maintenance

HANA Database Operations

Restart Database: 1. System > HANA Cloud 2. Restart database 3. Wait for recovery 4. Verify connections

Apply Patch Upgrades: 1. Review available patches 2. Schedule maintenance window 3. Apply patch 4. Validate functionality

Support Requests

Request SAP Support: 1. System > Support 2. Create incident 3. Provide details 4. Attach logs

Required Information:

• Tenant ID
• Error messages
• Steps to reproduce
• Screenshots/logs

---

Documentation Links

• Tenant Configuration: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/2f80b57
• Space Management: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/2ace657
• User Management: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/4fb82cb
• Monitoring: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/28910cd

---

Last Updated: 2025-11-22

SAP Datasphere Best Practices and Patterns

Overview

This document consolidates best practices for SAP Datasphere development, covering architecture, modeling, performance, and operations. These patterns are derived from SAP recommendations and real-world implementations.

Community Best Practices: https://pages.community.sap.com/topics/datasphere/best-practices-troubleshooting

Architecture Patterns

Layered Data Architecture

Implement a medallion/layered architecture for clarity and maintainability:

┌─────────────────────────────────────────────────────────────────┐
│                      CONSUMPTION LAYER                          │
│  Analytic Models, Consumption Views, Business Entities          │
│  Purpose: Consumer-ready analytics, SAC integration             │
└────────────────────────────────────────────────────────────────┘
                              ↑
┌─────────────────────────────────────────────────────────────────┐
│                      HARMONIZATION LAYER                        │
│  Fact Views, Dimension Views, Master Data                       │
│  Purpose: Standardized business objects, associations           │
└────────────────────────────────────────────────────────────────┘
                              ↑
┌─────────────────────────────────────────────────────────────────┐
│                      STAGING LAYER                              │
│  Cleansed Views, Validated Tables                               │
│  Purpose: Data quality, standardization, deduplication          │
└────────────────────────────────────────────────────────────────┘
                              ↑
┌─────────────────────────────────────────────────────────────────┐
│                      RAW LAYER                                  │
│  Remote Tables, Local Tables (raw copies)                       │
│  Purpose: Source data landing, unchanged format                 │
└────────────────────────────────────────────────────────────────┘

Naming Conventions

Prefixes by Layer:
  Raw: raw_<source>_<entity>
  Staging: stg_<source>_<entity>
  Dimension: dim_<entity>
  Fact: fact_<subject>
  Analytic Model: am_<subject>
  Data Access Control: dac_<scope>

Technical Names:
  - UPPER_SNAKE_CASE
  - Max 30 characters recommended
  - Avoid special characters

Business Names:
  - Title Case with spaces
  - Human-readable
  - Match glossary terms

Space Organization

Space Strategy:

  Single Tenant Pattern:
    - INTEGRATION: Data loading and staging
    - HARMONIZATION: Business layer modeling
    - ANALYTICS: Consumption models
    - SANDBOX: Development and testing

  Multi-Tenant Pattern:
    - INT_<REGION>: Regional integration
    - CORE_DATA: Shared master data
    - ANALYTICS_<BU>: Business unit analytics
    - SHARED: Cross-BU shared content

Modeling Best Practices

View Design

DO:

Good Practices:
  - Use meaningful business names for all objects
  - Document views with clear descriptions
  - Define proper semantic usage (Fact, Dimension)
  - Create associations between related entities
  - Use calculated columns for derived values
  - Apply filters early to reduce data volume

DON'T:

Avoid:
  - SELECT * without column selection
  - Hardcoded schema names
  - Missing key definitions
  - Circular associations
  - Complex nested views (>5 levels)
  - Mixing semantic types in single view

Analytic Model Design

Analytic Model Checklist:
  Structure:
    - [ ] Clear fact source identified
    - [ ] All dimensions linked via associations
    - [ ] Key columns properly defined
    - [ ] Measures use correct aggregation

  Measures:
    - [ ] Simple measures mapped directly
    - [ ] Calculated measures use correct formulas
    - [ ] Restricted measures have valid filters
    - [ ] Currency conversion configured if needed

  Dimensions:
    - [ ] Time dimension included
    - [ ] Hierarchies defined where needed
    - [ ] Text associations for descriptions
    - [ ] Master data properly linked

  Variables:
    - [ ] Filter variables for user selection
    - [ ] Reference date for time-dependent analysis
    - [ ] Default values set appropriately

Association Patterns

-- Standard Association
-- fact_sales → dim_product
-- Join: fact_sales.PRODUCT_ID = dim_product.PRODUCT_ID

-- Text Association (for language-dependent text)
-- dim_product → text_product
-- Join: dim_product.PRODUCT_ID = text_product.PRODUCT_ID
--       AND text_product.LANGUAGE = $session.language

-- Hierarchy Association
-- dim_org → hier_org
-- Join: dim_org.ORG_ID = hier_org.NODE_ID

Performance Optimization

View Performance

Optimization Techniques:

  1. Filter Pushdown:
     - Apply WHERE clauses in innermost views
     - Use variables for dynamic filtering
     - Avoid functions on filter columns

  2. Projection Pushdown:
     - Select only needed columns
     - Avoid SELECT * in production views
     - Remove unused calculated columns

  3. Join Optimization:
     - Use appropriate join types (INNER when possible)
     - Order joins by selectivity
     - Consider denormalization for hot paths

  4. Aggregation:
     - Aggregate at lowest level possible
     - Use pre-aggregated views for common queries
     - Consider materialized summaries

Persistence Strategy

When to Persist:

  Always Persist:
    - Views used in multiple downstream views
    - Complex joins with >3 tables
    - Heavy transformations (string parsing, etc.)
    - External source views with latency

  Consider Persisting:
    - Views with >1M rows
    - Frequently accessed views
    - Views with calculated columns

  Avoid Persisting:
    - Simple single-table projections
    - Views with high change frequency
    - Small reference tables

Partition Strategy

Partitioning Guidelines:

  Time-Based (Most Common):
    Column: ORDER_DATE, CREATED_AT
    Granularity: YEAR, MONTH
    Use When: Historical data, time-series analysis

  Range-Based:
    Column: CUSTOMER_ID, REGION
    Granularity: Value ranges
    Use When: Known distribution, regional queries

  Best Practices:
    - Partition on frequently filtered columns
    - Balance partition sizes (avoid skew)
    - Consider query patterns
    - Monitor partition usage

Data Integration Best Practices

Replication Strategy

Full Load vs Delta:

  Use Full Load When:
    - Small tables (<100K rows)
    - No change tracking in source
    - Complete refresh acceptable
    - Initial load

  Use Delta Load When:
    - Large tables (>100K rows)
    - Source supports CDC/ODP
    - Near-real-time needed
    - Minimize transfer volume

Task Chain Patterns

Sequential Pattern:
  1. Replicate Source Tables
  2. Run Transformation Flow
  3. Refresh Materialized Views
  4. Send Notification

Parallel Pattern:
  1. Start
  2. [Parallel] Replicate Table A | Replicate Table B | Replicate Table C
  3. [Wait All]
  4. Run Aggregation
  5. End

Error Handling:
  1. Try: Main ETL Logic
  2. Catch: Log Error, Send Alert
  3. Finally: Update Status Table, Cleanup

Connection Best Practices

Connection Security:
  - Use OAuth 2.0 when available
  - Rotate credentials regularly
  - Use service accounts, not personal credentials
  - Enable connection encryption

Connection Management:
  - Name connections descriptively
  - Document connection owners
  - Test connections after changes
  - Monitor connection health

Security Best Practices

Data Access Controls

DAC Design Principles:

  1. Principle of Least Privilege:
     - Start with no access
     - Add permissions explicitly
     - Regular access reviews

