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references/data-modeling.md
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references/data-modeling.md
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# Data Modeling Reference
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**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)
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---
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## Table of Contents
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1. [Analytic Models](#analytic-models)
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2. [Dimensions](#dimensions)
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3. [Facts and Measures](#facts-and-measures)
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4. [Hierarchies](#hierarchies)
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5. [Variables](#variables)
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6. [Currency and Unit Conversion](#currency-and-unit-conversion)
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7. [Structures](#structures)
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8. [Business Builder](#business-builder)
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9. [Semantic Types](#semantic-types)
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10. [Associations](#associations)
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---
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## Analytic Models
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Analytic models provide analytics-ready semantic structures for SAP Analytics Cloud.
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### Terminology Differences
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Key terminology differences between facts and analytic models:
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| In Fact Source | In Analytic Model |
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|----------------|-------------------|
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| Input parameters | Variables |
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| Attributes | Dimensions |
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### Critical Constraints
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- **LargeString limitation**: Attributes of type LargeString are not consumable in SAP Analytics Cloud
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- **Three-minute timeout**: Data preview and query execution have a 3-minute timeout
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- **Story resave required**: Modified analytic models require story resave in SAP Analytics Cloud
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- **Dimension deselection**: Dimensions used in associations cannot be deselected
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### Creating an Analytic Model
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**From Scratch**:
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1. Data Builder > New Analytic Model
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2. Add fact source
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3. Add dimension associations
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4. Define measures
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5. Configure variables
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6. Save and deploy
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**From Existing View/Table**:
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1. Open view or table
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2. Select "Create Analytic Model"
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3. Automatic fact/dimension detection
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4. Refine and deploy
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### Model Components
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| Component | Purpose | Cardinality |
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|-----------|---------|-------------|
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| Fact | Transactional data | 1 per model |
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| Dimension | Master data | 0..n per model |
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| Measure | Metrics | 1..n per model |
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| Variable | Parameters | 0..n per model |
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### Fact Sources
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**Supported Sources**:
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- Views (graphical, SQL)
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- Local tables
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- Remote tables
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**Requirements**:
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- Must contain measurable columns
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- Should have dimension keys
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- Recommended: time dimension key
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### Adding Dimensions
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1. Select fact source
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2. Identify dimension key columns
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3. Associate dimension views/tables
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4. Map key columns
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### Changing Model Sources
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**Replace Fact Source**:
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1. Open analytic model
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2. Select new fact source
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3. Remap associations
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4. Verify measures
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**Change Underlying Model**:
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- Update source view
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- Propagate changes
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- Validate model integrity
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### Data Preview
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Preview data in analytic models:
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- Select dimensions to display
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- Choose measures
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- Apply filters
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- Verify aggregations
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---
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## Dimensions
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Dimensions categorize and filter analytical data.
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### Creating Dimensions
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**Dimension View Requirements**:
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- Key column(s)
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- Text column (optional)
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- Hierarchy columns (optional)
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- Additional attributes
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### Dimension Types
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| Type | Use Case | Features |
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|------|----------|----------|
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| Standard | General categorization | Key, text, attributes |
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| Time | Calendar filtering | Date hierarchies |
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| Fiscal Time | Custom calendars | Fiscal periods |
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| Text Entity | Translations | Language-dependent |
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### Time Dimensions
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**Standard Time Dimension**:
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```
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Year > Quarter > Month > Week > Day
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```
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**Creating Time Data**:
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1. Space Settings > Time Data
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2. Select calendar type
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3. Define date range
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4. Generate time tables
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### Fiscal Time Dimensions
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Custom fiscal calendars:
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- Define fiscal year start
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- Configure periods
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- Map to calendar dates
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**Fiscal Variants**:
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- Standard (12 months)
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- 4-4-5 week calendar
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- Custom period definitions
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### Dimension Attributes
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**Attribute Types**:
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- Key attributes (identifiers)
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- Text attributes (descriptions)
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- Calculated attributes
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- Reference attributes
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**Prefix/Suffix**:
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Add prefixes or suffixes to distinguish attributes:
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```
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MATERIAL_ID -> DIM_MATERIAL_ID
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```
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### Time Dependency
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Enable time-dependent attributes (SCD Type 2):
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1. Enable time dependency on dimension
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2. Define valid-from/valid-to columns
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3. Query returns values valid at reference date
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---
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## Facts and Measures
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### Creating Facts
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**Fact Requirements**:
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- At least one measure column
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- Dimension key columns
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- Optional: time key column
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### Measure Types
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| Type | Description | Use Case |
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|------|-------------|----------|
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| Simple | Direct aggregation | SUM(amount) |
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| Calculated | Derived measure | revenue - cost |
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| Restricted | Filtered measure | SUM(amount) WHERE region='US' |
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| Count Distinct | Unique values | COUNT(DISTINCT customer) |
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| Non-Cumulative | Point-in-time | Inventory balance |
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| Currency Conversion | Dynamic conversion | Convert to target currency |
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| Unit Conversion | Dynamic conversion | Convert to target unit |
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### Simple Measures
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Define aggregation behavior:
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```yaml
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measure: total_sales
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aggregation: SUM
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source_column: sales_amount
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```
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### Calculated Measures
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**Expression Examples**:
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```sql
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-- Profit margin
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(revenue - cost) / revenue * 100
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-- Year-over-year growth
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(current_sales - previous_sales) / previous_sales * 100
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-- Weighted average
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SUM(price * quantity) / SUM(quantity)
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```
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### Restricted Measures
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Apply filters to measures:
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```yaml
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measure: us_sales
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base_measure: total_sales
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filter: region = 'US'
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```
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**Multiple Restrictions**:
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```yaml
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filter: region = 'US' AND year = 2024
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```
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### Count Distinct Measures
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Count unique values:
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```yaml
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measure: unique_customers
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type: COUNT_DISTINCT
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source_column: customer_id
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```
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### Non-Cumulative Measures
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Point-in-time values (not additive over time):
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**Use Cases**:
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- Inventory levels
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- Account balances
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- Headcount
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**Configuration**:
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1. Set measure as non-cumulative
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2. Define exception aggregation (LAST, FIRST, AVG)
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3. Specify aggregation dimension
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### Aggregation and Exception Aggregation
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**Standard Aggregation**:
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| Type | Behavior |
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|------|----------|
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| SUM | Add values |
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| MIN | Minimum value |
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| MAX | Maximum value |
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| COUNT | Count rows |
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| AVG | Average value |
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**Exception Aggregation**:
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Override standard aggregation for specific dimensions:
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- LAST: Last value
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- FIRST: First value
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- NOP: No aggregation
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---
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## Hierarchies
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Navigation structures for drill-down analysis.
