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# Administration Reference
**Source**: [https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Administering](https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Administering)
---
## Table of Contents
1. [Tenant Configuration](#tenant-configuration)
2. [Spaces and Storage](#spaces-and-storage)
3. [Users and Roles](#users-and-roles)
4. [Identity and Authentication](#identity-and-authentication)
5. [Monitoring](#monitoring)
6. [Elastic Compute Nodes](#elastic-compute-nodes)
7. [Data Provisioning Agent](#data-provisioning-agent)
8. [System Maintenance](#system-maintenance)
---
## 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**:
| Plan | Description |
|------|-------------|
| Free | Trial with limitations |
| Standard | Production use |
### Configuring Tenant Size
**Capacity Parameters**:
- Storage (GB)
- In-memory (GB)
- Compute units
**Sizing Recommendations**:
| Use Case | Storage | Memory |
|----------|---------|--------|
| Small | 256 GB | 32 GB |
| Medium | 1 TB | 128 GB |
| Large | 4 TB+ | 512 GB+ |
### 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**:
| Type | Purpose |
|------|---------|
| Technical User | System-to-system integration |
| API Access | REST API calls |
| Interactive Usage | User authentication |
**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**:
```json
{
"clientid": "sb-xxx",
"clientsecret": "xxx",
"url": "[https://xxx.authentication.xxx.hana.ondemand.com",](https://xxx.authentication.xxx.hana.ondemand.com",)
"apiurl": "[https://xxx.hana.ondemand.com"](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**:
| Property | Specifications |
|----------|----------------|
| Space Name | Maximum 30 characters; allows spaces and special characters |
| Space ID | Maximum 20 UPPERCASE letters/numbers; underscores only |
| Storage Type | SAP HANA Database (Disk and In-Memory) |
**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**:
| Setting | Description |
|---------|-------------|
| Data Access | Exposure for consumption defaults |
| Database User | Create for external tool connections |
| HDI Container | Associate HDI container |
| Time Data | Generate standardized time tables/dimensions |
| Auditing | Enable read/change action logging |
**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**:
| Type | Use | Performance |
|------|-----|-------------|
| Disk | Persistent data | Standard |
| In-Memory | Hot data | High |
| Object Store | Large files | Cost-effective |
### 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**:
```bash
# 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**:
| Property | Description |
|----------|-------------|
| Email | Login identifier |
| First Name | Display name |
| Last Name | Display name |
| Manager | Reporting structure |
### Role Types
**Global Roles**:
- Apply across all spaces
- System-level permissions
**Scoped Roles**:
- Space-specific permissions
- Object-level access
### Standard Roles
| Role | Description |
|------|-------------|
| DW Administrator | Full system access |
| DW Space Administrator | Space management |
| DW Integrator | Data integration |
| DW Modeler | Data modeling |
| DW Viewer | Read-only access |
### 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**:
```http
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**:
| Attribute | Purpose |
|-----------|---------|
| email | User identification |
| firstName | Display name |
| lastName | Display name |
| groups | Role assignment |
### 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**:
| Property | Description |
|----------|-------------|
| Start date/time | When task started |
| Object name/type | Object being processed |
| Space name | Space containing the object |
| Storage type | SAP HANA Database or Data Lake Files |
| Activity type | persist, replicate, execute |
| Status/substatus | Completion status with failure descriptions |
| SAP HANA Peak Memory (MiB) | Requires expensive statement tracing |
| SAP HANA Used Memory (MiB) | Memory consumption |
| SAP HANA Used CPU Time (ms) | Requires expensive statement tracing |
| SAP HANA Used Disk (MiB) | Disk consumption |
| Apache Spark Peak Memory | Peak memory for Spark tasks |
| Apache Spark Spill to Disk | Data spilled to disk |
| Apache Spark Used Cores | Number of cores used |
| Records count | Only for: views (persist), remote tables (replicate), data flows, intelligent lookups |
**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**:
```sql
-- Find running statements
SELECT * FROM M_ACTIVE_STATEMENTS;
-- Cancel statement
ALTER SYSTEM CANCEL SESSION 'connection_id';
```
### SAP HANA Monitoring Views
**System Views**:
| View | Purpose |
|------|---------|
| M_ACTIVE_STATEMENTS | Running queries |
| M_CONNECTIONS | Active connections |
| M_SERVICE_MEMORY | Memory usage |
| M_VOLUME_IO | I/O statistics |
### 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
| Parameter | Description |
|-----------|-------------|
| Node Name | Identifier |
| Capacity | Processing units |
| Warm-up Time | Pre-start minutes |
| Auto-shutdown | Idle timeout |
### 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**:
```properties
# Connection settings
datasphere.tenant.url=[https://xxx.hana.ondemand.com](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](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/2f80b57)
- **Space Management**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/2ace657](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/2ace657)
- **User Management**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/4fb82cb](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/4fb82cb)
- **Monitoring**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/28910cd](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/28910cd)
---
**Last Updated**: 2025-11-22

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# Connectivity Reference
**Source**: [https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Administering/Preparing-Connectivity](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](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](#connection-overview)
2. [SAP System Connections](#sap-system-connections)
3. [Cloud Platform Connections](#cloud-platform-connections)
4. [Database Connections](#database-connections)
5. [Streaming Connections](#streaming-connections)
6. [Generic Connections](#generic-connections)
7. [Connection Management](#connection-management)
8. [Cloud Connector](#cloud-connector)
9. [Data Provisioning Agent](#data-provisioning-agent)
10. [IP Allowlisting](#ip-allowlisting)
---
## Connection Overview
### Connection Types
SAP Datasphere supports 40+ connection types for data integration.
| Category | Connections |
|----------|-------------|
| SAP | S/4HANA, BW/4HANA, ECC, HANA, SuccessFactors |
| Cloud | AWS, Azure, GCP |
| Database | Oracle, SQL Server, JDBC |
| Streaming | Kafka, Confluent |
| Generic | OData, HTTP, SFTP, JDBC |
### Connection Features
| Feature | Description |
|---------|-------------|
| Remote Tables | Virtual data access |
| Data Flows | ETL pipelines |
| Replication Flows | Data replication |
| Model Import | BW model transfer |
### Complete Connection Feature Matrix
| Connection Type | Remote Tables | Replication Flows | Data Flows | Model Import |
|-----------------|---------------|-------------------|------------|--------------|
| **SAP Systems** |
| SAP S/4HANA Cloud | Yes | Yes (source) | Yes | Yes |
| SAP S/4HANA On-Premise | Yes | Yes (source) | Yes | Yes |
| SAP ABAP | Yes | Yes (source) | Yes | No |
| SAP BW | Yes | Via ABAP | Yes | No |
| SAP BW/4HANA Model Transfer | No | No | No | Yes |
| SAP BW Bridge | Yes | No | No | Yes |
| SAP ECC | Yes | Via ABAP | Yes | No |
| SAP HANA | Yes | Yes (source+target) | Yes | No |
| SAP HANA Cloud Data Lake Files | No | Yes (source+target) | Yes | No |
| SAP HANA Cloud Data Lake Relational Engine | Yes | No | Yes | No |
| SAP SuccessFactors | Yes | No | Yes | No |
| SAP Fieldglass | Yes | No | Yes | No |
| SAP Marketing Cloud | Yes | No | Yes | No |
| SAP Signavio | No | Yes (target) | No | No |
| **Cloud Platforms** |
| Amazon S3 | No | Yes (source+target) | Yes | No |
| Amazon Athena | Yes | No | No | No |
| Amazon Redshift | Yes | No | Yes | No |
| Google Cloud Storage | No | Yes (source+target) | Yes | No |
| Google BigQuery | Yes | Yes (target) | Yes | No |
| Microsoft Azure Blob Storage | No | No | Yes | No |
| Microsoft Azure Data Lake Gen2 | No | Yes (source+target) | Yes | No |
| Microsoft Azure SQL Database | Yes | Yes (source) | Yes | No |
| Microsoft SQL Server | Yes | Yes (source) | Yes | No |
| Microsoft OneLake | No | Yes (source) | No | No |
| **Databases** |
| Oracle | Yes | No | Yes | No |
| Generic JDBC | Yes | No | No | No |
| **Streaming** |
| Apache Kafka | No | Yes (target) | No | No |
| Confluent | No | Yes (source+target) | No | No |
| **Generic** |
| Generic OData | Yes | No | Yes | No |
| Generic HTTP | No | No | No | No |
| Generic SFTP | No | Yes (source+target) | Yes | No |
| Open Connectors | No | No | Yes | No |
| Hadoop HDFS | No | No | Yes | No |
| Cloud Data Integration | Yes | No | Yes | No |
| **Partner** |
| Adverity | Push* | No | No | No |
| Precog | Push* | No | No | No |
*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**:
| Scenario | Purpose | Required For |
|----------|---------|--------------|
| SAP_COM_0531 | OData Services | Remote tables (legacy) |
| SAP_COM_0532 | CDS View Replication | Data flows, Replication flows |
| SAP_COM_0722 | Model Transfer | BW model import |
**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**:
| Method | Use Case | Notes |
|--------|----------|-------|
| OAuth 2.0 (SAML Bearer Assertion) | Principal propagation/SSO | User identity passed through |
| OAuth 2.0 (Client Credentials) | Service-to-service | Technical user access |
| Basic Authentication | Legacy/simple setups | Not recommended for production |
| X.509 Client Certificate | Principal propagation with Cloud Connector | See SAP Note 2801396 for approved CAs |
**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**:
```yaml
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**:
```yaml
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**:
```yaml
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**:
```yaml
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**:
```yaml
type: SAP HANA Cloud
host: xxx.hana.trial-us10.hanacloud.ondemand.com
port: 443
authentication: User/Password
```
**SAP HANA On-Premise**:
```yaml
type: SAP HANA
cloud_connector: my_cc
virtual_host: hana.internal
port: 30015
authentication: User/Password
```
### SAP HANA Cloud Data Lake
**Files Connection**:
```yaml
type: SAP HANA Cloud, Data Lake Files
host: xxx.files.hdl.trial-us10.hanacloud.ondemand.com
container: my_container
```
**Relational Engine**:
```yaml
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**:
```yaml
type: SAP SuccessFactors
host: api.successfactors.com
company_id: mycompany
authentication: Basic
```
### SAP Fieldglass
**Connection Properties**:
```yaml
type: SAP Fieldglass
host: api.fieldglass.net
authentication: OAuth 2.0
```
### SAP Marketing Cloud
**Connection Properties**:
```yaml
type: SAP Marketing Cloud
host: mycompany.marketing.cloud.sap
authentication: OAuth 2.0
```
### SAP Signavio
**Connection Properties**:
```yaml
type: SAP Signavio
host: editor.signavio.com
authentication: API Key
```
---
## Cloud Platform Connections
### Amazon Web Services
**Amazon S3**:
```yaml
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**:
```yaml
type: Amazon Athena
region: us-east-1
workgroup: primary
s3_output_location: s3://query-results/
authentication: Access Key
```
**Amazon Redshift**:
```yaml
type: Amazon Redshift
host: cluster.xxx.redshift.amazonaws.com
port: 5439
database: mydb
authentication: User/Password
```
### Google Cloud Platform
**Google Cloud Storage**:
```yaml
type: Google Cloud Storage
project_id: my-project
bucket: my-bucket
authentication: Service Account
service_account_key: {...}
```
**Google BigQuery**:
```yaml
type: Google BigQuery
project_id: my-project
dataset: my_dataset
authentication: Service Account
```
### Microsoft Azure
**Azure Blob Storage**:
```yaml
type: Microsoft Azure Blob Storage
account_name: mystorageaccount
container: mycontainer
authentication: Account Key
```
**Azure Data Lake Gen2**:
```yaml
type: Microsoft Azure Data Lake Store Gen2
account_name: mydatalake
filesystem: myfilesystem
authentication: Service Principal
```
**Azure SQL Database**:
```yaml
type: Microsoft Azure SQL Database
server: myserver.database.windows.net
database: mydb
authentication: SQL Authentication
```
**Microsoft OneLake**:
```yaml
type: Microsoft OneLake
workspace: my-workspace
lakehouse: my-lakehouse
authentication: Service Principal
```
---
## Database Connections
### Oracle
**Prerequisites**:
- Data Provisioning Agent
- Oracle JDBC driver
**Connection Properties**:
```yaml
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**:
```yaml
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**:
```yaml
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**:
```yaml
type: Apache Kafka
bootstrap_servers: kafka1:9092,kafka2:9092
security_protocol: SASL_SSL
sasl_mechanism: PLAIN
```
### Confluent
**Connection Properties**:
```yaml
type: Confluent
bootstrap_servers: xxx.confluent.cloud:9092
cluster_id: xxx
api_key: xxx
api_secret: xxx
```
---
## Generic Connections
### Generic OData
**Connection Properties**:
```yaml
type: Generic OData
service_url: [https://api.example.com/odata/v2](https://api.example.com/odata/v2)
authentication: OAuth 2.0
```
**OData Versions**:
- OData V2
- OData V4
### Generic HTTP
**Connection Properties**:
```yaml
type: Generic HTTP
base_url: [https://api.example.com](https://api.example.com)
authentication: Bearer Token
```
### Generic SFTP
**Connection Properties**:
```yaml
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**:
```yaml
type: Open Connectors
instance_url: [https://api.openconnectors.ext.hanatrial.ondemand.com](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**:
```http
GET /api/v1/connections
Authorization: Bearer {token}
```
**Create Connection**:
```http
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**:
```yaml
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**:
| Issue | Solution |
|-------|----------|
| Connection refused | Check firewall rules |
| Authentication failed | Verify credentials |
| Timeout | Check network latency |
| Certificate error | Update certificates |
---
## 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**:
```ini
[Framework]
name=dp_agent_01
framework_port=5050
[Datasphere]
tenant_url=[https://xxx.hana.ondemand.com](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](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/eb85e15)
- **SAP S/4HANA**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/a98e5ff](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/a98e5ff)
- **Cloud Connector**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/f289920](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/f289920)
- **Data Provisioning Agent**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e87952d](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e87952d)
---
**Last Updated**: 2025-11-22

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# Content Transport Reference
**Source**: [https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Integrating-data-and-managing-spaces/Transporting-Content-Between-Tenants](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](https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Acquiring-Preparing-Modeling-Data/Creating-Finding-Sharing-Objects)
---
## Table of Contents
1. [Transport Overview](#transport-overview)
2. [Export Packages](#export-packages)
3. [Import Content](#import-content)
4. [Sharing Destinations](#sharing-destinations)
5. [CSN/JSON Export](#csnjson-export)
6. [Command Line Transport](#command-line-transport)
7. [SAP Cloud Transport Management](#sap-cloud-transport-management)
8. [Content Network](#content-network)
9. [Object Sharing](#object-sharing)
---
## Transport Overview
SAP Datasphere supports multiple methods for moving content between tenants.