  2. Reusable Controls:
     - Create generic DACs for common patterns
     - Use permission tables for flexibility
     - Avoid hardcoded values

  3. Performance:
     - Keep permission tables small
     - Index permission lookup columns
     - Test with production-like data volumes

Permission Table Pattern

-- Permission table structure
CREATE TABLE permission_region (
    USER_ID NVARCHAR(100),
    REGION_ID NVARCHAR(20),
    ACCESS_LEVEL NVARCHAR(20),  -- READ, WRITE, ADMIN
    VALID_FROM DATE,
    VALID_TO DATE
);

-- Sample permissions
INSERT INTO permission_region VALUES
('user1@company.com', 'EMEA', 'READ', '2024-01-01', '9999-12-31'),
('user1@company.com', 'APAC', 'READ', '2024-01-01', '9999-12-31'),
('user2@company.com', 'AMER', 'ADMIN', '2024-01-01', '9999-12-31');

Operational Best Practices

Monitoring

Key Metrics to Monitor:

  Storage:
    - Disk usage by space
    - Table growth trends
    - Unused object identification

  Performance:
    - Query execution times
    - Long-running statements
    - Failed executions

  Integration:
    - Flow execution status
    - Replication latency
    - Connection health

  Users:
    - Active user count
    - Query patterns
    - Failed logins

Troubleshooting Checklist

Slow Query Troubleshooting:
  1. [ ] Check View Analyzer for execution plan
  2. [ ] Verify table statistics are current
  3. [ ] Check for missing persistence
  4. [ ] Review join strategy
  5. [ ] Look for filter pushdown opportunities
  6. [ ] Check for engine mixing (row vs column store)

Failed Flow Troubleshooting:
  1. [ ] Check flow execution logs
  2. [ ] Verify source connection status
  3. [ ] Check target table constraints
  4. [ ] Review data type mappings
  5. [ ] Check for resource limits
  6. [ ] Verify credentials not expired

Documentation Standards

Object Documentation:

  Views:
    - Purpose and use case
    - Source systems
    - Refresh frequency
    - Key columns
    - Known limitations

  Flows:
    - Source and target
    - Transformation logic
    - Schedule
    - Error handling
    - Notification contacts

  Spaces:
    - Purpose
    - Team ownership
    - Data domains
    - Key objects
    - Related spaces

Anti-Patterns to Avoid

Common Mistakes

Architecture Anti-Patterns:
  - Flat structure without layers
  - Everything in one space
  - No naming conventions
  - Missing documentation

Modeling Anti-Patterns:
  - Overly complex single views
  - Missing associations
  - Wrong semantic usage
  - No key definitions

Performance Anti-Patterns:
  - No persistence strategy
  - SELECT * usage
  - Filter at wrong layer
  - Unnecessary complexity

Security Anti-Patterns:
  - Hardcoded credentials
  - Over-permissive access
  - No access reviews
  - Shared service accounts

Checklist Templates

Pre-Production Checklist

Before Go-Live:
  Code Quality:
    - [ ] All views have descriptions
    - [ ] Naming conventions followed
    - [ ] No hardcoded values
    - [ ] Error handling in flows

  Performance:
    - [ ] Performance tested with production volumes
    - [ ] Persistence strategy implemented
    - [ ] Statistics enabled

  Security:
    - [ ] Data access controls in place
    - [ ] Credentials secured
    - [ ] Access reviewed

  Operations:
    - [ ] Monitoring configured
    - [ ] Alerting set up
    - [ ] Documentation complete
    - [ ] Support contacts identified

Monthly Review Checklist

Monthly Tasks:
  - [ ] Review storage usage
  - [ ] Check for unused objects
  - [ ] Validate backup/transport
  - [ ] Review access permissions
  - [ ] Check credential expiration
  - [ ] Update documentation
  - [ ] Review performance trends
  - [ ] Update statistics

SAP Business Data Cloud

Overview

SAP Business Data Cloud (BDC) is SAP's unified data and analytics platform that brings together SAP Datasphere, SAP Analytics Cloud, SAP HANA Cloud, and curated data products under a single SaaS offering. Launched at SAP Business Unleashed on February 13, 2025, with controlled commercial availability expanding through 2025 and 2026, BDC provides an integrated environment for data management, analytics, planning, and enterprise AI.

Official Help Portal: https://help.sap.com/docs/business-data-cloud

What is SAP Business Data Cloud

SAP Business Data Cloud is a SaaS solution that combines multiple SAP data and analytics products into a single, unified platform:

BDC is managed through the BDC Cockpit, which provides administrators with visibility into data product pipelines, system connectivity, installation/activation progress, and overall platform health.

Official product page: https://www.sap.com/products/data-cloud.html

Where Datasphere Fits

SAP Datasphere remains the data foundation within BDC. For developers and data engineers, Datasphere continues to function as the primary workspace for:

• Data modeling (views, tables, analytic models)
• Data integration (replication flows, transformation flows, data flows, task chains)
• Semantic layer (business entities, consumption models)
• Data products (creating, publishing, and consuming governed data products)
• Catalog and governance (metadata, lineage, glossary)

What does NOT change for Datasphere developers:

• Existing Datasphere tenants, spaces, objects, and connections continue to work
• The Datasphere UI, CLI, and APIs remain the primary developer interface
• Data Builder and Business Builder tools are unchanged
• Connection types, security model, and administration are the same

What changes for Datasphere developers in a BDC formation:

• Access to curated data products from SAP applications (e.g., S/4HANA) through the BDC catalog
• Ability to share data products to SAP Databricks and SAP Snowflake via zero-copy Delta Sharing
• Integration with intelligent applications — pre-built analytics that combine data products with Datasphere modeling and SAC stories
• Enhanced monitoring through the BDC Cockpit for SAP-managed data pipelines
• Access to SAP Datasphere, Data Composer — a service for harmonizing data from multiple data products into unified customer profiles
• Semantic onboarding of HANA Cloud calculation views with preserved metadata (2026.11+)

Data Products in BDC vs. Datasphere Data Marketplace

The Datasphere Data Marketplace (covered in references/data-products-marketplace.md) allows Datasphere tenants to share and consume data products between spaces and tenants. BDC extends this model:

BDC data products are installed into Datasphere spaces and can be consumed in views and analytic models just like marketplace data products. The key addition is that BDC provides SAP-managed data products from source systems like S/4HANA, along with the infrastructure to share them to SAP Databricks and SAP Snowflake.

Data product lifecycle in BDC (as of Q2 2026):

• Individual data product management (activate, deactivate, update independently of packages)
• Pre-installation/activation/update validation checks
• Data product status tracking with granular lifecycle states
• Association with intelligent applications and data packages

SAP Databricks Integration

SAP Databricks brings the Databricks Data Intelligence Platform natively into the SAP landscape, fully integrated with BDC:

Key capabilities:

• Provisioned via SAP for Me — no separate Databricks account required
• Zero-copy data product sharing from BDC catalog to Databricks Unity Catalog
• Semantic metadata in Unity Catalog — BDC data product metadata (business names, descriptions, lineage) is accessible directly in Databricks (GA April 2026)
• Bidirectional sharing — data products can flow from Databricks back to BDC
• Databricks' full AI/ML stack available on SAP data with business context preserved

SAP Databricks documentation: https://docs.databricks.com/sap/en/

BDC Connect for Databricks enables bidirectional data sharing between existing Enterprise Databricks instances and BDC, using the same Delta Sharing protocol as the native SAP Databricks integration.

Related external skills: If your task involves working inside

Databricks (notebooks, Unity Catalog, Spark jobs, SAP Databricks in BDC),

the Databricks agent skills plugin covers that side:

https://github.com/databricks/databricks-agent-skills — offer the user to

install it; never install it unprompted.

