Generate CDS Analytics Star Schema

Generate a CDS analytical model — a star schema consisting of a cube view (@Analytics.dataCategory: #CUBE), one or more dimension views (#DIMENSION), and text views (#TEXT) — on top of a RAP business object or a plain DDIC table.

This skill replicates SAP Joule's "CDS Analytical Model Generation" capability (basic + extended scope) by combining ARC-1 (SAP system access) with mcp-sap-docs (documentation & best practices). Basic scope = cube + reuse existing dimensions. Extended scope = also generate new dimensions, and start from a DDIC table (not just a RAP BO).

Prerequisites — read this first

• Requires SAP_BASIS 7.5x with the analytics annotations. Verify with SAPManage(action="probe") (rap.available). If RAP/CDS isn't available, stop.
• The output is a set of interdependent CDS views (cube → dimensions → texts). They must be created as inactive drafts and activated together so SAP's activator resolves the cross-references in one pass. This skill uses SAPWrite(action="batch_create", activateAtEnd: true) for exactly that.

Smart Defaults (apply silently, do NOT ask)

Input

The user provides either:

• A RAP business object root entity / behavior pool (e.g. ZBP_R_TRAVEL or ZI_Travel), or
• A DDIC table (e.g. ZTRAVEL), or
• An interface CDS view to use as the fact source.

Optionally: which numeric fields are measures, which fields are dimensions, target package, and whether to generate new dimension views or reuse existing ones.

Step 1: Resolve and read the source

1a. RAP BO input — find the bound CDS root entity

If the user names a behavior pool class (ZBP_*), read the class metadata to find the root entity it's bound to:

SAPRead(type="CLAS", name="<ZBP_class>", format="structured")

Look for the bound root entity reference. Then read that CDS root entity (next step). If the user already named the CDS root entity / interface view, skip to 1c.

1b. DDIC table input — read the table structure

SAPRead(type="TABL", name="<table>")

Capture the field list with types. Identify candidate keys, dimension fields (foreign keys, characteristics), and numeric measure fields (amounts, quantities, counts).

1c. CDS view input — read source + elements

SAPRead(type="DDLS", name="<view>")
SAPRead(type="DDLS", name="<view>", include="elements")

The element list classifies fields. Decide for each:

• Measure: numeric, additive (amount / quantity / count) → goes in the cube with @Aggregation.default: #SUM
• Dimension: a characteristic you slice by (region, customer, date) → either reuse an existing dimension view or generate a new one
• Currency/Unit: paired with each amount/quantity measure

Step 2: Discover reusable dimensions

For each dimension field, check whether a reusable dimension view already exists before generating a new one:

SAPSearch(query="*<dimension_keyword>*", searchType="object", objectType="DDLS")

If a released #DIMENSION view exists (e.g. I_Country, I_CalendarDate), reuse it via association. Otherwise generate a new ZI_<X>_DIM (extended scope).

Step 3: Look up current analytics annotations

Ground the generation in current docs:

search("CDS analytical model cube Analytics.dataCategory CUBE dimension representativeKey")
search("ObjectModel.foreignKey.association text association Semantics.text")

Cite the doc IDs in your summary. Key facts (7.58):

• Cube: @Analytics: { dataCategory: #CUBE, internalName: #LOCAL } + @ObjectModel: { supportedCapabilities: [ #ANALYTICAL_PROVIDER ], modelingPattern: #ANALYTICAL_CUBE }
• A cube needs ≥1 measure; measures carry @Aggregation.default: #SUM (or #MIN/#MAX/#AVG); a numeric field WITHOUT @Aggregation.default is treated as a dimension
• Cube→dimension associations must be `[1..1]`; @ObjectModel.foreignKey.association: '_Dim' goes on the dimension key field in the cube
• Dimension: @Analytics.dataCategory: #DIMENSION + @ObjectModel: { representativeKey: '<key>', supportedCapabilities: [ #ANALYTICAL_DIMENSION ], modelingPattern: #ANALYTICAL_DIMENSION }
• Text view uses @ObjectModel.dataCategory: #TEXT (the ObjectModel. namespace — NOT @Analytics.dataCategory) + @Semantics.text: true + @Semantics.language: true

Step 4: Compose the model

Cube template

@AccessControl.authorizationCheck: #NOT_REQUIRED
@EndUserText.label: 'Sales Cube'
@Analytics: {
  dataCategory: #CUBE,
  internalName: #LOCAL
}
@ObjectModel: {
  supportedCapabilities: [ #ANALYTICAL_PROVIDER ],
  modelingPattern: #ANALYTICAL_CUBE
}
define view entity ZI_Sales_Cube
  as select from zsales_data
  association [1..1] to ZI_Region_Dim as _Region
    on _Region.RegionId = $projection.RegionId
{
      @ObjectModel.foreignKey.association: '_Region'
  key region_id   as RegionId,
  key calyear     as CalendarYear,

