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
• Inspecting authenticated Datasphere browser UI state in Microsoft Edge when MCP, CLI, logs, or exported artifacts cannot observe the needed state

For Datasphere browser triage, use the shared sap-browser-automation skill for manual in-app authentication, consent-gated authenticated Edge profile copying, fresh loopback CDP startup, target selection, and recovery. The local references/edge-cdp-control.md add-on retains Datasphere-specific boundaries. Use CDP for local read-only inspection of Data Builder, Business Builder, monitoring, connection, catalog, and admin UI state; ask before saving, deploying, deleting, importing, exporting, running task chains, or changing users/roles.

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

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)

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