BDC Connect Partner Integration

BDC Connect extends BDC's data fabric to partner platforms:

BDC Connect provisioning: https://help.sap.com/docs/SAP_BUSINESS_DATA_CLOUD/f7acf8c9dad54e99b5ce5ebc633ed8e1/ccbd8fe7c2394009b546b73b1dd6c164.html

SAPPHIRE 2026 Announcements

At SAPPHIRE Orlando (May 2026), SAP announced significant BDC direction:

• SAP Business AI Platform: BTP + BDC + Business Transformation Management converging into a unified AI platform with the SAP Knowledge Graph at its core
• SAP HANA Cloud natively in BDC: HANA Cloud becomes a core BDC component for transactional, analytical, and multi-model workloads
• MDG and Reltio in BDC: SAP Master Data Governance added as a BDC component; Reltio acquisition brings AI-based entity resolution and MCP support
• Joule Agents: Data products, analytic models, and planning structures creatable through natural language
• SAP Certified Data Architect: New certification program covering data strategy for the agentic AI era

Official Resources

• BDC Help Portal: https://help.sap.com/docs/business-data-cloud
• BDC What's New: https://help.sap.com/whats-new/31c53b47cac6482d89f167a1d2a4a50b
• BDC Onboarding Guide: https://help.sap.com/docs/SAP_BUSINESS_DATA_CLOUD/9b36d0ac59f24cbeb45617e36a7680fc
• BDC Feature Scope: https://help.sap.com/docs/SAP_BUSINESS_DATA_CLOUD/be7aeed9fd524097a626867e7e9bf151
• SAP Datasphere Help: https://help.sap.com/docs/SAP_DATASPHERE
• Datasphere What's New: https://help.sap.com/whats-new/48017b2cc4834fc6b6cae87097bd9e4d
• SAP Architecture Center - BDC: https://architecture.learning.sap.com/docs/ref-arch/a07a316077/3
• Monthly Community Blog: https://community.sap.com/t5/technology-blog-posts-by-sap/bg-p/technology-blog-sap (search "Business Data Cloud and Datasphere News")

SAP Datasphere Catalog and Governance

Overview

The SAP Datasphere Catalog provides a centralized hub for discovering, understanding, and governing data assets. It enables organizations to implement data governance practices while making trusted data accessible to business users.

Documentation: https://help.sap.com/docs/SAP_DATASPHERE/aca3ccb4b2f84eb8b6154e8fd2812c0e

Catalog Features

Asset Discovery

The catalog allows users to discover and explore:

• Data Products: Curated packages of views and models for specific use cases
• Views and Tables: Individual data objects with metadata
• Analytic Models: Consumer-ready analytics with measures and dimensions
• Glossary Terms: Business definitions and context
• KPIs: Key performance indicators with calculations

Catalog Capabilities

Glossary Management

Creating a Glossary

A business glossary provides standardized definitions for key business terms.

Glossary Structure:
  Categories:
    - Finance
    - Sales
    - HR
    - Supply Chain

  Term Components:
    - Name: Business-friendly term
    - Definition: Clear, unambiguous description
    - Examples: Usage examples
    - Synonyms: Alternative names
    - Related Terms: Links to related definitions
    - Owner: Responsible party
    - Status: Draft, Approved, Deprecated

Example Glossary Terms

Revenue

Term: Revenue
Category: Finance
Definition: The total income generated from sales of goods or services before any expenses are deducted
Calculation: SUM(Net Sales) for a given period
Synonyms: Sales, Turnover, Income
Related Terms: Gross Revenue, Net Revenue, Deferred Revenue
Owner: Finance Data Steward
Status: Approved

Customer Lifetime Value (CLV)

Term: Customer Lifetime Value
Abbreviation: CLV
Category: Sales
Definition: The predicted net profit attributed to the entire future relationship with a customer
Calculation: (Average Order Value × Purchase Frequency × Customer Lifespan) - Acquisition Cost
Related Terms: Customer Acquisition Cost, Churn Rate
Owner: Marketing Analytics
Status: Approved

Linking Terms to Data Assets

Associate glossary terms with views and columns:

1. Navigate to view in Data Builder 2. Open Business Purpose section 3. Link relevant glossary terms to:

• The view itself (overall purpose)
• Individual columns (column meaning)

Data Quality

Quality Rules

Define rules to validate data quality:

Quality Rule Types:
  Completeness:
    - Not Null checks
    - Required field validation

  Uniqueness:
    - Primary key uniqueness
    - Duplicate detection

  Validity:
    - Range checks (min/max)
    - Pattern matching (regex)
    - Domain values (allowed list)

  Timeliness:
    - Freshness checks
    - SLA monitoring

  Consistency:
    - Cross-field validation
    - Referential integrity

Quality Score Calculation

Quality Score = (Passed Records / Total Records) × 100

Aggregate Score = Weighted Average of:
  - Completeness Score (weight: 0.25)
  - Uniqueness Score (weight: 0.25)
  - Validity Score (weight: 0.30)
  - Timeliness Score (weight: 0.20)

Quality Monitoring

Set up continuous quality monitoring:

1. Create Quality Rules: Define expectations 2. Schedule Validation: Run rules on schedule 3. Set Thresholds: Define acceptable quality levels 4. Configure Alerts: Notify when quality drops 5. Track Trends: Monitor quality over time

Data Classification

Sensitivity Levels

Classify data by sensitivity:

Data Categories

Auto-Classification

Configure automatic classification based on:

1. Column Names: Match patterns like *_SSN, *_EMAIL 2. Data Patterns: Detect formats like phone numbers, credit cards 3. Source Systems: Apply rules based on origin 4. Glossary Terms: Inherit classification from linked terms

Data Lineage

Lineage Visualization

View data flow from source to consumption:

Source System → Remote Table → Staging View → Fact View → Analytic Model → SAC Story

Lineage Information

Impact Analysis

Before making changes, understand impact:

1. Select object in Data Builder 2. Click Impact Analysis 3. Review:

• Downstream dependencies
• Affected reports/stories
• User impact

Publishing to Catalog

Publication Workflow

1. Create Object → 2. Add Metadata → 3. Link Terms → 4. Request Approval → 5. Publish

Required Metadata for Publication

Publication Requirements:
  Required:
    - Business Name (readable name)
    - Description (purpose and content)
    - Owner (responsible person)
    - Classification (sensitivity level)

  Recommended:
    - Glossary Term Links
    - Quality Score
    - Data Freshness
    - Sample Data
    - Usage Guidelines

Approval Process

1. Submit for Review: Owner submits asset 2. Steward Review: Data steward validates 3. Quality Check: Automated quality validation 4. Approve/Reject: Decision with feedback 5. Publish: Make available in catalog

Governance Roles

Standard Roles

Role Assignments

Configure governance roles in Administration:

Governance Role Assignment:
  Data Steward - Finance:
    User: finance.steward@company.com
    Scope: Finance domain objects
    Permissions:
      - Approve publications
      - Edit glossary terms
      - Define quality rules

Policies and Compliance

Data Retention Policies

Retention Policy - Transaction Data:
  Category: Financial Transactions
  Retention Period: 7 years
  Archive After: 2 years
  Delete After: 7 years
  Legal Basis: Tax regulations

Retention Policy - Log Data:
  Category: System Logs
  Retention Period: 90 days
  Archive After: 30 days
  Delete After: 90 days

Access Policies

Combine with Data Access Controls:

Access Policy - Regional Data:
  Rule: Users can only access data from their assigned region
  Implementation:
    - Data Access Control: region_access
    - Permission Table: user_region_assignments
    - Criteria Column: REGION_ID

Audit Logging

Track all governance activities:

Best Practices

Governance Implementation

1. Start Small: Begin with critical data domains 2. Executive Sponsorship: Secure leadership support 3. Clear Ownership: Assign accountable owners 4. Automated Monitoring: Don't rely on manual checks 5. Regular Reviews: Audit governance effectiveness quarterly

Catalog Population

1. Prioritize by Usage: Catalog most-used assets first 2. Quality over Quantity: Well-documented few > poorly-documented many 3. Template Descriptions: Create standard description templates 4. Glossary First: Build glossary before linking to assets

User Adoption

1. Training: Educate users on catalog search 2. Quick Wins: Show value with popular datasets 3. Feedback Loop: Collect and act on user feedback 4. Gamification: Recognize top contributors

Integration with SAP Analytics Cloud

Catalog-Enabled Stories

Users can: 1. Search catalog from SAC 2. Preview data before adding 3. See lineage and quality info 4. Add trusted data to stories

Trusted Data Badge

Assets meeting criteria receive "Trusted" badge:

• Published to catalog
• Quality score > 90%
• Complete documentation
• Active owner assigned

SAP Datasphere CLI Reference

Overview

The SAP Datasphere command line interface (datasphere) provides programmatic access to many features available in the UI. This reference covers installation, configuration, and all available commands.

