Data Model Designer

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Design data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures.

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About This Skill

# Data Model Designer
## Business Case
### Problem Statement
Construction data management challenges:
Fragmented data across systems
Inconsistent data structures
Missing relationships between entities
Difficult data integration
### Solution
Systematic data model design for construction projects, defining entities, relationships, and schemas for effective data management.
## Technical Implementation
```python
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
import json
class DataType(Enum):
STRING = "string"
INTEGER = "integer"
FLOAT = "float"
BOOLEAN = "boolean"
DATE = "date"
DATETIME = "datetime"
TEXT = "text"
JSON = "json"
class RelationType(Enum):
ONE_TO_ONE = "1:1"
ONE_TO_MANY = "1:N"
MANY_TO_MANY = "N:M"
class ConstraintType(Enum):
PRIMARY_KEY = "primary_key"
FOREIGN_KEY = "foreign_key"
UNIQUE = "unique"
NOT_NULL = "not_null"
@dataclass
class Field:
name: str
data_type: DataType
nullable: bool = True
default: Any = None
description: str = ""
constraints: List[ConstraintType] = field(default_factory=list)
@dataclass
class Entity:
name: str
description: str
fields: List[Field] = field(default_factory=list)
primary_key: str = "id"
@dataclass
class Relationship:
name: str
from_entity: str
to_entity: str
relation_type: RelationType
from_field: str
to_field: str
class ConstructionDataModel:
"""Design data models for construction projects."""
def __init__(self, project_name: str):
self.project_name = project_name
self.entities: Dict[str, Entity] = {}
self.relationships: List[Relationship] = []
def add_entity(self, entity: Entity):
"""Add entity to model."""
self.entities[entity.name] = entity
def add_relationship(self, relationship: Relationship):
"""Add relationship between entities."""
self.relationships.append(relationship)
def create_entity(self, name: str, description: str,
fields: List[Dict[str, Any]]) -> Entity:
"""Create entity from field definitions."""
entity_fields = [
Field(
name=f['name'],
data_type=DataType(f.get('type', 'string')),
nullable=f.get('nullable', True),
default=f.get('default'),
description=f.get('description', ''),
constraints=[ConstraintType(c) for c in f.get('constraints', [])]
)
for f in fields
]
entity = Entity(name=name, description=description, fields=entity_fields)
self.add_entity(entity)
return entity
def create_relationship(self, from_entity: str, to_entity: str,
relation_type: str = "1:N",
from_field: str = None) -> Relationship:
"""Create relationship between entities."""
rel = Relationship(
name=f"{from_entity}_{to_entity}",
from_entity=from_entity,
to_entity=to_entity,
relation_type=RelationType(relation_type),
from_field=from_field or f"{to_entity.lower()}_id",
to_field="id"
)
self.add_relationship(rel)
return rel
def generate_sql_schema(self, dialect: str = "postgresql") -> str:
"""Generate SQL DDL statements."""
sql = []
type_map = {
DataType.STRING: "VARCHAR(255)",
DataType.INTEGER: "INTEGER",
DataType.FLOAT: "DECIMAL(15,2)",
DataType.BOOLEAN: "BOOLEAN",
DataType.DATE: "DATE",
DataType.DATETIME: "TIMESTAMP",
DataType.TEXT: "TEXT",
DataType.JSON: "JSONB" if dialect == "postgresql" else "JSON"
}
for name, entity in self.entities.items():
columns = []
for fld in entity.fields:
col = f" {fld.name} {type_map.get(fld.data_type, 'VARCHAR(255)')}"
if not fld.nullable:
col += " NOT NULL"
if ConstraintType.PRIMARY_KEY in fld.constraints:
col += " PRIMARY KEY"
columns.append(col)
sql.append(f"CREATE TABLE {name} (\n" + ",\n".join(columns) + "\n);")
for rel in self.relationships:
sql.append(f"""ALTER TABLE {rel.from_entity}
ADD CONSTRAINT fk_{rel.name}
FOREIGN KEY ({rel.from_field}) REFERENCES {rel.to_entity}({rel.to_field});""")
return "\n\n".join(sql)
def generate_json_schema(self) -> Dict[str, Any]:
"""Generate JSON Schema representation."""
schemas = {}
for name, entity in self.entities.items():
properties = {}
required = []
for fld in entity.fields:
prop = {"description": fld.description}
if fld.data_type == DataType.STRING:
prop["type"] = "string"
elif fld.data_type == DataType.INTEGER:
prop["type"] = "integer"
elif fld.data_type == DataType.FLOAT:
prop["type"] = "number"
elif fld.data_type == DataType.BOOLEAN:
