sap-datasphere

What this skill does

# SAP Datasphere Skill

## Related Skills

- **dependency-upgrade**: Use for secure dependency policy and lockfile hygiene when managing large connector or integration projects with package-managed tooling

## Table of Contents

- [Overview](#overview)
- [Quick Reference](#quick-reference)
  - [Core Components](#core-components)
  - [Object Types](#object-types)
- [Data Builder](#data-builder)
  - [Graphical Views](#graphical-views)
  - [SQL Views](#sql-views)
  - [Tables](#tables)
  - [Flows](#flows)
  - [Task Chains](#task-chains)
- [Business Builder](#business-builder)
- [Analytic Models](#analytic-models)
- [Connections](#connections)
- [Space Management](#space-management)
- [Data Access Control](#data-access-control)
- [Monitoring](#monitoring)
- [CLI Reference](#cli-reference)
- [Data Products & Marketplace](#data-products--marketplace)
- [Catalog & Governance](#catalog--governance)
- [Content Transport](#content-transport)
- [Common Issues](#common-issues)
- [Bundled Resources](#bundled-resources)
- [Documentation Links](#documentation-links)

## Overview

SAP Datasphere is SAP's cloud-native data warehouse solution on SAP Business Technology Platform (BTP). This skill provides comprehensive guidance for data acquisition, preparation, modeling, administration, and integration.

**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

---

## Quick Reference

### Core Components

| Component | Purpose | Key Objects |
|-----------|---------|-------------|
| **Data Builder** | Data acquisition & preparation | Views, Tables, Flows, Task Chains |
| **Business Builder** | Semantic layer modeling | Business Entities, Fact Models, Consumption Models |
| **Analytic Model** | Analytics-ready structures | Dimensions, Facts, Measures, Hierarchies |
| **Connections** | External data sources | 40+ connection types |
| **Spaces** | Logical data containers | Storage, Users, Objects |

### 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.

```sql
-- 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

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`.

---

## 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