Documentation: https://help.sap.com/docs/SAP_DATASPHERE/d0ecd6f297ac40249072a44df0549c1a

Installation

Prerequisites

• Node.js 16.x or later
• npm 8.x or later
• OAuth 2.0 client credentials (from SAP BTP)

Install CLI

# Install globally
npm install -g @sap/datasphere-cli

# Verify installation
datasphere --version

# Get help
datasphere --help

Authentication

OAuth 2.0 Setup

1. Create Service Instance in SAP BTP Cockpit:

• Service: SAP Datasphere
• Plan: api
• Create service key

2. Extract Credentials from service key:

• clientid
• clientsecret
• url (token URL)

Configure CLI

# Interactive configuration
datasphere configure

# Manual configuration
datasphere configure set tenant-url https://<tenant>.eu10.hcs.cloud.sap
datasphere configure set oauth-client-id <client-id>
datasphere configure set oauth-client-secret <client-secret>
datasphere configure set oauth-token-url https://<tenant>.authentication.eu10.hana.ondemand.com/oauth/token

# Verify configuration
datasphere configure list

Environment Variables

export DATASPHERE_TENANT_URL=https://your-tenant.eu10.hcs.cloud.sap
export DATASPHERE_OAUTH_CLIENT_ID=your-client-id
export DATASPHERE_OAUTH_CLIENT_SECRET=your-client-secret
export DATASPHERE_OAUTH_TOKEN_URL=https://your-tenant.authentication.eu10.hana.ondemand.com/oauth/token

Profiles

# Create profile
datasphere configure --profile production

# Use profile
datasphere spaces list --profile production

# Set default profile
export DATASPHERE_PROFILE=production

Command Reference

datasphere configuration

Manage TLS certificates (DW Administrator role required).

# List certificates
datasphere configuration certificates list

# Upload certificate
datasphere configuration certificates upload --file ./cert.pem

# Delete certificate
datasphere configuration certificates delete --id <cert-id>

datasphere dbusers

Manage database users (DW Space Administrator role required).

# List database users
datasphere dbusers list --space <space-id>

# Create database user
datasphere dbusers create \
  --space <space-id> \
  --name <db-user-name> \
  --enable-read

# Reset password
datasphere dbusers reset-password \
  --space <space-id> \
  --name <db-user-name>

# Delete database user
datasphere dbusers delete \
  --space <space-id> \
  --name <db-user-name>

datasphere global-roles

Manage global roles (DW Administrator role required).

# List global roles
datasphere global-roles list

# Read role details
datasphere global-roles read --role <role-name>

# Add user to role
datasphere global-roles users add \
  --role <role-name> \
  --user <user-email>

# Remove user from role
datasphere global-roles users remove \
  --role <role-name> \
  --user <user-email>

datasphere marketplace

Manage Data Marketplace (DW Modeler role required).

Data Providers

# List providers
datasphere marketplace providers list

# Read provider
datasphere marketplace providers read --id <provider-id>

# Update provider
datasphere marketplace providers update \
  --id <provider-id> \
  --contact-email new-email@company.com

# Batch update
datasphere marketplace providers batch-update \
  --file providers-update.json

Data Products

# List products
datasphere marketplace products list

# Read product
datasphere marketplace products read --id <product-id>

# Create product
datasphere marketplace products create \
  --name "Analytics Package" \
  --provider <provider-id> \
  --visibility internal

# Update product status
datasphere marketplace products update \
  --id <product-id> \
  --status published

# Delete product
datasphere marketplace products delete --id <product-id>

datasphere objects

Manage modeling objects (DW Modeler role required).

# List objects in space
datasphere objects list --space <space-id>

# List by type
datasphere objects list --space <space-id> --type view
datasphere objects list --space <space-id> --type local-table
datasphere objects list --space <space-id> --type remote-table
datasphere objects list --space <space-id> --type data-flow
datasphere objects list --space <space-id> --type replication-flow
datasphere objects list --space <space-id> --type transformation-flow
datasphere objects list --space <space-id> --type analytic-model
datasphere objects list --space <space-id> --type task-chain

# Read object definition (CSN/JSON)
datasphere objects read \
  --space <space-id> \
  --technical-name <object-name> \
  --output json

# Create object
datasphere objects create \
  --space <space-id> \
  --definition-file ./definition.json

# Update object
datasphere objects update \
  --space <space-id> \
  --technical-name <object-name> \
  --definition-file ./definition.json

# Deploy object
datasphere objects deploy \
  --space <space-id> \
  --technical-name <object-name>

# Delete object
datasphere objects delete \
  --space <space-id> \
  --technical-name <object-name>

datasphere scoped-roles

Manage scoped roles (DW Administrator role required).

# List scoped roles
datasphere scoped-roles list

# Read scoped role
datasphere scoped-roles read --role <role-name>

# Create scoped role
datasphere scoped-roles create \
  --name <role-name> \
  --description "Role description"

# Update scoped role
datasphere scoped-roles update \
  --role <role-name> \
  --permissions read,write

# Delete scoped role
datasphere scoped-roles delete --role <role-name>

datasphere spaces

Manage spaces (DW Administrator or DW Space Administrator role).

# List spaces
datasphere spaces list

# Read space
datasphere spaces read --space <space-id>

# Create space (DW Administrator only)
datasphere spaces create \
  --space-id <space-id> \
  --space-name "Space Name" \
  --disk-storage 100 \
  --memory 32

# Update space
datasphere spaces update \
  --space <space-id> \
  --disk-storage 200 \
  --priority 2

# Delete space
datasphere spaces delete --space <space-id>

# Manage space users
datasphere spaces users list --space <space-id>
datasphere spaces users add --space <space-id> --user <email> --role "DW Modeler"
datasphere spaces users remove --space <space-id> --user <email>

# Manage HDI containers
datasphere spaces hdi-containers list --space <space-id>
datasphere spaces hdi-containers add --space <space-id> --container <hdi-name>

datasphere workload

Manage workload priorities (DW Administrator role required).

# Get space priorities
datasphere workload priorities list

# Set space priority
datasphere workload priorities set \
  --space <space-id> \
  --priority 2

# Set statement limits
datasphere workload statement-limits set \
  --space <space-id> \
  --max-memory 8GB \
  --max-time 3600

datasphere tasks

Manage tasks and task chains (DW Integrator role required).