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### Hierarchy Types
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| Type | Structure | Example |
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|------|-----------|---------|
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| Level-Based | Fixed levels | Year > Quarter > Month |
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| Parent-Child | Recursive | Org hierarchy |
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| External | Reference table | Custom hierarchy |
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### Creating Level-Based Hierarchies
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1. Add hierarchy to dimension
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2. Define level columns
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3. Set level order
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4. Configure node properties
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### Creating Parent-Child Hierarchies
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1. Define parent column
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2. Define child column
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3. Configure orphan handling
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4. Set root detection
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### Hierarchy with Directory
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Use directory table to define hierarchy nodes:
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**Directory Table Structure**:
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```
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node_id | parent_id | node_name | level
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H1 | null | Root | 1
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H2 | H1 | Region | 2
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H3 | H2 | Country | 3
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```
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### External Hierarchies
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Reference external hierarchy definitions:
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- BW hierarchies
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- Custom hierarchy tables
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- Time hierarchies
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---
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## Variables
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Runtime parameters for analytic models.
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### Variable Types
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| Type | Purpose | Example |
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|------|---------|---------|
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| Standard | General filtering | Region selection |
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| Reference Date | Time filtering | Reporting date |
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| Filter | Predefined filters | Current year |
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| Restricted Measure | Measure parameters | Currency selection |
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### Creating Variables
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1. Open analytic model
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2. Add variable
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3. Define type and properties
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4. Set default value
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5. Configure input help
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### Standard Variables
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**Properties**:
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- Name and description
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- Data type
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- Selection type (single, multiple, range)
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- Default value
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### Reference Date Variables
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Control time-dependent queries:
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- Current date
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- Specific date
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- Relative date (yesterday, last month)
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### Filter Variables
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Predefined filter combinations:
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```yaml
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variable: current_fiscal_year
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filters:
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- fiscal_year = CURRENT_FISCAL_YEAR
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```
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### Derived Variables
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Calculate variable values from other variables:
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```yaml
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variable: previous_year
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derived_from: selected_year - 1
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```
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### Dynamic Defaults
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Set defaults based on context:
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- Current user
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- Current date
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- System variables
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---
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## Currency and Unit Conversion
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Dynamic conversion in analytic models.
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### Currency Conversion
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**Requirements**:
|
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- TCUR* tables (SAP standard)
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- Exchange rate types
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- Reference date
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### Setting Up Currency Conversion
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1. Import TCUR tables (TCURR, TCURV, TCURF, TCURX)
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2. Create currency conversion views
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3. Enable conversion on measures
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4. Configure target currency
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### Currency Conversion Measure
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```yaml
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measure: sales_usd
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type: currency_conversion
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source_measure: sales_local
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source_currency: local_currency
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target_currency: 'USD'
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exchange_rate_type: 'M'
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reference_date: posting_date
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```
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### Currency Conversion Scenarios
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| Scenario | Configuration |
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|----------|---------------|
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| Fixed target | target_currency = 'USD' |
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| Variable target | target_currency = :IP_CURRENCY |
|
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| Source currency column | source_currency = currency_key |
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|
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### Unit Conversion
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**Requirements**:
|
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- T006* tables (SAP standard)
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- Unit conversion factors
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|
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### Setting Up Unit Conversion
|
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|
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1. Import T006 tables (T006, T006A, T006D)
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2. Create unit conversion views
|
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3. Enable conversion on measures
|
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4. Configure target unit
|
||||
|
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### Unit Conversion Measure
|
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|
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```yaml
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measure: quantity_kg
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type: unit_conversion
|
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source_measure: quantity
|
||||
source_unit: unit_of_measure
|
||||
target_unit: 'KG'
|
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```
|
||||
|
||||
---
|
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## Structures
|
||||
|
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Group measures for organized presentation.
|
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|
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### Creating Structures
|
||||
|
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1. Add structure to analytic model
|
||||
2. Define structure members
|
||||
3. Configure member properties
|
||||
|
||||
### Structure Members
|
||||
|
||||
**Types**:
|
||||
- Simple member (reference measure)
|
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- Calculated member (expression)
|
||||
- Restricted member (filtered)
|
||||
|
||||
### Calculated Structure Members
|
||||
|
||||
```yaml
|
||||
member: profit_margin
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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
|
||||
Reference in New Issue
Block a user