### Transport Methods
| Method | Use Case | Complexity |
|--------|----------|------------|
| Export/Import Packages | Manual transport | Low |
| Cloud Transport Management | Automated pipelines | Medium |
| CSN/JSON Files | Developer workflow | Low |
| Command Line | CI/CD integration | Medium |
### 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
| Object Type | Auto-Includes Dependencies | Notes |
|-------------|---------------------------|-------|
| Connections | No | No dependencies on other objects |
| Remote Tables | Yes | Includes connection information |
| Local Tables | No | Structure only; no interdependencies |
| Flows (Data/Replication/Transformation) | Yes | Auto-exports all source and target definitions |
| Views (Graphical/SQL) | Yes | Exports all sources and applied data access controls |
| Intelligent Lookups | Yes | Exports input and lookup entity definitions |
| Analytic Models | Yes | Exports fact and dimension source definitions |
| **E/R Models** | **Manual** | Objects must be manually selected; not auto-included |
| Data Access Controls | Yes | Exports permissions entity definition |
| **Task Chains** | **Manual** | Objects must be manually selected; not auto-included |
| Business Entities/Versions | Yes | Exports all versions, source entities, and authorization scenarios |
| Fact Models | Yes | Exports all versions and dependent source models/entities |
| Consumption Models | Yes | Exports all perspectives and dependent models/entities |
| Authorization Scenarios | Yes | Exports associated data access control |
> **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)
---
## 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**:
```yaml
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
---
## Import Content
### Import Process
1. Transport > Import
2. Select source (file or destination)
3. Review contents
4. Configure options
5. Execute import
### Import Options
| Option | Description |
|--------|-------------|
| Create New | Create all objects |
| Update Existing | Update if exists |
| Skip Existing | Don't overwrite |
| Overwrite | Replace all |
### 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
---
## 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
```yaml
destination:
name: production_tenant
url: [https://prod.datasphere.cloud.sap](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
---
## 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
```json
{
"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
---
## Command Line Transport
### Overview
Use the datasphere CLI for automated transport operations.
### Installation
```bash
npm install -g @sap/datasphere-cli
```
### Authentication
```bash
# Login
datasphere login --url [https://tenant.datasphere.cloud.sap](https://tenant.datasphere.cloud.sap)
# Using service key
datasphere login --service-key key.json
```
### Export Commands
```bash
# 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
```bash
# 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**:
```yaml
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
```
---
## 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
| Action | Description |
|--------|-------------|
| Export | Create transport file |
| Import | Apply to target |
| Forward | Move to next node |
| Release | Approve transport |
### Monitoring Transports
1. Transport Management cockpit
2. View transport queue
3. Check status
4. Review logs
---
## 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
---
## Object Sharing
### Sharing Within Tenant
**Share to Other Spaces**:
1. Select object
2. Share > Select spaces
3. Configure permissions
4. Confirm
**Share Permissions**:
| Permission | Capabilities |
|------------|--------------|
| Read | View, use as source |
| Read/Write | Modify, extend |
| Full | All operations |
### 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/
```
---
## Managing Exported Content
### View Exported Packages
1. Transport > Exported Packages
2. View package list
3. Check status
4. Download/delete
### Package Lifecycle
| Status | Description |
|--------|-------------|
| Draft | Being created |
| Ready | Available for export |
| Exported | Downloaded/shared |
| Archived | Retained for history |
### Cleanup
**Delete Old Packages**:
- Review retention policy
- Delete unused packages
- Archive important versions
---
## 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
---
## Documentation Links
- **Transport Overview**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/df12666](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/df12666)
- **Export Packages**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/24aba84](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/24aba84)
- **Import Content**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/b607a12](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/b607a12)
- **CSN/JSON**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/f8ff062](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/f8ff062)
- **CLI**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/6494657](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/6494657)
---
**Last Updated**: 2025-11-22

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# Data Access and Security Reference
**Source**: [https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Integrating-data-and-managing-spaces/Data-Access-Control](https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Integrating-data-and-managing-spaces/Data-Access-Control)
---
## Table of Contents
1. [Data Access Controls Overview](#data-access-controls-overview)
2. [Single Values Data Access Control](#single-values-data-access-control)
3. [Operator and Values Data Access Control](#operator-and-values-data-access-control)
4. [Hierarchy Data Access Control](#hierarchy-data-access-control)
5. [Hierarchy with Directory Data Access Control](#hierarchy-with-directory-data-access-control)
6. [Importing BW Analysis Authorizations](#importing-bw-analysis-authorizations)
7. [Applying Data Access Controls](#applying-data-access-controls)
8. [Row-Level Security in Intelligent Applications](#row-level-security-in-intelligent-applications)
9. [Space Access Control](#space-access-control)
10. [Audit Logging](#audit-logging)
---
## Data Access Controls Overview
Data Access Controls (DACs) implement row-level security in SAP Datasphere.
### Purpose
- Restrict data visibility by user
- Implement fine-grained authorization
- Comply with data privacy requirements
- Support multi-tenant scenarios
### DAC Types
| Type | Use Case | Complexity |
|------|----------|------------|
| Single Values | Simple value matching | Low |
| Operator and Values | Complex conditions | Medium |
| Hierarchy | Node-based filtering | Medium |
| Hierarchy with Directory | Complex hierarchical | High |
### Architecture
```
User Request
↓
Data Access Control
↓
Criteria Evaluation
↓
Row Filtering
↓
Result Set
```
### DAC Components
**Criteria**:
- Columns used for filtering
- User attributes for matching
- Operators for comparison
**Permissions Entity**:
- Maps users to allowed values
- User IDs must be in the form required by your identity provider
- Supports wildcards (`*` for all records)
- Hierarchy node references
- **Cannot** be protected by data access controls themselves
- **Cannot** contain protected sources
- Must be encapsulated in views when shared across spaces
### Performance Considerations
| Factor | Recommendation |
|--------|----------------|
| Source table size | Replicate tables exceeding 500,000 rows |
| Permissions per user | Avoid exceeding 5,000 records for Operator/Values controls |
| Wildcard operator | Use `*` for all-records access |
| Persisted views | Views with protected sources **cannot** be persisted |
### Security Enforcement Scope
**Important**: Row-level security can be circumvented while the view remains in its original space.
Security is enforced only when the view is:
1. **Shared to another space**
2. **Consumed outside the space** (e.g., in SAP Analytics Cloud)
Controls filter results in data previews based on current user within the space.
---
## Single Values Data Access Control
### Overview
Simple value-based filtering using exact matches.
### Creating Single Values DAC
1. Data Builder > New Data Access Control
2. Select "Single Values"
3. Define criteria column
4. Configure permissions table
5. Deploy
### Criteria Configuration
**Single Criterion**:
```yaml
criterion: region
column: region_code
```
**Multiple Criteria**:
```yaml
criteria:
- region: region_code
- company: company_code
```
### Permissions Table
**Structure**:
| User | Region | Company |
|------|--------|---------|
| user1@company.com | US | 1000 |
| user1@company.com | EU | 1000 |
| user2@company.com | * | 2000 |
**Wildcard Support**:
- `*` matches all values
- Explicit values for specific access
### Example
**Scenario**: Restrict sales data by region
**DAC Definition**:
```yaml
type: Single Values
criteria:
- name: region
column: sales_region
permissions:
- user: alice@company.com
region: North America
- user: bob@company.com
region: Europe
- user: charlie@company.com
region: "*" # All regions
```
---
## Operator and Values Data Access Control
### Overview
Complex filtering using comparison operators.