# List tasks
datasphere tasks list --space <space-id>

# List task chains
datasphere tasks list --space <space-id> --type task-chain

# Run task
datasphere tasks run \
  --space <space-id> \
  --task-name <task-name>

# Run task chain
datasphere tasks run \
  --space <space-id> \
  --task-chain <chain-name>

# Run with parameters
datasphere tasks run \
  --space <space-id> \
  --task-chain <chain-name> \
  --parameters '{"param1": "value1"}'

# Get task status
datasphere tasks status \
  --space <space-id> \
  --run-id <run-id>

# Cancel task
datasphere tasks cancel \
  --space <space-id> \
  --run-id <run-id>

# Manage schedules
datasphere tasks schedules list --space <space-id>
datasphere tasks schedules create \
  --space <space-id> \
  --task-chain <chain-name> \
  --cron "0 6 * * *" \
  --timezone "Europe/Berlin"

datasphere users

Manage users (DW Administrator role required).

# List users
datasphere users list

# Read user
datasphere users read --user <user-email>

# Create user
datasphere users create \
  --user <user-email> \
  --first-name "John" \
  --last-name "Doe"

# Update user
datasphere users update \
  --user <user-email> \
  --first-name "Jonathan"

# Delete user
datasphere users delete --user <user-email>

Output Formats

# Table format (default)
datasphere spaces list

# JSON format
datasphere spaces list --output json

# YAML format
datasphere spaces list --output yaml

# Quiet mode (minimal output)
datasphere spaces create ... --quiet

# Verbose mode (debug)
datasphere spaces list --verbose

Error Handling

Common Errors

Retry Logic

# Built-in retry
datasphere spaces list --retry 3 --retry-delay 5

CI/CD Integration

GitHub Actions

name: Deploy to Datasphere
on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: '18'
      - run: npm install -g @sap/datasphere-cli
      - run: |
          datasphere configure set tenant-url ${{ secrets.DS_TENANT_URL }}
          datasphere configure set oauth-client-id ${{ secrets.DS_CLIENT_ID }}
          datasphere configure set oauth-client-secret ${{ secrets.DS_CLIENT_SECRET }}
          datasphere configure set oauth-token-url ${{ secrets.DS_TOKEN_URL }}
      - run: datasphere objects import --space PROD --input-file ./export/package.zip

GitLab CI

deploy:
  image: node:18
  script:
    - npm install -g @sap/datasphere-cli
    - datasphere configure set tenant-url $DS_TENANT_URL
    - datasphere configure set oauth-client-id $DS_CLIENT_ID
    - datasphere configure set oauth-client-secret $DS_CLIENT_SECRET
    - datasphere objects import --space PROD --input-file ./export/package.zip

Best Practices

1. Use Profiles: Create separate profiles for dev/test/prod environments 2. Secure Credentials: Use environment variables or secret managers, never commit credentials 3. Idempotent Scripts: Design scripts to be safely re-runnable 4. Error Handling: Check exit codes and handle failures gracefully 5. Logging: Use --verbose for troubleshooting, --quiet for automation 6. Rate Limiting: Add delays between batch operations to avoid throttling

Connectivity Reference

Source: https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Administering/Preparing-Connectivity Source: https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Integrating-data-and-managing-spaces/Integrating-Data-Via-Connections

---

Table of Contents

1. Connection Overview 2. SAP System Connections 3. Cloud Platform Connections 4. Database Connections 5. Streaming Connections 6. Generic Connections 7. Connection Management 8. Cloud Connector 9. Data Provisioning Agent 10. IP Allowlisting

---

Connection Overview

Connection Types

SAP Datasphere supports 40+ connection types for data integration.

Connection Features

Complete Connection Feature Matrix

*Push = Data pushed via database user SQL Interface

Creating Connections

1. Connections > Create 2. Select connection type 3. Configure properties 4. Test connection 5. Save

Connection Properties

Common Properties:

• Connection Name
• Description
• Technical User
• Authentication Method

---

SAP System Connections

SAP S/4HANA Cloud

Communication Arrangement Scenarios:

Important: The same communication user must be added to all communication arrangements used for the connection.

Prerequisites by Feature:

Remote Tables (Recommended):

• ABAP SQL service exposure for federated CDS view access
• Or: Data Provisioning Agent with CloudDataIntegrationAdapter + SAP_COM_0531
• CDS views must be extraction-enabled and released (annotated with @Analytics.dataExtraction.enabled: true)

Data Flows:

• Communication arrangement for SAP_COM_0532
• CDS views must be released for extraction

Replication Flows:

• Cloud Connector configured (acts as secure tunnel to S/4HANA Cloud)
• ABAP SQL service exposure (recommended)
• Communication arrangement for SAP_COM_0532
• CDS views must be extraction-enabled and released
• Optional: RFC fast serialization (SAP Note 3486245)
• See SAP Note 3297105 for replication-specific requirements

Model Import:

• Data Provisioning Agent with CloudDataIntegrationAdapter
• Communication arrangements: SAP_COM_0532, SAP_COM_0531, SAP_COM_0722

Authorization Requirements:

• Users/services need proper authorizations to expose CDS views
• Communication user requires roles for OData/CDS metadata extraction
• Some CDS views may require SAP Notes to unblock discovery (check view-specific notes)

Authentication Options:

X.509 Certificate Setup for Principal Propagation: 1. Generate certificate using OpenSSL or SAP Cloud Identity Services 2. Upload certificate to communication user in S/4HANA Cloud 3. Configure Cloud Connector for principal propagation (if applicable) 4. Add user to communication system with "SSL Client Certificate" authentication 5. Create required communication arrangements 6. Test connection with actual user to verify propagation

Connection Properties:

type: SAP S/4HANA Cloud
host: mycompany.s4hana.ondemand.com
authentication: OAuth 2.0
client_id: xxx
client_secret: xxx

SAP S/4HANA On-Premise

Prerequisites:

• Cloud Connector configured
• RFC user with authorization
• Network connectivity

Authentication:

• Basic (user/password)
• X.509 certificate

Supported Features:

• Remote tables (CDS views, tables)
• Replication flows (SLT, ODP)
• Real-time replication
• ABAP RFC streaming

Connection Properties:

type: SAP S/4HANA On-Premise
cloud_connector: my_cloud_connector
virtual_host: s4hana.internal:443
system_id: S4H
client: 100
authentication: Basic

SAP BW/4HANA Model Transfer

Prerequisites:

• BW/4HANA 2.0+
• Remote connection configured in BW
• Authorization for model transfer

Supported Objects:

• CompositeProviders
• InfoObjects
• Queries
• Hierarchies

Connection Properties:

type: SAP BW/4HANA Model Transfer
host: bw4hana.company.com
system_id: BW4
client: 100

SAP BW Bridge

Prerequisites:

• BW Bridge provisioned
• Network connectivity

Supported Features:

• Run BW process chains
• Access BW objects
• Hybrid scenarios

SAP ECC

Prerequisites:

• Cloud Connector
• RFC user
• ODP extractors

Connection Properties:

type: SAP ECC
cloud_connector: my_cc
virtual_host: ecc.internal
system_id: ECC
client: 100

SAP HANA (Cloud and On-Premise)

SAP HANA Cloud:

type: SAP HANA Cloud
host: xxx.hana.trial-us10.hanacloud.ondemand.com
port: 443
authentication: User/Password

SAP HANA On-Premise:

type: SAP HANA
cloud_connector: my_cc
virtual_host: hana.internal
port: 30015
authentication: User/Password

SAP HANA Cloud Data Lake

Files Connection:

type: SAP HANA Cloud, Data Lake Files
host: xxx.files.hdl.trial-us10.hanacloud.ondemand.com
container: my_container

Relational Engine:

type: SAP HANA Cloud, Data Lake Relational Engine
host: xxx.iq.hdl.trial-us10.hanacloud.ondemand.com
port: 443

SAP SuccessFactors

Prerequisites:

• OData API enabled
• API user with permissions

Connection Properties:

type: SAP SuccessFactors
host: api.successfactors.com
company_id: mycompany
authentication: Basic