      @Semantics.amount.currencyCode: 'CurrencyCode'
      @Aggregation.default: #SUM
      amount       as SalesAmount,

      currency     as CurrencyCode,

      @Aggregation.default: #SUM
      @EndUserText.label: 'Number of Records'
      abap.int8'1' as RecordCount,

      _Region
}

Dimension template

@AccessControl.authorizationCheck: #NOT_REQUIRED
@EndUserText.label: 'Region Dimension'
@Analytics.dataCategory: #DIMENSION
@ObjectModel: {
  representativeKey: 'RegionId',
  supportedCapabilities: [ #ANALYTICAL_DIMENSION ],
  modelingPattern: #ANALYTICAL_DIMENSION
}
define view entity ZI_Region_Dim
  as select from zregion
  association [0..1] to ZI_Region_Txt as _Text
    on _Text.RegionId = $projection.RegionId
{
      @ObjectModel.text.association: '_Text'
  key region_id as RegionId,
      _Text
}

Text view template

@AccessControl.authorizationCheck: #NOT_REQUIRED
@EndUserText.label: 'Region - Text'
@ObjectModel.dataCategory: #TEXT
@ObjectModel.representativeKey: 'RegionId'
define view entity ZI_Region_Txt
  as select from zregion_t
{
  key region_id as RegionId,

      @Semantics.language: true
  key spras     as Language,

      @Semantics.text: true
      regiontext  as RegionName
}

(The text view stands alone — it declares no association, so it carries no @ObjectModel.foreignKey.association. The link is one-directional: the dimension associates to this text view via _Text + @ObjectModel.text.association, not the other way round.)

Composition rules (enforce before writing):

• Cube has ≥1 measure (@Aggregation.default).
• Each amount measure has @Semantics.amount.currencyCode + the currency field is projected; each quantity measure has @Semantics.quantity.unitOfMeasure + the unit field is projected.
• Every cube→dimension association is [1..1] and the dimension key field carries @ObjectModel.foreignKey.association.
• Every dimension has a representativeKey.
• Text views use @ObjectModel.dataCategory: #TEXT + @Semantics.text/language.

Step 5: Show the plan as a tree, then batch-create

Show the user the model tree before writing:

Cube: ZI_Sales_Cube  (from zsales_data)
  Measures: SalesAmount (SUM, currency CurrencyCode), RecordCount (SUM)
  Dimensions:
    ZI_Region_Dim (new) → ZI_Region_Txt (new)

On confirmation, create all views in one batch with deferred activation so cross-references resolve together:

SAPWrite(action="batch_create", activateAtEnd=true, objects=[
  { type: "DDLS", name: "ZI_Region_Txt",  source: "<text_ddl>",      package: "$TMP" },
  { type: "DDLS", name: "ZI_Region_Dim",  source: "<dimension_ddl>", package: "$TMP" },
  { type: "DDLS", name: "ZI_Sales_Cube",  source: "<cube_ddl>",      package: "$TMP" }
])

Order the array dependencies-first (text → dimension → cube) so that even if activateAtEnd falls back to per-object activation on an older release, the chain still resolves. With activateAtEnd: true, ARC-1 writes all three as inactive drafts and fires one terminal activation over the whole graph.

If batch_create with activateAtEnd is not honored (older release), fall back to: create each object with SAPWrite(action="create", ...) then a single SAPActivate(objects=[...]) over all created objects.

Step 6: Verify

SAPRead(type="DDLS", name="ZI_Sales_Cube", include="elements")

Confirm the cube is live and its dimension association + measures resolved.

Step 7: Offer the query layer

The cube is the foundation; the query is what end users consume. Offer:

"Want me to generate an analytical query (the consumable KPI view) on top of this cube? → generate-cds-analytical-query"

Error Handling

Notes

• Basic vs extended scope: Basic = reuse existing dimensions (Step 2 finds released #DIMENSION views). Extended = also generate new ZI_*_DIM + ZI_*_TXT and accept a DDIC table as the fact source (Step 1b).
• This skill builds the model; the consumable query is a separate step (generate-cds-analytical-query).
• For a transactional RAP service (not analytics), use generate-rap-service.
• Reuse SAP standard dimensions where they exist (I_CalendarDate, I_Country, I_Currency) instead of regenerating them — SAPSearch in Step 2.