### Creating Operator and Values DAC
1. Data Builder > New Data Access Control
2. Select "Operator and Values"
3. Define criteria with operators
4. Configure permissions
5. Deploy
### Supported Operators
| Operator | Symbol | Description |
|----------|--------|-------------|
| Equal | = | Exact match |
| Not Equal | != | Exclude value |
| Less Than | < | Below threshold |
| Greater Than | > | Above threshold |
| Between | BT | Range inclusive |
| Contains Pattern | CP | Pattern match |
### Criteria Configuration
```yaml
criteria:
- name: amount_range
column: order_amount
operators: [=, <, >, BT]
- name: status
column: order_status
operators: [=, !=]
```
### Permissions Table
| User | Criterion | Operator | Value 1 | Value 2 |
|------|-----------|----------|---------|---------|
| user1 | amount | BT | 0 | 10000 |
| user2 | amount | > | 10000 | - |
| user3 | status | != | DRAFT | - |
### Example
**Scenario**: Restrict by amount threshold
**DAC Definition**:
```yaml
type: Operator and Values
criteria:
- name: amount_threshold
column: transaction_amount
permissions:
- user: junior_analyst@company.com
criterion: amount_threshold
operator: "<"
value: 10000
- user: senior_analyst@company.com
criterion: amount_threshold
operator: "*" # All amounts
```
---
## Hierarchy Data Access Control
### Overview
Filter data based on hierarchy node membership.
### Creating Hierarchy DAC
1. Data Builder > New Data Access Control
2. Select "Hierarchy"
3. Reference hierarchy view
4. Configure permissions
5. Deploy
### Hierarchy Configuration
**Hierarchy Reference**:
```yaml
hierarchy:
view: cost_center_hierarchy
node_column: cost_center_id
parent_column: parent_cost_center
```
### Node-Based Permissions
| User | Node | Include Descendants |
|------|------|---------------------|
| user1 | CC1000 | Yes |
| user2 | CC2000 | No |
| user3 | ROOT | Yes |
### Example
**Scenario**: Restrict by organizational hierarchy
**DAC Definition**:
```yaml
type: Hierarchy
hierarchy:
view: org_hierarchy
node: org_unit_id
criteria:
- column: responsible_org_unit
permissions:
- user: manager_a@company.com
node: DEPT_A
descendants: true
- user: manager_b@company.com
node: DEPT_B
descendants: true
```
---
## Hierarchy with Directory Data Access Control
### Overview
Complex hierarchical filtering with directory-based node definitions.
### Creating Hierarchy with Directory DAC
1. Data Builder > New Data Access Control
2. Select "Hierarchy with Directory"
3. Define directory table
4. Configure hierarchy relationship
5. Set permissions
6. Deploy
### Directory Table Structure
**Directory Definition**:
```sql
CREATE TABLE auth_directory (
node_id VARCHAR(50),
node_type VARCHAR(20),
parent_node VARCHAR(50),
level_number INTEGER
)
```
### Configuration
```yaml
type: Hierarchy with Directory
directory:
table: auth_directory
node_column: node_id
parent_column: parent_node
type_column: node_type
criteria:
- column: cost_center
directory_type: COST_CENTER
```
### Permissions
| User | Node ID | Node Type |
|------|---------|-----------|
| user1 | H_1000 | COST_CENTER |
| user2 | H_2000 | PROFIT_CENTER |
---
## Importing BW Analysis Authorizations
### Overview
Import existing SAP BW or BW/4HANA analysis authorizations.
### Prerequisites
- BW connection configured
- Authorization objects available
- User mapping defined
### Import Process
1. Data Builder > New Data Access Control
2. Select "Import from BW"
3. Choose connection
4. Select authorization objects
5. Map to local objects
6. Deploy
### Supported Objects
**BW Authorization Objects**:
- RSECAUTH (Analysis Authorizations)
- InfoObject restrictions
- Hierarchy authorizations
### Mapping Configuration
```yaml
import:
connection: bw4hana_prod
authorization: ZSALES_AUTH
mapping:
- bw_characteristic: 0COMP_CODE
local_column: company_code
- bw_characteristic: 0REGION
local_column: sales_region
```
---
## Applying Data Access Controls
### Apply to Graphical Views
1. Open graphical view
2. View properties > Security
3. Select data access control
4. Map criteria columns
5. Deploy
### Apply to SQL Views
1. Open SQL view
2. View properties > Security
3. Select data access control
4. Map criteria columns
5. Deploy
### Apply to Analytic Models
1. Open analytic model
2. Model properties > Security
3. Select data access control
4. Map to fact/dimension columns
5. Deploy
**Analytic Model Constraint**: Cannot map data access controls to dimensions with:
- Standard variables
- Reference date variables
- X variables
### Criteria Mapping
**Mapping Configuration**:
```yaml
data_access_control: region_dac
mappings:
- dac_criterion: region
view_column: sales_region
- dac_criterion: company
view_column: company_code
```
### Process Source Changes
When source columns change:
1. Open DAC editor
2. Process source changes
3. Update mappings
4. Redeploy
---
## Row-Level Security in Intelligent Applications
### Overview
Apply row-level security to data delivered through intelligent applications.
### Configuration
1. Install intelligent application
2. Configure data access
3. Apply DAC to exposed views
4. Test user access
### Supported Applications
- SAP Analytics Cloud
- Third-party BI tools
- Custom applications
---
## Space Access Control
### Overview
Control user access at the space level.
### Space User Management
**Add Users to Space**:
1. Space > Members
2. Add user
3. Assign role
4. Save
**Space Roles**:
| Role | Permissions |
|------|-------------|
| Space Administrator | Full control |
| Integrator | Data integration |
| Modeler | Create/modify objects |
| Viewer | Read-only access |
### Cross-Space Sharing
**Share Objects**:
1. Select object
2. Share to other spaces
3. Define share permissions
4. Confirm sharing
**Share Permissions**:
- Read: View data
- Read/Write: Modify data
- Full: All operations
---
## Audit Logging
### Overview
Track data access and modifications for compliance.
### Enable Audit Logging
1. Space > Settings
2. Enable audit logging
3. Select audit events
4. Configure retention
### Audited Events
| Event | Description |
|-------|-------------|
| Read | Data access |
| Insert | New records |
| Update | Record changes |
| Delete | Record removal |
### Audit Log Structure
```json
{
"timestamp": "2024-01-15T10:30:00Z",
"user": "analyst@company.com",
"action": "READ",
"object": "sales_data_view",
"rows_affected": 1500,
"filters": "region='US'"
}
```
### Log Retention
**Configure Retention**:
- Set retention period (days)
- Automatic cleanup
- Archive options
### Viewing Audit Logs
1. System > Monitoring
2. Audit Logs
3. Filter by criteria
4. Export if needed
---
## Best Practices
### DAC Design
- Keep criteria simple
- Use hierarchies for complex org structures
- Test with representative users
- Document authorization model
### Performance
- Index criterion columns
- Limit permission table size
- Use wildcards judiciously
- Monitor query performance
### Maintenance
- Regular permission reviews
- User offboarding process
- Audit log monitoring
- Documentation updates
---
## Documentation Links
- **Data Access Controls**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/a032e51](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/a032e51)
- **Single Values DAC**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/5246328](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/5246328)
- **Hierarchy DAC**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/0afeeed](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/0afeeed)
- **Space Access**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/9d59fe5](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/9d59fe5)
---
**Last Updated**: 2025-11-22

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# Data Acquisition and Preparation Reference
**Source**: [https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Acquiring-Preparing-Modeling-Data/Acquiring-and-Preparing-Data-in-the-Data-Builder](https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Acquiring-Preparing-Modeling-Data/Acquiring-and-Preparing-Data-in-the-Data-Builder)
---
## Table of Contents
1. [Data Flows](#data-flows)
2. [Replication Flows](#replication-flows)
3. [Transformation Flows](#transformation-flows)
4. [Local Tables](#local-tables)
5. [Remote Tables](#remote-tables)
6. [Task Chains](#task-chains)
7. [Python Operators](#python-operators)
8. [Data Transformation](#data-transformation)
9. [Semantic Onboarding](#semantic-onboarding)
10. [File Spaces and Object Store](#file-spaces-and-object-store)
---
## Data Flows
Data flows provide ETL capabilities for data transformation and loading.
### Prerequisites
**Required Privileges**:
- Data Warehouse General (`-R------`) - SAP Datasphere access
- Connection (`-R------`) - Read connections
- Data Warehouse Data Builder (`CRUD----`) - Create/edit/delete flows
- Space Files (`CRUD----`) - Manage space objects
- Data Warehouse Data Integration (`-RU-----`) - Run flows
- Data Warehouse Data Integration (`-R--E---`) - Schedule flows
### Creating a Data Flow
1. Navigate to Data Builder
2. Select "New Data Flow"
3. Add source operators
4. Add transformation operators
5. Add target operator
6. Save and deploy
### Key Limitations
- **No delta processing**: Use replication flows for delta/CDC data instead
- **Single target table** only per data flow
- **Local tables only**: Data flows load exclusively to local tables in the repository
- **Double quotes unsupported** in identifiers (column/table names)
- **Spatial data types** not supported
- **ABAP source preview** unavailable (except CDS views and LTR objects)
- **Transformation operators** cannot be previewed
### Advanced Properties
**Dynamic Memory Allocation**:
| Setting | Memory Range | Use Case |
|---------|--------------|----------|
| Small | 1-2 GB | Low volume |
| Medium | 2-3 GB | Standard volume |
| Large | 3-5 GB | High volume |
**Additional Options**:
- Automatic restart on failure
- Input parameters support
### Data Flow Operators
**Source Operators**:
- Remote tables
- Local tables
- Views
- CSV files
**Transformation Operators**:
| Operator | Purpose | Configuration |
|----------|---------|---------------|
| Join | Combine sources | Join type, conditions |
| Union | Stack sources | Column mapping |
| Projection | Select columns | Include/exclude, rename |
| Filter | Row filtering | Filter conditions |
| Aggregation | Group and aggregate | Group by, aggregates |
| Script | Custom Python | Python code |
| Calculated Column | Derived values | Expression |
**Target Operators**:
- Local table (new or existing)
- Truncate and insert or delta merge
### Join Operations
**Join Types**:
- Inner Join: Matching rows only
- Left Outer: All left + matching right
- Right Outer: All right + matching left
- Full Outer: All rows from both
- Cross Join: Cartesian product
**Join Conditions**:
```
source1.column = source2.column
```
### Aggregation Operations
**Aggregate Functions**:
- SUM, AVG, MIN, MAX
- COUNT, COUNT DISTINCT
- FIRST, LAST
### Calculated Columns
**Expression Syntax**:
```sql
CASE WHEN column1 > 100 THEN 'High' ELSE 'Low' END
CONCAT(first_name, ' ', last_name)
ROUND(amount * exchange_rate, 2)
```
### Input Parameters
Define runtime parameters for dynamic filtering:
**Parameter Types**:
- String
- Integer
- Date
- Timestamp
**Usage in Expressions**:
```sql
WHERE region = :IP_REGION
```
### Running Data Flows
**Execution Options**:
- Manual run from Data Builder
- Scheduled via task chain
- API trigger
**Run Modes**:
- Full: Process all data
- Delta: Process changes only (requires delta capture)
---
## Replication Flows
Replicate data from source systems to SAP Datasphere or external targets.