SAP Fieldglass

Connection Properties:

type: SAP Fieldglass
host: api.fieldglass.net
authentication: OAuth 2.0

SAP Marketing Cloud

Connection Properties:

type: SAP Marketing Cloud
host: mycompany.marketing.cloud.sap
authentication: OAuth 2.0

SAP Signavio

Connection Properties:

type: SAP Signavio
host: editor.signavio.com
authentication: API Key

---

Cloud Platform Connections

Amazon Web Services

Amazon S3:

type: Amazon Simple Storage Service
region: us-east-1
bucket: my-data-bucket
authentication: Access Key
access_key_id: AKIA...
secret_access_key: xxx

Amazon Athena:

type: Amazon Athena
region: us-east-1
workgroup: primary
s3_output_location: s3://query-results/
authentication: Access Key

Amazon Redshift:

type: Amazon Redshift
host: cluster.xxx.redshift.amazonaws.com
port: 5439
database: mydb
authentication: User/Password

Google Cloud Platform

Google Cloud Storage:

type: Google Cloud Storage
project_id: my-project
bucket: my-bucket
authentication: Service Account
service_account_key: {...}

Google BigQuery:

type: Google BigQuery
project_id: my-project
dataset: my_dataset
authentication: Service Account

Microsoft Azure

Azure Blob Storage:

type: Microsoft Azure Blob Storage
account_name: mystorageaccount
container: mycontainer
authentication: Account Key

Azure Data Lake Gen2:

type: Microsoft Azure Data Lake Store Gen2
account_name: mydatalake
filesystem: myfilesystem
authentication: Service Principal

Azure SQL Database:

type: Microsoft Azure SQL Database
server: myserver.database.windows.net
database: mydb
authentication: SQL Authentication

Microsoft OneLake:

type: Microsoft OneLake
workspace: my-workspace
lakehouse: my-lakehouse
authentication: Service Principal

---

Database Connections

Oracle

Prerequisites:

• Data Provisioning Agent
• Oracle JDBC driver

Connection Properties:

type: Oracle
host: oracle.company.com
port: 1521
service_name: ORCL
authentication: User/Password

Microsoft SQL Server

Prerequisites:

• Data Provisioning Agent
• JDBC driver

Connection Properties:

type: Microsoft SQL Server
host: sqlserver.company.com
port: 1433
database: mydb
authentication: SQL Server Authentication

Generic JDBC

Prerequisites:

• Data Provisioning Agent
• JDBC driver uploaded

Connection Properties:

type: Generic JDBC
jdbc_url: jdbc:postgresql://host:5432/db
driver_class: org.postgresql.Driver
authentication: User/Password

---

Streaming Connections

Apache Kafka

Prerequisites:

• Kafka cluster accessible
• SSL certificates (if TLS)

Connection Properties:

type: Apache Kafka
bootstrap_servers: kafka1:9092,kafka2:9092
security_protocol: SASL_SSL
sasl_mechanism: PLAIN

Confluent

Connection Properties:

type: Confluent
bootstrap_servers: xxx.confluent.cloud:9092
cluster_id: xxx
api_key: xxx
api_secret: xxx

---

Generic Connections

Generic OData

Connection Properties:

type: Generic OData
service_url: https://api.example.com/odata/v2
authentication: OAuth 2.0

OData Versions:

• OData V2
• OData V4

Generic HTTP

Connection Properties:

type: Generic HTTP
base_url: https://api.example.com
authentication: Bearer Token

Generic SFTP

Connection Properties:

type: Generic SFTP
host: sftp.example.com
port: 22
authentication: Password or SSH Key

Open Connectors

Prerequisites:

• SAP Open Connectors instance
• Connector configured

Connection Properties:

type: Open Connectors
instance_url: https://api.openconnectors.ext.hanatrial.ondemand.com
organization_secret: xxx
user_secret: xxx
element_token: xxx

---

Connection Management

Editing Connections

1. Connections > Select connection 2. Edit properties 3. Test connection 4. Save changes

Deleting Connections

Prerequisites:

• No dependent objects
• No active replications

1. Connections > Select 2. Delete 3. Confirm

Validating Connections

Validation Checks:

• Network connectivity
• Authentication
• Authorization
• Object access

REST API Management

List Connections:

GET /api/v1/connections
Authorization: Bearer {token}

Create Connection:

POST /api/v1/connections
Content-Type: application/json

{
  "name": "my_connection",
  "type": "SAP_HANA",
  "properties": {...}
}

Pause Real-Time Replication

Per Connection: 1. Select connection 2. Pause real-time replication 3. Resume when ready

---

Cloud Connector

Overview

Cloud Connector enables secure connectivity between SAP BTP and on-premise systems.

Installation

1. Download from SAP Support Portal 2. Install on-premise server 3. Configure initial settings 4. Connect to SAP BTP subaccount

Configuration

System Mapping:

virtual_host: s4hana.internal
virtual_port: 443
internal_host: s4hana.company.local
internal_port: 443
protocol: HTTPS

Access Control:

• URL path restrictions
• HTTP method restrictions
• Principal propagation

Troubleshooting

Common Issues:

---

Data Provisioning Agent

Overview

Data Provisioning Agent enables connectivity to on-premise databases and applications.

Installation

Requirements:

• Java 11+
• 4 GB RAM minimum
• Network access

Installation Steps: 1. Download agent installer 2. Run installation 3. Configure agent properties 4. Register with Datasphere

Agent Configuration

dpagentconfig.ini:

[Framework]
name=dp_agent_01
framework_port=5050

[Datasphere]
tenant_url=https://xxx.hana.ondemand.com

⚠️ Security Note: The dpagentconfig.ini file contains sensitive configuration and credentials. Ensure proper file permissions (chmod 600 on Linux) and keep it out of version control. Consider using environment variables for credentials where supported.

Adapter Registration

Register Adapter: 1. System > Data Provisioning 2. Select agent 3. Add adapter 4. Configure adapter properties

Available Adapters:

• ABAP ODP Adapter
• HANA SDI Adapters
• Database adapters
• File adapters

ODBC Driver Upload

Upload Third-Party Drivers: 1. System > Data Provisioning 2. Select agent 3. Upload ODBC driver 4. Restart agent

Agent Monitoring

Monitor Status:

• Connection status
• Adapter status
• Replication status
• Error logs

---

IP Allowlisting

Obtain IP Addresses

Datasphere Outbound IPs: 1. System > Configuration 2. View IP addresses 3. Add to source system allowlist

Configure Allowlist

In Datasphere: 1. System > Security 2. IP Allowlist 3. Add allowed IP ranges 4. Save

IP Range Format:

192.168.1.0/24
10.0.0.0/8

---

Certificate Management

Managing Certificates

Upload Certificate: 1. System > Security > Certificates 2. Upload certificate file 3. Associate with connection

Certificate Types:

• Server certificates (TLS)
• Client certificates (mutual TLS)
• Root CA certificates

Certificate Expiration

Monitor Expiration:

• System > Security > Certificates
• Check expiration dates
• Renew before expiry

---

Documentation Links

• Connections Overview: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/eb85e15
• SAP S/4HANA: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/a98e5ff
• Cloud Connector: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/f289920
• Data Provisioning Agent: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e87952d

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Last Updated: 2025-11-22

Content Transport Reference

Source: https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Integrating-data-and-managing-spaces/Transporting-Content-Between-Tenants Source: https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Acquiring-Preparing-Modeling-Data/Creating-Finding-Sharing-Objects

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Table of Contents

1. Transport Overview 2. Export Packages 3. Import Content 4. Sharing Destinations 5. CSN/JSON Export 6. Command Line Transport 7. SAP Cloud Transport Management 8. Content Network 9. Object Sharing

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Transport Overview

SAP Datasphere supports multiple methods for moving content between tenants.

