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afv-library/skills/data-cloud-bdt-expert/assets/sample_bdts/minimal_dmo_to_dmo.json

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feat(bdt): reference docs + sample BDTs @W-22196528@ Adds the curated reference library the skill loads on demand, plus four synthetic sample BDTs used by docs, tests, and LLM-mode demos. references/ (4 curated Markdown files): - bdt-reference.md — top-level BDT JSON anatomy: envelope, nodes, edges, UI layer, definitions, businessType semantics. Cites the core-262 upstream JSON schema and Connect API spec. - bdt-node-catalog.md — every node type (DMO Source, DMO Sink, Filter, Join, Union, Aggregate, Window, Formula, Split, Append, etc.) with its required/optional fields and typical usage. Audited against core-262 enums. - bdt-function-catalog.md — the expression-language function surface (string, numeric, date, conditional, aggregate). Grouped by category with signature + one-line semantics. - bdt-window-functions.md — windowing operators (ROW_NUMBER, RANK, LEAD/LAG, running aggregates) with PARTITION BY / ORDER BY grammar and gotchas. assets/sample_bdts/ (4 synthetic, dependency-free BDTs): - minimal_dmo_to_dmo.json — smallest valid BDT (1 source, 1 sink). - joins_and_filters.json — join + filter composition. - window_and_aggregate.json — window function + aggregate in one graph. - append_and_split.json — append-then-split branching topology. Grounding rules enforced in this commit: - Every claim in references/ cites an upstream source (core-262 JSON schema, Connect API reference, or the Data Cloud BDT editor spec). No speculative content. - No raw DITA or internal-only documentation is shipped; references are synthesized from public-facing material. - BusinessTypeEnum values use the canonical camelCase casing from core-262 (case-cleanup fix included here). - Sample BDTs are original synthetic fixtures, not redacted customer data. Each is small enough to read end-to-end. @W-22196528@
2026-04-24 01:15:27 +08:00
{
"version": "66.0",
"nodes": {
"LOAD_ACCOUNT": {
"action": "load",
"sources": [],
"parameters": {
"dataset": {"name": "ssot__Account__dlm", "type": "dataModelObject"},
"fields": ["ssot__Id__c", "ssot__Name__c", "ssot__Industry__c"],
"sampleDetails": {"type": "TopN", "sortBy": []}
}
},
"FORMULA_NAME_UPPER": {
"action": "formula",
"sources": ["LOAD_ACCOUNT"],
"parameters": {
"expressionType": "SQL",
"fields": [
{
"name": "AccountNameUpper__c",
"label": "Account Name (upper)",
"formulaExpression": "upper(ssot__Name__c)",
"type": "TEXT",
"businessType": "Text",
"precision": 255,
"defaultValue": ""
}
]
}
},
"OUTPUT_ACCOUNT_UPPER": {
"action": "outputD360",
"sources": ["FORMULA_NAME_UPPER"],
"parameters": {
"name": "Account_Upper__dlm",
"type": "dataModelObject",
"writeMode": "OVERWRITE",
"fieldsMappings": [
{"sourceField": "ssot__Id__c", "targetField": "ssot__Id__c"},
{"sourceField": "AccountNameUpper__c", "targetField": "Name_Upper__c"},
{"sourceField": "ssot__Industry__c", "targetField": "Industry__c"}
]
}
}
},
"ui": {
"nodes": {
"LOAD_ACCOUNT": {"label": "Account", "type": "LOAD_DATASET", "top": 100, "left": 100},
"FORMULA_NAME_UPPER": {"label": "Uppercase Name", "type": "FORMULA", "top": 100, "left": 260},
"OUTPUT_ACCOUNT_UPPER": {"label": "Account Upper", "type": "OUTPUT", "top": 100, "left": 420}
},
"connectors": [
{"source": "LOAD_ACCOUNT", "target": "FORMULA_NAME_UPPER"},
{"source": "FORMULA_NAME_UPPER", "target": "OUTPUT_ACCOUNT_UPPER"}
],
"hiddenColumns": []
}
}
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