### Creating a Replication Flow
1. Navigate to Data Builder
2. Select "New Replication Flow"
3. Add source connection and objects
4. Add target connection
5. Configure load type and mappings
6. Save and deploy
### Source Systems
**SAP Sources**:
- SAP S/4HANA Cloud (ODP, CDS views)
- SAP S/4HANA On-Premise (ODP, SLT, CDS)
- SAP BW/4HANA
- SAP ECC
- SAP HANA
**Cloud Storage Sources**:
- Amazon S3
- Azure Blob Storage
- Google Cloud Storage
- SFTP
**Streaming Sources**:
- Apache Kafka
- Confluent Kafka
### Target Systems
**SAP Datasphere Targets**:
- Local tables (managed by replication flow)
**External Targets**:
- Apache Kafka
- Confluent Kafka
- Google BigQuery
- Amazon S3
- Azure Blob Storage
- Google Cloud Storage
- SFTP
- SAP Signavio
### Load Types
| Load Type | Description | Use Case |
|-----------|-------------|----------|
| Initial Only | One-time full load | Static data |
| Initial + Delta | Full load then changes | Standard replication |
| Real-Time | Continuous streaming | Live data |
### Configuration Options
**Flow-Level Properties**:
| Property | Description | Default |
|----------|-------------|---------|
| Delta Load Frequency | Interval for delta changes | Configurable |
| Skip Unmapped Target Columns | Ignore unmapped columns | Optional |
| Merge Data Automatically | Auto-merge for file space targets | Requires consent |
| Source Thread Limit | Parallel threads for source (1-160) | 16 |
| Target Thread Limit | Parallel threads for target (1-160) | 16 |
| Content Type | Template or Native format | Template |
**Object-Level Properties**:
| Property | Description |
|----------|-------------|
| Load Type | Initial Only, Initial+Delta, Delta Only |
| Delta Capture | Enable CDC tracking |
| ABAP Exit | Custom projection logic |
| Object Thread Count | Thread count for delta operations |
| Delete Before Load | Clear target before loading |
### Critical Constraints
- **No input parameters**: Replication flows do not support input parameters
- **Thread limits read-only at design time**: Editable only after deployment
- **Content Type applies globally**: Selection affects all replication objects in the flow
- **ABAP systems**: Consult SAP Note 3297105 before creating replication flows
### Content Type (ABAP Sources)
| Type | Date/Timestamp Handling | Use Case |
|------|-------------------------|----------|
| Template Type | Applies ISO format requirements | Standard integration |
| Native Type | Dates → strings, timestamps → decimals | Custom formatting |
**Filters**:
- Define row-level filters on source
- Multiple filter conditions with AND/OR
- **Important**: For ODP-CDS, filters must apply to primary key fields only
**Mappings**:
- Automatic column mapping
- Manual mapping overrides
- Exclude columns
**Projections**:
- Custom SQL expressions
- Column transformations
- Calculated columns
- ABAP Exit for custom projection logic
### Sizing and Performance
**Thread Configuration**:
- Source/Target Thread Limits: 1-160 (default: 16)
- Higher values = more parallelism but more resources
- Consider source system capacity
**Capacity Planning**:
- Estimate data volume per table
- Consider network bandwidth
- Plan for parallel execution
- RFC fast serialization (SAP Note 3486245) for improved performance
**Load Balancing**:
- Distribute across multiple flows
- Schedule during off-peak hours
- Monitor resource consumption
### Unsupported Data Types
- BLOB, CLOB (large objects)
- Spatial data types
- Custom ABAP types
- Virtual Tables (SAP HANA Smart Data Access)
- Row Tables (use COLUMN TABLE only)
---
## Transformation Flows
Delta-aware transformations with automatic change propagation.
### Creating a Transformation Flow
1. Navigate to Data Builder
2. Select "New Transformation Flow"
3. Add source (view or graphical view)
4. Add target table
5. Configure run settings
6. Save and deploy
### Key Constraints and Limitations
**Data Access Restrictions**:
Views and Open SQL schema objects cannot be used if they:
- Reference remote tables (except BW Bridge)
- Consume views with data access controls
- Have controls applied to them
**Loading Constraints**:
- Loading delta changes from views is not supported
- Only loads data to local SAP Datasphere repository tables
- Remote tables in BW Bridge spaces must be shared with the SAP Datasphere space
### Runtime Options
| Runtime | Storage Target | Use Case |
|---------|----------------|----------|
| HANA | SAP HANA Database storage | Standard transformations |
| SPARK | SAP HANA Data Lake Files storage | Large-scale file processing |
### Load Types
| Load Type | Description | Requirements |
|-----------|-------------|--------------|
| Initial Only | Full dataset load | None |
| Initial and Delta | Full load then changes | Delta capture enabled on source and target tables |
### Input Parameter Constraints
- Cannot be created/edited in Graphical View Editor
- Scheduled flows use default values
- **Not supported** in Python operations (Spark runtime)
- Exclude from task chain input parameters
### Source Options
- Graphical view (created inline)
- SQL view (created inline)
- Existing views
### Target Table Management
**Options**:
- Create new local table
- Use existing local table
**Column Handling**:
- Add new columns automatically
- Map columns manually
- Exclude columns
### Run Modes
| Mode | Action | Use Case |
|------|--------|----------|
| Start | Process delta changes | Regular runs |
| Delete | Remove target records | Cleanup |
| Truncate | Clear and reload | Full refresh |
### Delta Processing
Transformation flows track changes automatically:
- Insert: New records
- Update: Modified records
- Delete: Removed records
### File Space Transformations
Transform data in object store (file spaces):
**Supported Functions**:
- String functions
- Numeric functions
- Date functions
- Conversion functions
---
## Local Tables
Store data directly in SAP Datasphere.
### Creating Local Tables
**Methods**:
1. Data Builder > New Table
2. Import from CSV
3. Create from data flow target
4. Create from replication flow target
### Storage Options
| Storage | Target System | Use Case |
|---------|---------------|----------|
| Disk | SAP HANA Cloud, SAP HANA database | Standard persistent storage |
| In-Memory | SAP HANA Cloud, SAP HANA database | High-performance hot data |
| File | SAP HANA Cloud data lake storage | Large-scale cost-effective storage |
### Table Properties
**Key Columns**:
- Primary key definition
- Unique constraints
**Data Types**:
- String (VARCHAR)
- Integer (INT, BIGINT)
- Decimal (DECIMAL)
- Date, Time, Timestamp
- Boolean
- Binary
### Partitioning
**Partition Types**:
- Range partitioning (date/numeric)
- Hash partitioning
**Benefits**:
- Improved query performance
- Parallel processing
- Selective data loading
### Delta Capture
Enable change tracking for incremental processing:
1. Enable delta capture on table
2. Track insert/update/delete operations
3. Query changes with delta tokens
**Important Constraint**: Once delta capture is enabled and deployed, it **cannot be modified or disabled**.
### Allow Data Transport
Available for dimensions on SAP Business Data Cloud formation tenants:
- Enables data inclusion during repository package transport
- Limited to initial import data initialization
- **Applies only to**: Dimensions, text entities, or relational datasets
### Data Maintenance
**Operations**:
- Insert records
- Update records
- Delete records
- Truncate table
- Load from file
### Local Table (File)
Store data in object store:
**Supported Formats**:
- Parquet
- CSV
- JSON
**Use Cases**:
- Large datasets
- Cost-effective storage
- Integration with data lakes
---
## Remote Tables
Virtual access to external data without copying.
### Importing Remote Tables
1. Select connection in source browser
2. Choose tables/views to import
3. Configure import settings
4. Deploy remote table
### Data Access Modes
| Mode | Description | Performance |
|------|-------------|-------------|
| Remote | Query source directly | Network dependent |
| Replicated | Copy to local storage | Fast queries |
### Replication Options
**Full Replication**:
- Copy all data
- Scheduled refresh
**Real-Time Replication**:
- Continuous change capture
- Near real-time updates
**Partitioned Replication**:
- Divide data into partitions
- Parallel loading
### Remote Table Properties
**Statistics**:
- Create statistics for query optimization
- Update statistics periodically
**Filters**:
- Define partitioning filters
- Limit data volume
---
## Task Chains
Orchestrate multiple data integration tasks.
### Creating Task Chains
1. Navigate to Data Builder
2. Select "New Task Chain"
3. Add task nodes
4. Configure dependencies
5. Save and deploy
### Supported Task Types
**Repository Objects**:
| Task Type | Activity | Description |
|-----------|----------|-------------|
| Remote Tables | Replicate | Replicate remote table data |
| Views | Persist | Persist view data to storage |
| Intelligent Lookups | Run | Execute intelligent lookup |
| Data Flows | Run | Execute data flow |
| Replication Flows | Run | Run with load type *Initial Only* |
| Transformation Flows | Run | Execute transformation flow |
| Local Tables | Delete Records | Delete records with Change Type "Deleted" |
| Local Tables (File) | Merge | Merge delta files |
| Local Tables (File) | Optimize | Compact files |
| Local Tables (File) | Delete Records | Remove data |
**Non-Repository Objects**:
| Task Type | Description |
|-----------|-------------|
| Open SQL Procedure | Execute SAP HANA schema procedures |
| BW Bridge Process Chain | Run SAP BW Bridge processes |
**Toolbar-Only Objects**:
| Task Type | Description |
|-----------|-------------|
| API Task | Call external REST APIs |
| Notification Task | Send email notifications |
**Nested Objects**:
| Task Type | Description |
|-----------|-------------|
| Task Chain | Reference locally-created or shared task chains |
### Object Prerequisites
- All objects must be deployed before adding to task chains
- SAP HANA Open SQL schema procedures require EXECUTE privileges granted to space users
- Views **cannot** have data access controls assigned
- Data flows with input parameters use default values during task chain execution
- Persisting views may include only one parameter with default value
### Execution Control
**Sequential Execution**:
- Tasks run one after another
- Succeeding task runs only when previous completes with *completed* status
- Failure stops chain execution
**Parallel Execution**:
- Multiple branches run simultaneously
- Completion condition options:
- **ANY**: Succeeds when any parallel task completes
- **ALL**: Succeeds only when all parallel tasks complete
- Synchronization at join points
**Layout Options**:
- Top-Bottom orientation
- Left-Right orientation
- Drag tasks to reorder
**Apache Spark Settings**:
- Override default Apache Spark Application Settings per task
- Configure memory and executor settings
### Input Parameters
Pass parameters to task chain tasks:
**Parameter Definition**:
```yaml
name: region
type: string
default: "US"
```
**Parameter Usage**:
- Pass to data flows
- Use in filters
- Dynamic configuration
### Scheduling
**Simple Schedule**:
- Daily, weekly, monthly
- Specific time
**Cron Expression**:
```
0 0 6 * * ? # Daily at 6 AM
0 0 */4 * * ? # Every 4 hours
```
**Important Scheduling Constraint**: If scheduling remote tables with *Replicated (Real-time)* data access, replication type converts to batch replication at the next scheduled run (eliminates real-time updates).
### Email Notifications
Configure notifications for:
- Success
- Failure
- Warning
**Recipient Options**:
- Tenant users (searchable after task chain is deployed)
- External email addresses (requires deployed task chain for recipient selection)
**Export Constraint**: CSN/JSON export does not include notification recipients
---
## Python Operators
Custom data processing with Python.
### Creating Python Operators
1. Add Script operator to data flow
2. Define input/output ports
3. Write Python code
4. Configure execution
### Python Script Structure
```python
def transform(data):
"""
Transform input data.
Args:
data: pandas DataFrame
Returns:
pandas DataFrame
"""
# Your transformation logic
result = data.copy()
result['new_column'] = result['existing'].apply(my_function)
return result
```
### Available Libraries
- pandas
- numpy
- scipy
- scikit-learn
- datetime
### Best Practices
- Keep transformations simple
- Handle null values explicitly
- Log errors appropriately
- Test with sample data
---
## Data Transformation
Column-level transformations in graphical views.