Transport Methods

Critical Limitation

"Only object definitions can be transported. Data cannot be transported between SAP Datasphere tenants" — the Transport app handles structure only, not actual data records.

Transportable Objects with Dependency Behavior

Note on Manual Selection: E/R Models and Task Chains require manual selection because they represent complex container objects with multiple potential dependencies. Unlike Analytic Models or Flows that have clear source→target relationships, these objects may reference many unrelated items. Explicit user selection prevents unintended transports of large object graphs.

Non-Transportable Items

• Data (table contents)
• Connection credentials
• User assignments
• Schedules
• Notification recipients (for task chains)

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Export Packages

Creating Packages

1. Transport > Create Package 2. Enter package name 3. Select objects 4. Configure options 5. Create package

Package Configuration

Package Properties:

name: sales_analytics_v1
description: Sales analytics data model
include_dependencies: true

Object Selection

Select Objects:

• Individual selection
• Select with dependencies
• Select by space
• Select by type

Dependency Handling:

• Auto-include dependencies
• Skip existing objects
• Override conflicts

Package Contents

Package Structure:

package/
├── manifest.json
├── objects/
│   ├── tables/
│   ├── views/
│   ├── flows/
│   └── models/
└── metadata/

Export Package

Export Options:

• Download as file
• Share to destination
• SAP Cloud Transport

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Import Content

Import Process

1. Transport > Import 2. Select source (file or destination) 3. Review contents 4. Configure options 5. Execute import

Import Options

Conflict Resolution

Conflict Types:

• Object exists
• Name collision
• Dependency missing
• Version mismatch

Resolution Actions:

• Rename object
• Override existing
• Skip object
• Abort import

Import Validation

Pre-Import Checks:

• Object compatibility
• Dependency availability
• Permission verification
• Space capacity

Post-Import Steps

1. Review imported objects 2. Configure connections 3. Set up schedules 4. Assign permissions 5. Deploy objects

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Sharing Destinations

Overview

Sharing destinations enable direct content transfer between tenants.

Adding Sharing Destinations

1. Transport > Sharing Destinations 2. Add destination 3. Configure connection 4. Test connectivity 5. Save

Destination Configuration

destination:
  name: production_tenant
  url: https://prod.datasphere.cloud.sap
  authentication: OAuth 2.0
  client_id: xxx
  client_secret: xxx

Share to Destination

1. Select package 2. Choose destination 3. Configure options 4. Share

Receive from Destination

1. Transport > Incoming 2. Select package 3. Review contents 4. Import

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CSN/JSON Export

Overview

Export objects in CSN (Core Schema Notation) JSON format for version control and CI/CD.

Exporting to CSN/JSON

1. Select objects 2. Export > CSN/JSON 3. Download file

CSN File Structure

{
  "definitions": {
    "space.view_name": {
      "kind": "entity",
      "@EndUserText.label": "View Label",
      "elements": {
        "column1": {
          "type": "cds.String",
          "length": 100
        }
      }
    }
  }
}

Importing from CSN/JSON

1. Transport > Import 2. Select CSN/JSON file 3. Map to space 4. Import

Use Cases

• Version control (Git)
• CI/CD pipelines
• Backup/restore
• Cross-environment deployment

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Command Line Transport

Overview

Use the datasphere CLI for automated transport operations.

Installation

npm install -g @sap/datasphere-cli

Authentication

# Login
datasphere login --url https://tenant.datasphere.cloud.sap

# Using service key
datasphere login --service-key key.json

Export Commands

# Export space definitions
datasphere spaces read --space SALES_ANALYTICS --output export.json

# Export specific objects
datasphere spaces read --space SALES_ANALYTICS --definitions VIEW:sales_view,TABLE:customers --output export.json

# Export with verbose output
datasphere spaces read --space SALES_ANALYTICS --output export.json --verbose

Import Commands

# Import/create space from file (target determined by file content)
datasphere spaces create --file-path export.json

# Import with verbose output
datasphere spaces create --file-path export.json --verbose

Note: The target space is determined by the content of the JSON file. Use the Transport app UI for more granular control over target space mapping.

CI/CD Integration

GitHub Actions Example:

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Install CLI
        run: npm install -g @sap/datasphere-cli
      - name: Login
        run: datasphere login --service-key ${{ secrets.DS_SERVICE_KEY }}
      - name: Import
        run: datasphere spaces create --file-path models/export.json

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SAP Cloud Transport Management

Overview

Integrate with SAP Cloud Transport Management for enterprise transport pipelines.

Prerequisites

• SAP Cloud Transport Management subscription
• Transport routes configured
• Datasphere integration enabled

Configuration

1. System > Transport Management 2. Enable integration 3. Configure transport nodes 4. Set up routes

Transport Landscape

Development → Quality → Production
     ↓           ↓           ↓
  DEV Node   QA Node    PROD Node

Creating Transport Requests

1. Transport > Create Request 2. Select objects 3. Assign to route 4. Submit

Transport Actions

Monitoring Transports

1. Transport Management cockpit 2. View transport queue 3. Check status 4. Review logs

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Content Network

Overview

Access SAP and partner business content from the Content Network.

Accessing Content Network

1. Content Network app 2. Browse available content 3. Select packages 4. Install

Available Content

SAP Content:

• Best practice data models
• Industry solutions
• Analytics content
• Integration packages

Partner Content:

• Third-party connectors
• Industry extensions
• Custom solutions

Installing Content

1. Select content package 2. Review dependencies 3. Configure target space 4. Install

Managing Installed Content

Update Content:

• Check for updates
• Review changes
• Apply updates

Remove Content:

• Identify dependencies
• Remove objects
• Clean up

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Object Sharing

Sharing Within Tenant

Share to Other Spaces: 1. Select object 2. Share > Select spaces 3. Configure permissions 4. Confirm

Share Permissions:

Sharing Entities and Task Chains

Share Entity: 1. Open entity 2. Sharing settings 3. Add spaces 4. Set permissions

Share Task Chain: 1. Open task chain 2. Share to spaces 3. Configure execution permissions

Working in Spaces

Space Isolation:

• Objects belong to one space
• Share for cross-space access
• Permissions cascade

Repository Explorer

Find Objects: 1. Repository Explorer 2. Search/browse 3. View details 4. Access object

Object Actions:

• Open
• Copy
• Share
• Delete

Folders

Organize with Folders: 1. Create folder structure 2. Move objects 3. Set folder permissions

Folder Structure:

Space/
├── Sales/
│   ├── Views/
│   └── Models/
├── Finance/
│   ├── Reports/
│   └── Flows/
└── Shared/

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Managing Exported Content

View Exported Packages

1. Transport > Exported Packages 2. View package list 3. Check status 4. Download/delete

Package Lifecycle

Cleanup

Delete Old Packages:

• Review retention policy
• Delete unused packages
• Archive important versions

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Best Practices

Transport Strategy

1. Define transport landscape 2. Establish naming conventions 3. Document dependencies 4. Test before production

Version Control

• Use meaningful package names
• Include version numbers
• Maintain changelog
• Tag releases

Testing

• Validate in QA first
• Check data access controls
• Verify connections
• Test schedules

Documentation

• Document transport contents
• Record configuration changes
• Note manual steps
• Update runbooks

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Documentation Links

• Transport Overview: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/df12666
• Export Packages: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/24aba84
• Import Content: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/b607a12
• CSN/JSON: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/f8ff062
• CLI: https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/6494657

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Last Updated: 2025-11-22

SAP Datasphere Data Marketplace

Overview

The SAP Datasphere Data Marketplace enables organizations to share, discover, and consume data products across the enterprise and with external partners. Data providers can create curated data packages while consumers can easily find and acquire data for their needs.