### Text Transformations
| Function | Description | Example |
|----------|-------------|---------|
| Change Case | Upper/lower/title | UPPER(name) |
| Concatenate | Join columns | CONCAT(first, last) |
| Extract | Substring | SUBSTRING(text, 1, 5) |
| Split | Divide by delimiter | SPLIT(full_name, ' ') |
| Find/Replace | Text substitution | REPLACE(text, 'old', 'new') |
### Numeric Transformations
| Function | Description |
|----------|-------------|
| ROUND | Round to precision |
| FLOOR | Round down |
| CEIL | Round up |
| ABS | Absolute value |
| MOD | Modulo operation |
### Date Transformations
| Function | Description |
|----------|-------------|
| YEAR | Extract year |
| MONTH | Extract month |
| DAY | Extract day |
| DATEDIFF | Date difference |
| ADD_DAYS | Add days to date |
### Filter Operations
```sql
-- Numeric filter
amount > 1000
-- Text filter
region IN ('US', 'EU', 'APAC')
-- Date filter
order_date >= '2024-01-01'
-- Null handling
customer_name IS NOT NULL
```
---
## Semantic Onboarding
Import objects with business semantics from SAP systems.
### SAP S/4HANA Import
Import CDS views with annotations:
- Semantic types (currency, unit)
- Associations
- Hierarchies
- Text relationships
### SAP BW/4HANA Import
Import BW objects:
- InfoObjects
- CompositeProviders
- Queries
- Analysis Authorizations
### Import Process
1. Select source connection
2. Browse available objects
3. Select objects to import
4. Review semantic mapping
5. Deploy imported objects
---
## File Spaces and Object Store
Store and process data in object store.
### Creating File Spaces
1. System > Configuration > Spaces
2. Create new file space
3. Configure object store connection
4. Set storage limits
### Data Loading
**Supported Formats**:
- Parquet (recommended)
- CSV
- JSON
**Loading Methods**:
- Replication flows
- Transformation flows
- API upload
### In-Memory Acceleration
Enable in-memory storage for faster queries:
1. Select table/view
2. Enable in-memory storage
3. Configure refresh schedule
### Premium Outbound Integration
Export data to external systems:
- Configure outbound connection
- Schedule exports
- Monitor transfer status
---
## Documentation Links
- **Data Flows**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e30fd14](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e30fd14)
- **Replication Flows**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/25e2bd7](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/25e2bd7)
- **Transformation Flows**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/f7161e6](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/f7161e6)
- **Task Chains**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/d1afbc2](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/d1afbc2)
---
**Last Updated**: 2025-11-22

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# Data Integration Monitor Reference
**Source**: [https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Integrating-data-and-managing-spaces/Data-Integration-Monitor](https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Integrating-data-and-managing-spaces/Data-Integration-Monitor)
---
## Table of Contents
1. [Monitor Overview](#monitor-overview)
2. [Remote Tables Monitoring](#remote-tables-monitoring)
3. [Local Tables Monitoring](#local-tables-monitoring)
4. [Real-Time Replication](#real-time-replication)
5. [Data Persistence](#data-persistence)
6. [View Analyzer](#view-analyzer)
7. [Flow Monitoring](#flow-monitoring)
8. [Task Chain Monitoring](#task-chain-monitoring)
9. [Scheduling](#scheduling)
10. [Statuses and Notifications](#statuses-and-notifications)
---
## Monitor Overview
The Data Integration Monitor provides visibility into data integration activities.
### Accessing the Monitor
1. Navigate to Data Integration Monitor
2. Select space
3. Choose monitor tab
### Monitor Tabs
| Tab | Purpose |
|-----|---------|
| Remote Tables | Virtual and replicated tables |
| Local Tables | Locally stored tables |
| Views | Persisted view data |
| Flows | Data/replication/transformation flows |
| Task Chains | Orchestrated tasks |
### Authorization and Permissions
**Required Privileges**:
- DW Integrator role (full access)
- DW Modeler role (limited access)
- Custom roles with monitor privileges
---
## Remote Tables Monitoring
### Remote Table Status
| Status | Description |
|--------|-------------|
| Available | Ready for queries |
| Replicating | Data loading |
| Error | Replication failed |
| Paused | Replication paused |
### Data Access Modes
**Remote Only**:
- Queries execute on source
- No local storage
- Real-time data
**Remote and Replication**:
- Data copied locally
- Faster queries
- Scheduled refresh
### Replication Operations
**Full Replication**:
1. Select remote table
2. Start replication
3. Monitor progress
4. Verify completion
**Replicate Full Set**:
- Initial load
- Complete refresh
- Scheduled full loads
**Replicate Data Changes**:
- Delta replication
- Real-time changes
- CDC-based
### Partitioning Data Loads
**Configure Partitions**:
1. Select remote table
2. Define partition column
3. Set partition values
4. Load partitions
**Partition Benefits**:
- Parallel loading
- Selective refresh
- Reduced memory
### Statistics
**Create Statistics**:
1. Select remote table
2. Create statistics
3. Schedule refresh
**Statistics Benefits**:
- Query optimization
- Better execution plans
- Improved performance
### Monitoring Remote Queries
**Query Metrics**:
- Execution time
- Rows returned
- Network latency
- Resource usage
---
## Local Tables Monitoring
### Local Table Status
| Status | Description |
|--------|-------------|
| Active | Table available |
| Loading | Data being loaded |
| Error | Load failed |
### Data Operations
**Load Data**:
- From files
- From data flows
- From replication flows
**Delete Data**:
- Full truncate
- Selective delete
- Partition delete
### Record Deletion Control
**Deletion Options**:
- Allow deletion
- Prevent deletion
- Soft delete
### Local Tables (File)
**Object Store Tables**:
- Parquet format
- Delta Lake support
- Optimized storage
**Operations**:
| Operation | Description |
|-----------|-------------|
| Merge | Combine delta files |
| Optimize | Compact files |
| Delete | Remove data |
**Merge Operations**:
1. Select table
2. Run merge
3. Verify results
---
## Real-Time Replication
### Source Requirements
**Connection Support**:
Real-time/trigger-based replication depends on the connection type. Check connection documentation for support.
**Source Object Requirements**:
- Objects must be enabled for Change Data Capture (CDC)
- If previously deployed without real-time capability, re-deploy the table
- Source views: **Not supported**
- ABAP Dictionary tables: **Not supported**
### ABAP ODP Source Requirements
**ODP-BW** (SAP BW sources):
Only InfoProviders with change logs:
| Object Type | Requirements |
|-------------|--------------|
| DataStore objects (advanced) - ADSO | Data activation with change log |
| Standard DataStore objects (classic) - ODSO | Must have change log |
| InfoObjects | Must support delta |
**Version Requirements**: SAP BW 7.4 SP23+ or SAP BW 7.5 SP17+
**ODP-CDS** (CDS views):
- All ABAP CDS views with primary key AND delta-enabled
- **Important**: Filters must apply to primary key fields only
- Non-key field filters risk inconsistent deletion propagation
**ODP-SAPI** (Extractors):
- Delta-enabled extractors with primary keys
- ADD* methods excluded
### SAP HANA Smart Data Access Requirements
- Remote objects must be **COLUMN TABLE** type
- Row Tables: **Not supported**
- Virtual Tables: **Not supported**
- Some data types and table features restricted
- Replication **cannot be paused**
### SAP HANA CDI Adapter
- Recommended: DP Agent version 2.6.1 or higher
### Enabling Real-Time
**Enable Steps**:
1. Navigate to Data Integration Monitor
2. Select relevant space
3. Access Remote Tables section
4. Select target remote table
5. If switching from scheduled to real-time:
- Execute "Remove Replicated Data" first
- Clear existing replica
- Delete existing schedule
6. Select "Enable Real-Time Data Replication"
**Important**: No logs are generated when data is replicated in real-time mode
### Replication Status
| Status | Description |
|--------|-------------|
| Active | Receiving changes |
| Paused | Temporarily stopped |
| Error | Replication failed |
| Initial Load | Loading base data |
### Pausing and Resuming
**Pause Replication**:
- Maintenance windows
- Source system changes
- Performance tuning
**Resume Replication**:
1. Verify source availability
2. Check queue status
3. Resume replication
4. Monitor catch-up
### Recovery After Failure
**Automatic Recovery**:
- Reconnection attempts
- Queue recovery
- Delta catch-up
**Manual Recovery**:
1. Identify failure cause
2. Fix underlying issue
3. Resume or restart
4. Verify data consistency
### Watermarks
**Watermark Tracking**:
- Current position in change stream
- Last processed change
- Recovery point
**View Watermarks**:
1. Select replicated table
2. View watermark details
3. Monitor lag
---
## Data Persistence
### Persisting Views
**Enable Persistence**:
1. Open view properties
2. Enable persistence
3. Configure schedule
4. Deploy
### Run Modes
| Mode | Description | Use Case |
|------|-------------|----------|
| Full | Complete refresh | Initial/reset |
| Delta | Incremental refresh | Regular updates |
| Clean Up | Remove stale data | Maintenance |
### Persistence Metrics
**Monitor Metrics**:
- Last run time
- Duration
- Rows processed
- Storage used
### Detailed Logs
**View Logs**:
1. Select persisted view
2. Open run history
3. View detailed logs
**Log Information**:
- SQL statements
- Execution times
- Error messages
- Row counts
### Memory Consumption
**Monitor Memory**:
- Current usage
- Peak usage
- Trend analysis
**Optimization**:
- Partition data
- Schedule off-peak
- Reduce scope
### Partitioning Persisted Views
**Create Partitions**:
1. Define partition column
2. Set partition scheme
3. Configure retention
4. Deploy
**Partition Schemes**:
- Range (date-based)
- Hash (value-based)
- List (explicit values)
### Data Access Control Integration
Persisted views respect data access controls:
- Row-level security applied
- User context evaluated
- Cached securely
---
## View Analyzer
### Getting Started
**Access View Analyzer**:
1. Data Integration Monitor
2. Views tab
3. Select view
4. Open analyzer
### Analysis Features
**Execution Plan**:
- Query decomposition
- Join analysis
- Filter pushdown
**Performance Metrics**:
- Execution time
- Memory usage
- I/O statistics
### Exploring Views
**Analyze View**:
1. Select view
2. Run analysis
3. Review results
**Analysis Output**:
- Execution plan visualization
- Performance recommendations
- Optimization suggestions
### Analyze Results
**Result Interpretation**:
| Metric | Good | Warning | Critical |
|--------|------|---------|----------|
| Exec Time | <1s | 1-10s | >10s |
| Memory | <1GB | 1-10GB | >10GB |
| Rows | Expected | +/-20% | >2x expected |
---
## Flow Monitoring
### Data Flow Monitoring
**Monitor Data Flows**:
1. Flows tab
2. Select data flow
3. View run history
**Run Metrics**:
- Start/end time
- Duration
- Rows processed
- Status
### Transformation Flow Monitoring
**Monitor Transformation Flows**:
1. Select transformation flow
2. View executions
3. Analyze metrics
**Metrics**:
| Metric | Description |
|--------|-------------|
| Duration | Total run time |
| Rows Inserted | New records |
| Rows Updated | Changed records |
| Rows Deleted | Removed records |
**Change Run Mode**:
- Full refresh
- Delta processing
- Truncate and reload
**Cancel Running Flow**:
1. Select running flow
2. Cancel execution
3. Review partial results
### Replication Flow Monitoring
**Monitor Replication Flows**:
1. Select replication flow
2. View status
3. Check metrics
**Replication Metrics**:
- Objects replicated
- Rows per object
- Last update time
- Error count
**Statuses and Substatuses**:
| Status | Substatus | Meaning |
|--------|-----------|---------|
| Running | Initial Load | First-time load |
| Running | Delta | Processing changes |
| Error | Connection Failed | Connectivity issue |
| Error | Authorization | Permission denied |
**Working with Existing Runs**:
- View run history
- Compare runs
- Identify trends
### File Space Operations
**Override Default Settings**:
- Custom parallelism
- Memory limits
- File formats
---
## Task Chain Monitoring
### Monitor Task Chains
**View Task Chains**:
1. Task Chains tab
2. Select chain
3. View executions
**Execution Details**:
- Task sequence
- Individual task status
- Duration per task
- Error details
### Schedule Management
**Modify Schedule Owner**:
1. Select scheduled chain
2. Transfer ownership
3. Confirm change
**Pause/Resume Scheduled Tasks**:
- Pause: Temporarily stop
- Resume: Continue schedule
### Task Chain Metrics
| Metric | Description |
|--------|-------------|
| Total Duration | End-to-end time |
| Task Count | Number of tasks |
| Success Rate | Completed/total |
| Avg Task Duration | Average per task |
---
## Scheduling
### Simple Schedules
**Schedule Types**:
| Type | Pattern |
|------|---------|
| Daily | Every day at time |
| Weekly | Specific days |
| Monthly | Specific dates |
**Create Schedule**:
1. Select object
2. Add schedule
3. Configure timing
4. Activate
### Cron Expressions
**Cron Format**:
```
┌───────────── second (0-59)
│ ┌───────────── minute (0-59)
│ │ ┌───────────── hour (0-23)
│ │ │ ┌───────────── day of month (1-31)
│ │ │ │ ┌───────────── month (1-12)
│ │ │ │ │ ┌───────────── day of week (0-7)
│ │ │ │ │ │
* * * * * *
```
**Examples**:
```
0 0 6 * * ? # Daily at 6:00 AM
0 0 */4 * * ? # Every 4 hours
0 30 8 * * MON # Monday at 8:30 AM
0 0 0 1 * ? # First of month midnight
```
### Schedule Management
**View Schedules**:
- Active schedules
- Next run time
- Last run status
**Modify Schedules**:
- Change timing
- Pause/resume
- Delete schedule
---
## Statuses and Notifications
### Understanding Statuses
**Object Status**:
| Status | Color | Meaning |
|--------|-------|---------|
| Completed | Green | Successful |
| Running | Blue | In progress |
| Warning | Yellow | Partial success |
| Error | Red | Failed |
**Substatus Details**:
- Detailed error information
- Actionable guidance
- Related logs
### Warning Notifications
**Configure Warnings**:
1. User profile
2. Notification settings
3. Select events
**Warning Types**:
- Execution warnings
- Capacity warnings
- Expiration warnings
### Email Notifications
**Configure Email**:
1. Set up email
2. Select events
3. Choose recipients
**Events**:
- Task completion
- Task failure
- Schedule events
- System alerts
---
## Documentation Links
- **Monitor Overview**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/4cbf7c7](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/4cbf7c7)
- **Remote Tables**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/4dd95d7](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/4dd95d7)
- **Real-Time Replication**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/441d327](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/441d327)
- **View Analyzer**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/8921e5a](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/8921e5a)
- **Scheduling**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/7fa0762](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/7fa0762)
---
**Last Updated**: 2025-11-22

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# Data Modeling Reference
**Source**: [https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Acquiring-Preparing-Modeling-Data/Modeling-Data-in-the-Data-Builder](https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Acquiring-Preparing-Modeling-Data/Modeling-Data-in-the-Data-Builder)
---
## Table of Contents
1. [Analytic Models](#analytic-models)
2. [Dimensions](#dimensions)
3. [Facts and Measures](#facts-and-measures)
4. [Hierarchies](#hierarchies)
5. [Variables](#variables)
6. [Currency and Unit Conversion](#currency-and-unit-conversion)
7. [Structures](#structures)
8. [Business Builder](#business-builder)
9. [Semantic Types](#semantic-types)
10. [Associations](#associations)
---
## Analytic Models
Analytic models provide analytics-ready semantic structures for SAP Analytics Cloud.
### Terminology Differences
Key terminology differences between facts and analytic models:
| In Fact Source | In Analytic Model |
|----------------|-------------------|
| Input parameters | Variables |
| Attributes | Dimensions |
### Critical Constraints
- **LargeString limitation**: Attributes of type LargeString are not consumable in SAP Analytics Cloud
- **Three-minute timeout**: Data preview and query execution have a 3-minute timeout
- **Story resave required**: Modified analytic models require story resave in SAP Analytics Cloud
- **Dimension deselection**: Dimensions used in associations cannot be deselected
### Creating an Analytic Model
**From Scratch**:
1. Data Builder > New Analytic Model
2. Add fact source
3. Add dimension associations
4. Define measures
5. Configure variables
6. Save and deploy
**From Existing View/Table**:
1. Open view or table
2. Select "Create Analytic Model"
3. Automatic fact/dimension detection
4. Refine and deploy
### Model Components
| Component | Purpose | Cardinality |
|-----------|---------|-------------|
| Fact | Transactional data | 1 per model |
| Dimension | Master data | 0..n per model |
| Measure | Metrics | 1..n per model |
| Variable | Parameters | 0..n per model |
### Fact Sources
**Supported Sources**:
- Views (graphical, SQL)
- Local tables
- Remote tables
**Requirements**:
- Must contain measurable columns
- Should have dimension keys
- Recommended: time dimension key
### Adding Dimensions
1. Select fact source
2. Identify dimension key columns
3. Associate dimension views/tables
4. Map key columns
### Changing Model Sources
**Replace Fact Source**:
1. Open analytic model
2. Select new fact source
3. Remap associations
4. Verify measures
**Change Underlying Model**:
- Update source view
- Propagate changes
- Validate model integrity
### Data Preview
Preview data in analytic models:
- Select dimensions to display
- Choose measures
- Apply filters
- Verify aggregations
---
## Dimensions
Dimensions categorize and filter analytical data.
### Creating Dimensions
**Dimension View Requirements**:
- Key column(s)
- Text column (optional)
- Hierarchy columns (optional)
- Additional attributes
### Dimension Types
| Type | Use Case | Features |
|------|----------|----------|
| Standard | General categorization | Key, text, attributes |
| Time | Calendar filtering | Date hierarchies |
| Fiscal Time | Custom calendars | Fiscal periods |
| Text Entity | Translations | Language-dependent |
### Time Dimensions
**Standard Time Dimension**:
```
Year > Quarter > Month > Week > Day
```
**Creating Time Data**:
1. Space Settings > Time Data
2. Select calendar type
3. Define date range
4. Generate time tables
### Fiscal Time Dimensions
Custom fiscal calendars:
- Define fiscal year start
- Configure periods
- Map to calendar dates
**Fiscal Variants**:
- Standard (12 months)
- 4-4-5 week calendar
- Custom period definitions
### Dimension Attributes
**Attribute Types**:
- Key attributes (identifiers)
- Text attributes (descriptions)
- Calculated attributes
- Reference attributes
**Prefix/Suffix**:
Add prefixes or suffixes to distinguish attributes:
```
MATERIAL_ID -> DIM_MATERIAL_ID
```
### Time Dependency
Enable time-dependent attributes (SCD Type 2):
1. Enable time dependency on dimension
2. Define valid-from/valid-to columns
3. Query returns values valid at reference date
---
## Facts and Measures
### Creating Facts
**Fact Requirements**:
- At least one measure column
- Dimension key columns
- Optional: time key column
### Measure Types
| Type | Description | Use Case |
|------|-------------|----------|
| Simple | Direct aggregation | SUM(amount) |
| Calculated | Derived measure | revenue - cost |
| Restricted | Filtered measure | SUM(amount) WHERE region='US' |
| Count Distinct | Unique values | COUNT(DISTINCT customer) |
| Non-Cumulative | Point-in-time | Inventory balance |
| Currency Conversion | Dynamic conversion | Convert to target currency |
| Unit Conversion | Dynamic conversion | Convert to target unit |
### Simple Measures
Define aggregation behavior:
```yaml
measure: total_sales
aggregation: SUM
source_column: sales_amount
```
### Calculated Measures
**Expression Examples**:
```sql
-- Profit margin
(revenue - cost) / revenue * 100
-- Year-over-year growth
(current_sales - previous_sales) / previous_sales * 100
-- Weighted average
SUM(price * quantity) / SUM(quantity)
```
### Restricted Measures
Apply filters to measures:
```yaml
measure: us_sales
base_measure: total_sales
filter: region = 'US'
```
**Multiple Restrictions**:
```yaml
filter: region = 'US' AND year = 2024
```
### Count Distinct Measures
Count unique values:
```yaml
measure: unique_customers
type: COUNT_DISTINCT
source_column: customer_id
```
### Non-Cumulative Measures
Point-in-time values (not additive over time):
**Use Cases**:
- Inventory levels
- Account balances
- Headcount
**Configuration**:
1. Set measure as non-cumulative
2. Define exception aggregation (LAST, FIRST, AVG)
3. Specify aggregation dimension
### Aggregation and Exception Aggregation
**Standard Aggregation**:
| Type | Behavior |
|------|----------|
| SUM | Add values |
| MIN | Minimum value |
| MAX | Maximum value |
| COUNT | Count rows |
| AVG | Average value |
**Exception Aggregation**:
Override standard aggregation for specific dimensions:
- LAST: Last value
- FIRST: First value
- NOP: No aggregation
---
## Hierarchies
Navigation structures for drill-down analysis.
### Hierarchy Types
| Type | Structure | Example |
|------|-----------|---------|
| Level-Based | Fixed levels | Year > Quarter > Month |
| Parent-Child | Recursive | Org hierarchy |
| External | Reference table | Custom hierarchy |
### Creating Level-Based Hierarchies
1. Add hierarchy to dimension
2. Define level columns
3. Set level order
4. Configure node properties
### Creating Parent-Child Hierarchies
1. Define parent column
2. Define child column
3. Configure orphan handling
4. Set root detection
### Hierarchy with Directory
Use directory table to define hierarchy nodes:
**Directory Table Structure**:
```
node_id | parent_id | node_name | level
H1 | null | Root | 1
H2 | H1 | Region | 2
H3 | H2 | Country | 3
```
### External Hierarchies
Reference external hierarchy definitions:
- BW hierarchies
- Custom hierarchy tables
- Time hierarchies
---
## Variables
Runtime parameters for analytic models.
### Variable Types
| Type | Purpose | Example |
|------|---------|---------|
| Standard | General filtering | Region selection |
| Reference Date | Time filtering | Reporting date |
| Filter | Predefined filters | Current year |
| Restricted Measure | Measure parameters | Currency selection |
### Creating Variables
1. Open analytic model
2. Add variable
3. Define type and properties
4. Set default value
5. Configure input help
### Standard Variables
**Properties**:
- Name and description
- Data type
- Selection type (single, multiple, range)
- Default value
### Reference Date Variables
Control time-dependent queries:
- Current date
- Specific date
- Relative date (yesterday, last month)
### Filter Variables
Predefined filter combinations:
```yaml
variable: current_fiscal_year
filters:
- fiscal_year = CURRENT_FISCAL_YEAR
```
### Derived Variables
Calculate variable values from other variables:
```yaml
variable: previous_year
derived_from: selected_year - 1
```
### Dynamic Defaults
Set defaults based on context:
- Current user
- Current date
- System variables
---
## Currency and Unit Conversion
Dynamic conversion in analytic models.