Documentation: https://help.sap.com/docs/SAP_DATASPHERE/e4059f908d16406492956e5dbcf142dc

Key Concepts

Data Products

A data product is a curated, documented, and governed package of data assets designed for a specific use case.

Data Product Components:
  Core Assets:
    - Views (facts, dimensions)
    - Analytic Models
    - Business Entities

  Documentation:
    - Description and purpose
    - Sample queries
    - Usage guidelines
    - Data dictionary

  Governance:
    - Owner information
    - Quality score
    - Freshness guarantee
    - Terms of use

Data Providers

Organizations or teams that create and publish data products.

Provider Profile:
  Organization: Finance Data Team
  Contact: finance-data@company.com
  Visibility: Internal | Partner | Public
  Products: 12 published
  Rating: 4.5/5 stars

Data Consumers

Users who discover and request access to data products.

Creating Data Products

Step 1: Plan the Data Product

Planning Checklist:
  Purpose:
    - What business problem does this solve?
    - Who are the target consumers?
    - What questions can it answer?

  Contents:
    - Which views/models to include?
    - What granularity (summary vs detail)?
    - Time range and refresh frequency?

  Quality:
    - Data quality requirements met?
    - Documentation complete?
    - Sample queries prepared?

Step 2: Prepare Data Assets

1. Create Views/Models in Data Builder 2. Set Semantic Usage (Fact, Dimension, Analytic Model) 3. Configure Associations between entities 4. Enable Exposure for consumption 5. Document each object thoroughly

Step 3: Create Data Product

1. Navigate to Data Products in Datasphere 2. Click Create Data Product 3. Configure:

Data Product Configuration:
  Basic Information:
    Name: Sales Analytics Package
    Description: >
      Comprehensive sales data package including transactions,
      customer master data, and product information. Supports
      regional and product-level analysis.
    Category: Sales & Marketing

  Content:
    Space: PROD_ANALYTICS
    Objects:
      - fact_sales (Fact)
      - dim_customer (Dimension)
      - dim_product (Dimension)
      - dim_time (Dimension)
      - am_sales_analysis (Analytic Model)

  Terms:
    Use Case: Internal analytics and reporting
    Restrictions: No external sharing without approval
    SLA: Data refreshed daily by 6:00 AM CET

Step 4: Add Documentation

## Sales Analytics Package

### Overview
This data product provides comprehensive sales transaction data
combined with customer and product master data for analytics.

### Use Cases
- Regional sales performance analysis
- Customer segmentation
- Product mix optimization
- Revenue forecasting

### Data Model

fact_sales (grain: order line item) ├── dim_customer (via CUSTOMER_ID) ├── dim_product (via PRODUCT_ID) └── dim_time (via ORDER_DATE)

### Key Measures
| Measure | Description | Aggregation |
|---------|-------------|-------------|
| Revenue | Net sales amount | SUM |
| Quantity | Units sold | SUM |
| Margin | Revenue - Cost | SUM |
| Avg Order Value | Revenue / Orders | AVG |

### Sample Query

SELECT p.CATEGORY, SUM(s.REVENUE) as Total_Revenue FROM fact_sales s JOIN dim_product p ON s.PRODUCT_ID = p.PRODUCT_ID GROUP BY p.CATEGORY ORDER BY Total_Revenue DESC

### Refresh Schedule
- Full Refresh: Daily at 2:00 AM CET
- Data Latency: T-1 (previous day)

Step 5: Set Visibility and Access

Visibility Options:
  Internal:
    - Available to all tenant users
    - No approval required

  Specific Users:
    - Available to selected users/teams
    - Requires explicit access grant

  Partner:
    - Available to trusted partners
    - Via Data Sharing Destination

  External:
    - Listed on public marketplace
    - Commercial terms apply

Step 6: Publish

1. Review data product configuration 2. Preview as consumer would see it 3. Click Publish 4. Product appears in Marketplace

Data Provider Management

Creating a Data Provider

1. Navigate to Data Marketplace > Providers 2. Click Create Provider 3. Configure:

Provider Configuration:
  Name: Enterprise Analytics Team
  Description: Central analytics data provider
  Contact Email: analytics@company.com
  Website: https://wiki.company.com/analytics

  Visibility: Internal
  Logo: Upload company/team logo

  Terms:
    Default Terms of Use: Link to wiki
    Support Contact: analytics-support@company.com

Managing Products

# CLI: List provider's products
datasphere marketplace products list --provider <provider-id>

# CLI: Update product
datasphere marketplace products update \
  --id <product-id> \
  --status published

# CLI: Batch update contact info
datasphere marketplace providers update \
  --id <provider-id> \
  --contact-email new-email@company.com

Consuming Data Products

Discovering Products

1. Navigate to Data Marketplace 2. Use search and filters:

• By category (Finance, Sales, HR, etc.)
• By provider
• By rating
• By data freshness

3. View product details and previews

Requesting Access

1. Select data product 2. Click Request Access 3. Provide justification:

Access Request:
  Requestor: analyst@company.com
  Data Product: Sales Analytics Package
  Justification: >
    Need sales data for Q4 planning analysis.
    Will use in SAC story for leadership review.
  Duration: Permanent | 6 months | 3 months | 1 month
  Space: ANALYTICS_TEAM

Approval Workflow

Request → Owner Review → Approve/Reject → Access Granted

Approvers can:

• View requestor details
• Check justification
• Set access duration
• Add conditions

Accessing Approved Products

Once approved:

1. Direct Query: Products appear in your space as shared views 2. Import to SAC: Available in SAC model creation 3. Use in Views: Reference in your own views

Data Sharing Destinations

Share data products across tenants:

Configure Sharing Destination

1. Go to System > Administration > Data Sharing 2. Click Create Destination 3. Configure:

Sharing Destination:
  Name: Partner Analytics
  Target Tenant: partner-tenant.eu10.hcs.cloud.sap
  Authentication: OAuth 2.0
  Objects Shared:
    - Sales Summary (aggregated, no PII)
    - Product Catalog

Share Products

1. Edit data product 2. Add sharing destination 3. Configure permissions:

• Read-only access
• Specific objects only
• Data filters applied

Pricing and Monetization

For commercial data sharing:

Pricing Models

Configuring Pricing

Product Pricing:
  Model: Subscription
  Plans:
    Basic:
      Price: $500/month
      Includes: Summary views only
      Limit: 10,000 queries/month

    Professional:
      Price: $2,000/month
      Includes: All views and models
      Limit: 100,000 queries/month

    Enterprise:
      Price: Custom
      Includes: Full access + API
      Limit: Unlimited

Governance and Compliance

Data Product Standards

Establish standards for publication:

Publication Requirements:
  Documentation:
    - Description ≥ 100 characters
    - Sample queries included
    - Refresh schedule documented

  Quality:
    - Quality score ≥ 85%
    - No critical quality issues

  Governance:
    - Owner assigned
    - Classification set
    - Terms of use defined

  Technical:
    - All objects deployed
    - Associations configured
    - Tested with sample queries

Audit Trail

Track all marketplace activities:

Best Practices

For Providers

1. Clear Naming: Use business-friendly product names 2. Complete Documentation: Assume consumers have no context 3. Quality First: Don't publish low-quality data 4. Active Support: Respond to consumer questions 5. Version Control: Document changes between versions

For Consumers

1. Check Quality: Review quality scores before use 2. Understand Lineage: Know where data comes from 3. Respect Terms: Follow usage restrictions 4. Provide Feedback: Rate and review products 5. Report Issues: Flag data quality problems

Marketplace Governance

1. Review Process: Require approval before publication 2. Quality Gates: Automate quality checks 3. Regular Audits: Review access and usage 4. Sunset Policy: Deprecate unused products 5. Feedback Integration: Act on consumer feedback