### Currency Conversion
**Requirements**:
- TCUR* tables (SAP standard)
- Exchange rate types
- Reference date
### Setting Up Currency Conversion
1. Import TCUR tables (TCURR, TCURV, TCURF, TCURX)
2. Create currency conversion views
3. Enable conversion on measures
4. Configure target currency
### Currency Conversion Measure
```yaml
measure: sales_usd
type: currency_conversion
source_measure: sales_local
source_currency: local_currency
target_currency: 'USD'
exchange_rate_type: 'M'
reference_date: posting_date
```
### Currency Conversion Scenarios
| Scenario | Configuration |
|----------|---------------|
| Fixed target | target_currency = 'USD' |
| Variable target | target_currency = :IP_CURRENCY |
| Source currency column | source_currency = currency_key |
### Unit Conversion
**Requirements**:
- T006* tables (SAP standard)
- Unit conversion factors
### Setting Up Unit Conversion
1. Import T006 tables (T006, T006A, T006D)
2. Create unit conversion views
3. Enable conversion on measures
4. Configure target unit
### Unit Conversion Measure
```yaml
measure: quantity_kg
type: unit_conversion
source_measure: quantity
source_unit: unit_of_measure
target_unit: 'KG'
```
---
## Structures
Group measures for organized presentation.
### Creating Structures
1. Add structure to analytic model
2. Define structure members
3. Configure member properties
### Structure Members
**Types**:
- Simple member (reference measure)
- Calculated member (expression)
- Restricted member (filtered)
### Calculated Structure Members
```yaml
member: profit_margin
expression: ([revenue] - [cost]) / [revenue] * 100
```
### Restricted Structure Members
```yaml
member: us_revenue
base_member: revenue
restriction: region = 'US'
```
---
## Business Builder
Create business-oriented semantic models for consumption by SAP Analytics Cloud and Microsoft Excel.
### Business Builder Purpose
The Business Builder "combines, refines, and enriches Data Builder objects" with these benefits:
- **Loose Coupling**: Data source switching without disrupting reporting
- **Measure Enrichment**: Add derived, calculated measures and new attributes
- **Reusability**: Single business entities used across multiple models
### Business Builder Objects
| Object | Purpose | Contains |
|--------|---------|----------|
| Business Entity | Reusable component | Attributes, associations |
| Fact Model | Intermediate layer (optional) | Facts, dimensions |
| Consumption Model | Star schema for analytics | Business entities, measures |
| Perspective | Exposed view for BI tools | Selected measures/dimensions |
### Workflow
```
Data Builder Objects
↓
Business Entities (consume Data Builder entities)
↓
Fact Models (optional intermediate layer)
↓
Consumption Models (star schemas)
↓
Perspectives (expose to SAP Analytics Cloud, Excel, BI clients)
```
### Creating Business Entities
1. Business Builder > New Business Entity
2. Select data source (from Data Builder)
3. Define key
4. Add attributes
5. Define associations
6. **Loose coupling**: Can switch data source later without breaking reports
### Business Entity Types
**Dimension Entity**:
- Master data
- Key and text
- Hierarchy support
**Transaction Entity**:
- Transactional data
- Measures
- Dimension references
### Creating Fact Models
1. Business Builder > New Fact Model
2. Add fact entities
3. Add dimension entities
4. Define measures
5. Configure filters
### Creating Consumption Models
1. Business Builder > New Consumption Model
2. Add fact model
3. Configure perspectives
4. Add filters
5. Set authorizations
### Perspectives
Perspectives expose data to external tools:
- SAP Analytics Cloud
- Microsoft Excel
- Other BI clients
- OData API consumers
**Creating Perspectives**:
1. Open consumption model
2. Create new perspective
3. Select measures to expose
4. Select dimensions to include
5. Configure default filters
6. Deploy
### Authorization Scenarios
Row-level security in Business Builder:
1. Create authorization scenario
2. Define criteria (user attributes)
3. Assign to consumption model
### Import from SAP BW/4HANA
Import BW models:
- CompositeProviders
- InfoObjects
- Queries
---
## Semantic Types
Define column semantics for SAP Analytics Cloud.
### Attribute Semantic Types
| Type | Purpose | Example |
|------|---------|---------|
| Key | Identifier | customer_id |
| Text | Description | customer_name |
| Currency | Currency code | currency_key |
| Unit | Unit of measure | uom |
| Date | Date value | order_date |
### Measure Semantic Types
| Type | Purpose |
|------|---------|
| Amount | Currency amounts |
| Quantity | Measured quantities |
| Count | Counted values |
| Percentage | Ratios |
### Setting Semantic Types
1. Open view/table properties
2. Select column
3. Set semantic type
4. Configure related columns
---
## Associations
Define relationships between entities.
### Association Types
| Type | Cardinality | Use Case |
|------|-------------|----------|
| To-One | n:1 | Fact to dimension |
| To-Many | 1:n | Parent to children |
### Creating Associations
1. Select source entity
2. Add association
3. Select target entity
4. Map key columns
5. Configure properties
### Association Properties
**Join Type**:
- Inner (default)
- Left Outer
**Cardinality**:
- Exactly One
- Zero or One
- Many
### Text Associations
Link dimension to text entity:
```yaml
association: customer_text
target: customer_texts
join: customer_id = text_customer_id
filter: language = :SYSTEM_LANGUAGE
```
---
## Documentation Links
- **Analytic Models**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e5fbe9e](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e5fbe9e)
- **Business Builder**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/3829d46](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/3829d46)
- **Dimensions**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/5aae0e9](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/5aae0e9)
- **Measures**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e4cc3e8](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e4cc3e8)
---
**Last Updated**: 2025-11-22

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# Graphical and SQL Views Reference
**Source**: [https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Acquiring-Preparing-Modeling-Data](https://github.com/SAP-docs/sap-datasphere/tree/main/docs/Acquiring-Preparing-Modeling-Data)
---
## Table of Contents
1. [Graphical Views](#graphical-views)
2. [SQL Views](#sql-views)
3. [Entity-Relationship Models](#entity-relationship-models)
4. [Intelligent Lookups](#intelligent-lookups)
5. [View Operations](#view-operations)
6. [Input Parameters](#input-parameters)
7. [Data Access Controls](#data-access-controls-in-views)
8. [Persistence](#persistence)
9. [Validation and Performance](#validation-and-performance)
10. [SQL Reference](#sql-reference)
---
## Graphical Views
Create views visually using drag-and-drop operations.
### Prerequisites
**Required Scoped Role Privileges**:
| Privilege | Access | Description |
|-----------|--------|-------------|
| Data Warehouse General | `-R------` | System access |
| Data Warehouse Data Builder | `CRUD----` | Create/edit/delete views |
| Space Files | `CRUD----` | Manage space objects |
The **DW Modeler** role template includes these privileges.
### Creating a Graphical View
1. Data Builder > New Graphical View
2. Add source from repository or connection
3. Add transformation nodes
4. Configure output columns
5. Save and deploy
### Output Node Properties
**Required Properties**:
- Business Name (display name)
- Technical Name (immutable after saving)
- Package assignment (immutable after selection)
**Semantic Usage Options**:
| Type | Purpose | Use Case |
|------|---------|----------|
| Fact | Transactional data with measures | Sales, orders |
| Dimension | Master data for categorization | Products, customers |
| Hierarchy | Hierarchical structure | Org chart, geography |
| Text | Language-dependent labels | Translations |
| Relational Dataset | Generic relational data | Any data |
| Analytical Dataset | Analytics-ready (deprecated) | Legacy models |
| Hierarchy with Directory | Multiple parent-child hierarchies | Complex org structures |
**Dimension Type**: Standard or Fiscal Time (requires fiscal calendar configuration)
**Exposure for Consumption**:
- Enable OData, ODBC, JDBC access
- Required for external BI tools
- **Note**: DW Viewer role users can only preview if this is enabled
**Analytical Mode Option**:
- Optimized for analytical queries
- Automatic aggregation behavior
- Sends `USE_OLAP_PLAN` hint to SAP HANA
**Data Preview Restrictions (DW Viewer Role)**:
- Cannot preview data if *Expose for Consumption* is disabled
- Can only preview data in the output node (not intermediate nodes)
### Editor Toolbar Tools
| Tool | Purpose |
|------|---------|
| Save/Save As | Design-time repository persistence |
| Deploy | Runtime environment activation |
| Share | Cross-space distribution |
| Preview Data | Node output visualization |
| Undo/Redo | Change reversal/restoration |
| Export | CSN/JSON file export |
| Impact & Lineage | Dependency graph visualization |
| Generate OData Request | OData API access preparation |
| Runtime Metrics | Performance analysis with Explain Plan generation |
| Generate Semantics | AI-assisted semantic type identification |
| Versions | Version history access |
| Details | Properties panel toggle |
### Key Constraint
**Operator Limitation**: You can only create ONE of each operator type (Filter, Projection, Calculated Columns, Aggregation) per source or join.
### Adding Sources
**Source Types**:
- Local tables
- Remote tables
- Views
- Shared entities
**Source Browser**:
- Browse connections
- Search objects
- Preview data
### Join Operations
**Creating Joins**:
1. Drag second source onto canvas
2. Connect to existing source
3. Select join type
4. Configure join conditions
**Join Types**:
| Type | Result |
|------|--------|
| Inner | Matching rows only |
| Left Outer | All left + matching right |
| Right Outer | All right + matching left |
| Full Outer | All rows from both |
| Cross | Cartesian product |
**Join Conditions**:
```
Table1.customer_id = Table2.customer_id
Table1.year = Table2.fiscal_year AND Table1.month = Table2.period
```
### Union Operations
**Creating Unions**:
1. Add second source
2. Select union operation
3. Map columns between sources
**Union Types**:
- Union All: Include duplicates
- Union: Remove duplicates
### Filter Operations
**Filter Types**:
- Simple filter (column = value)
- Range filter (column BETWEEN x AND y)
- List filter (column IN (a, b, c))
- Pattern filter (column LIKE '%pattern%')
**Filter Syntax**:
```sql
status = 'Active'
amount >= 1000 AND amount <= 10000
region IN ('US', 'EU', 'APAC')
customer_name LIKE 'A%'
created_date IS NOT NULL
```
### Aggregation Operations
**Group By**:
1. Add aggregation node
2. Select grouping columns
3. Configure aggregate functions
**Aggregate Functions**:
| Function | Description |
|----------|-------------|
| SUM | Total of values |
| AVG | Average value |
| MIN | Minimum value |
| MAX | Maximum value |
| COUNT | Row count |
| COUNT DISTINCT | Unique count |
### Column Operations
**Reorder Columns**:
- Drag columns in output panel
- Set column order
**Rename Columns**:
- Click column name
- Enter new business name
**Exclude Columns**:
- Uncheck columns in output panel
- Hidden from downstream
### Calculated Columns
**Creating Calculated Columns**:
1. Add calculated column
2. Enter expression
3. Set data type
4. Name column
**Expression Types**:
- Arithmetic: `price * quantity`
- String: `CONCAT(first_name, ' ', last_name)`
- Conditional: `CASE WHEN status = 'A' THEN 'Active' ELSE 'Inactive' END`
- Date: `YEAR(order_date)`
### Conversion Columns
**Currency Conversion**:
1. Add currency conversion column
2. Select source amount column
3. Select source currency column
4. Configure target currency
5. Set exchange rate type
**Unit Conversion**:
1. Add unit conversion column
2. Select source quantity column
3. Select source unit column
4. Configure target unit
**Geo Coordinates**:
1. Add geo coordinates column
2. Select latitude/longitude columns
3. Configure coordinate system
### Replacing Sources
**Replace Source**:
1. Select source node
2. Choose "Replace"
3. Select new source
4. Remap columns
**Process Source Changes**:
- Detect schema changes
- Update mappings
- Handle removed columns
---
## SQL Views
Create views using SQL or SQLScript.
### Creating an SQL View
1. Data Builder > New SQL View
2. Write SQL statement
3. Validate syntax
4. Save and deploy
### Language Options
| Language | Capabilities | Use Case |
|----------|--------------|----------|
| SQL (Standard Query) | SELECT with JOIN, UNION operators | Standard views |
| SQLScript (Table Function) | IF, loops, complex structures | Advanced logic |
### Critical Syntax Requirements
**Double Quotes Mandatory**: Use double quotes for all table, column, and alias references in SELECT statements.
```sql
-- Correct
SELECT "customer_id", "customer_name" AS "name" FROM "customers"
-- Incorrect - will fail
SELECT customer_id, customer_name AS name FROM customers
```
**LIMIT vs TOP**: Use LIMIT keyword (TOP is not supported)
```sql
-- Correct
SELECT * FROM orders LIMIT 100
-- Incorrect
SELECT TOP 100 * FROM orders
```
**Format Button**: Available for SQL only (not SQLScript)
### Data Preview Constraints
- Data preview unavailable when any source is cross-space shared with input parameters
- Wide tables may truncate results to prevent memory issues
### Basic SQL View
```sql
SELECT
customer_id,
customer_name,
region,
country
FROM customers
WHERE active = 'Y'
```
### SQL View with Joins
```sql
SELECT
o.order_id,
o.order_date,
c.customer_name,
p.product_name,
ol.quantity,
ol.unit_price
FROM orders o
INNER JOIN customers c ON o.customer_id = c.customer_id
INNER JOIN order_lines ol ON o.order_id = ol.order_id
INNER JOIN products p ON ol.product_id = p.product_id
```
### SQL View with Aggregation
```sql
SELECT
customer_id,
YEAR(order_date) AS order_year,
COUNT(*) AS order_count,
SUM(order_amount) AS total_amount,
AVG(order_amount) AS avg_amount
FROM orders
GROUP BY customer_id, YEAR(order_date)
```
### SQLScript Views
**Table Variables**:
```sql
DO BEGIN
lt_customers = SELECT * FROM customers WHERE region = 'US';
lt_orders = SELECT * FROM orders WHERE customer_id IN (SELECT customer_id FROM :lt_customers);
SELECT * FROM :lt_orders;
END;
```
**Control Flow**:
```sql
DO BEGIN
DECLARE lv_year INTEGER := YEAR(CURRENT_DATE);
IF :lv_year > 2024 THEN
SELECT * FROM orders WHERE order_year = :lv_year;
ELSE
SELECT * FROM archive_orders WHERE order_year = :lv_year;
END IF;
END;
```
### Input Parameters in SQL Views
**Parameter Definition**:
```sql
-- Input parameter: IP_REGION (String)
SELECT *
FROM customers
WHERE region = :IP_REGION
```
**Multiple Parameters**:
```sql
-- IP_START_DATE (Date), IP_END_DATE (Date), IP_REGION (String)
SELECT *
FROM orders
WHERE order_date BETWEEN :IP_START_DATE AND :IP_END_DATE
AND region = :IP_REGION
```
---
## Entity-Relationship Models
Visual data modeling with entities and associations.
### Creating an E-R Model
1. Data Builder > New E-R Model
2. Add entities (tables/views)
3. Create associations
4. Save and deploy
### Adding Entities
**Create Table**:
- Define columns
- Set primary key
- Configure properties
**Create View**:
- Define SELECT statement
- Configure output
**Add Existing**:
- Drag from repository
- Reference existing objects
### Creating Associations
1. Select source entity
2. Draw line to target entity
3. Configure join columns
4. Set cardinality
**Association Properties**:
| Property | Options |
|----------|---------|
| Cardinality | 1:1, 1:n, n:1, n:m |
| Join Type | Inner, Left Outer |
| Semantic | Reference, Composition |
### Adding Related Entities
**Discover Related**:
- Analyze existing associations
- Suggest related entities
- Auto-create associations
---
## Intelligent Lookups
Match and enrich data using fuzzy logic when traditional joins fail due to data quality issues.
**Purpose**: Merge data from two entities even when problems joining them exist (unreliable foreign keys, inconsistent naming, data quality issues).
### Technical Architecture
**Component Structure**:
1. Input entity with mandatory pairing column
2. Lookup entity with designated return columns
3. Rule node (exact or fuzzy matching)
4. Output view configuration
### Pairing Column Requirements
The pairing column identifies individual records:
- Typically ID fields or unique identifiers
- Can be a calculated column concatenating multiple values
- Falls back to key column if primary identifier unavailable
### Creating an Intelligent Lookup
1. Data Builder > New Intelligent Lookup
2. Add input entity
3. Add lookup entity
4. **Define pairing column**
5. Configure match rules
6. Define output
### Match Rule Types
**Exact Match**:
```yaml
rule: exact_customer_id
input_column: customer_id
lookup_column: customer_key
match_type: exact
```
**Fuzzy Match**:
```yaml
rule: fuzzy_company_name
input_column: company_name
lookup_column: organization_name
match_type: fuzzy
threshold: 0.8
```
### Fuzzy Match Configuration
| Parameter | Description | Default |
|-----------|-------------|---------|
| Threshold | Match score (0-1) | 0.8 |
| Algorithm | Matching algorithm | Levenshtein |
| Case Sensitive | Match case | No |
### Result Categories
Results are color-coded with percentages on rule symbols:
| Category | Color | Description | Actions |
|----------|-------|-------------|---------|
| Matched | Green | Records matched against lookup data | Can reject |
| Review | Green | Fuzzy matches between review/matched thresholds | Approve or reject |
| Multiple | Yellow | Records matching 2+ lookup records | Select candidate |
| Unmatched | Red | No matching lookup record found | Manual match |
| Unprocessed | Grey | New records not processed since last run | Run lookup |
### Processing Results
**Matched Results**:
- Single match: Auto-assign
- Multiple matches: Review/select candidates
- No match: Manual assignment
**Unmatched Results**:
- Create new lookup records
- Manual matching
- Skip records
### Rule Management
**Modification Handling**:
- Modifying rules prompts deletion of subsequent results
- User-confirmed matches can be preserved or deleted
**Adding Rules**:
- **Add Rule for Multiple Matches**: Applies AND logic to narrow down candidates
- **Add Rule for Unmatched Records**: Targets unmatched category for re-processing
**Important**: Redeployment required after rule modification before re-execution
### Multi-Rule Lookups
Combine multiple rules:
1. Exact match on ID
2. Fuzzy match on name
3. Location-based match
**Example: Address Enrichment**
```yaml
rules:
- exact: postal_code
- fuzzy: street_name (0.85)
- fuzzy: city_name (0.9)
```
---
## View Operations
### Saving and Deploying
**Save**: Store definition
**Deploy**: Activate for use
**Deployment Validation**:
- Syntax check
- Dependency check
- Semantic validation
### Object Dependencies
**View Dependencies**:
```
View A (deployed)
└── View B (requires A)
└── View C (requires B)
```
**Impact Analysis**:
- Find dependent objects
- Assess change impact
- Plan modifications
### Lineage Analysis
**Column Lineage**:
- Track column origins
- Understand transformations
- Document data flow
**Impact Lineage**:
- Identify downstream impact
- Plan changes safely
### Version Management
**Version History**:
- View all versions
- Compare versions
- Restore previous version
---
## Input Parameters
Runtime parameters for dynamic filtering.
### Creating Input Parameters
1. Open view properties
2. Add input parameter
3. Configure type and default
4. Use in filter/expression
### Parameter Types
| Type | Use Case | Example |
|------|----------|---------|
| String | Text filtering | Region code |
| Integer | Numeric filtering | Year |
| Date | Date filtering | Start date |
| Timestamp | DateTime filtering | As-of timestamp |
### Parameter Usage
**In Filters**:
```sql
WHERE region = :IP_REGION
```
**In Expressions**:
```sql
CASE WHEN year = :IP_YEAR THEN 'Current' ELSE 'Historical' END
```
**Default Values**:
```yaml
parameter: IP_YEAR
type: Integer
default: YEAR(CURRENT_DATE)
```
---
## Data Access Controls in Views
Apply row-level security to views.
### Applying Data Access Control
1. Open view properties
2. Select "Data Access Control"
3. Choose DAC object
4. Map columns
5. Deploy
### DAC Integration
**Criteria Mapping**:
```yaml
view_column: region
dac_criteria: user_region
```
**Multiple Criteria**:
```yaml
mappings:
- region: user_region
- company_code: user_company
```
---
## Persistence
Store view results for improved performance.
### Enabling Persistence
1. Open view properties
2. Enable persistence
3. Configure refresh schedule
4. Deploy
### Persistence Options
| Option | Description |
|--------|-------------|
| Scheduled | Refresh at intervals |
| On-Demand | Manual refresh |
| Delta | Incremental refresh |
### Partitioning Persisted Views
**Partition by Date**:
```yaml
partition_column: order_date
partition_type: range
partition_function: monthly
```
---
## Validation and Performance
### Validating View Data
**Data Preview**:
- View sample data
- Check row counts
- Verify calculations
**Validation Rules**:
- Data type checks
- Null checks
- Business rules
### Analyzing View Performance
**Performance Analysis**:
- Execution time
- Row counts
- Resource usage
**Optimization Tips**:
- Filter early
- Minimize joins
- Use appropriate indexes
- Consider persistence
---
## SQL Reference
### Common SQL Functions
**String Functions**:
| Function | Example |
|----------|---------|
| CONCAT | CONCAT(a, b) |
| SUBSTRING | SUBSTRING(s, 1, 5) |
| UPPER/LOWER | UPPER(name) |
| TRIM | TRIM(text) |
| LENGTH | LENGTH(string) |
| REPLACE | REPLACE(s, 'old', 'new') |
**Numeric Functions**:
| Function | Example |
|----------|---------|
| ROUND | ROUND(num, 2) |
| FLOOR/CEIL | FLOOR(num) |
| ABS | ABS(value) |
| MOD | MOD(a, b) |
| POWER | POWER(base, exp) |
**Date Functions**:
| Function | Example |
|----------|---------|
| YEAR | YEAR(date) |
| MONTH | MONTH(date) |
| DAY | DAY(date) |
| ADD_DAYS | ADD_DAYS(date, 7) |
| DATEDIFF | DATEDIFF(d1, d2) |
| CURRENT_DATE | CURRENT_DATE |
**Conversion Functions**:
| Function | Example |
|----------|---------|
| CAST | CAST(num AS VARCHAR) |
| TO_DATE | TO_DATE(str, 'YYYY-MM-DD') |
| TO_DECIMAL | TO_DECIMAL(str, 10, 2) |
### Window Functions
```sql
-- Row number
ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date)
-- Running total
SUM(amount) OVER (PARTITION BY customer_id ORDER BY order_date)
-- Rank
RANK() OVER (PARTITION BY region ORDER BY sales DESC)
```
---
## Documentation Links
- **Graphical Views**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/27efb47](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/27efb47)
- **SQL Views**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/81920e4](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/81920e4)
- **E-R Models**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/a91c042](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/a91c042)
- **Intelligent Lookups**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/8f29f80](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/8f29f80)
- **SQL Reference**: [https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/6a37cc5](https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/6a37cc5)
---
**Last Updated**: 2025-11-22