prop["type"] = "boolean"
else:
prop["type"] = "string"
properties[fld.name] = prop
if not fld.nullable:
required.append(fld.name)
schemas[name] = {
"type": "object",
"title": entity.description,
"properties": properties,
"required": required
}
return schemas
def generate_er_diagram(self) -> str:
"""Generate Mermaid ER diagram."""
lines = ["erDiagram"]
for name, entity in self.entities.items():
for fld in entity.fields[:5]:
lines.append(f" {name} {{")
lines.append(f" {fld.data_type.value} {fld.name}")
lines.append(" }")
for rel in self.relationships:
rel_symbol = {
RelationType.ONE_TO_ONE: "||--||",
RelationType.ONE_TO_MANY: "||--o{",
RelationType.MANY_TO_MANY: "}o--o{"
}.get(rel.relation_type, "||--o{")
lines.append(f" {rel.from_entity} {rel_symbol} {rel.to_entity} : \"{rel.name}\"")
return "\n".join(lines)
def validate_model(self) -> List[str]:
"""Validate data model for issues."""
issues = []
for rel in self.relationships:
if rel.from_entity not in self.entities:
issues.append(f"Missing entity: {rel.from_entity}")
if rel.to_entity not in self.entities:
issues.append(f"Missing entity: {rel.to_entity}")
for name, entity in self.entities.items():
has_pk = any(ConstraintType.PRIMARY_KEY in f.constraints for f in entity.fields)
if not has_pk:
issues.append(f"Entity '{name}' has no primary key")
return issues
class ConstructionEntities:
"""Standard construction data entities."""
@staticmethod
def project_entity() -> Entity:
return Entity(
name="projects",
description="Construction projects",
fields=[
Field("id", DataType.INTEGER, False, constraints=[ConstraintType.PRIMARY_KEY]),
Field("code", DataType.STRING, False, constraints=[ConstraintType.UNIQUE]),
Field("name", DataType.STRING, False),
Field("status", DataType.STRING),
Field("start_date", DataType.DATE),
Field("end_date", DataType.DATE),
Field("budget", DataType.FLOAT)
]
)
@staticmethod
def activity_entity() -> Entity:
return Entity(
name="activities",
description="Schedule activities",
fields=[
Field("id", DataType.INTEGER, False, constraints=[ConstraintType.PRIMARY_KEY]),
Field("project_id", DataType.INTEGER, False),
Field("wbs_code", DataType.STRING),
Field("name", DataType.STRING, False),
Field("start_date", DataType.DATE),
Field("end_date", DataType.DATE),
Field("percent_complete", DataType.FLOAT)
]
)
@staticmethod
def cost_item_entity() -> Entity:
return Entity(
name="cost_items",
description="Project cost items",
fields=[
Field("id", DataType.INTEGER, False, constraints=[ConstraintType.PRIMARY_KEY]),
Field("project_id", DataType.INTEGER, False),
Field("wbs_code", DataType.STRING),
Field("description", DataType.STRING),
Field("budgeted_cost", DataType.FLOAT),
Field("actual_cost", DataType.FLOAT)
]
)
```
## Quick Start
```python
# Create model
model = ConstructionDataModel("Office Building A")
# Add standard entities
model.add_entity(ConstructionEntities.project_entity())
model.add_entity(ConstructionEntities.activity_entity())
model.add_entity(ConstructionEntities.cost_item_entity())
# Add relationships
model.create_relationship("activities", "projects")
model.create_relationship("cost_items", "projects")
# Generate SQL
sql = model.generate_sql_schema("postgresql")
print(sql)
```
## Common Use Cases
### 1. Custom Entity
```python
model.create_entity(
name="change_orders",
description="Project change orders",
fields=[
{"name": "id", "type": "integer", "nullable": False, "constraints": ["primary_key"]},
{"name": "project_id", "type": "integer", "nullable": False},
{"name": "amount", "type": "float"},
{"name": "status", "type": "string"}
]
)
```
### 2. Generate ER Diagram
```python
er_diagram = model.generate_er_diagram()
print(er_diagram)
```
### 3. Validate Model
```python
issues = model.validate_model()
for issue in issues:
print(f"Issue: {issue}")
```
## Resources
DDC Book: Chapter 2.5 - Data Models and Standards
Website: https://datadrivenconstruction.io

Use Cases

Design database schemas from natural language descriptions of business requirements
Generate entity-relationship diagrams showing table structures and foreign keys
Create migration scripts for PostgreSQL, MySQL, or SQLite from designed models
Normalize database designs and identify potential denormalization trade-offs
Map business domain concepts to relational database tables with proper constraints

Pros & Cons

Pros

+ Natural language input lowers the barrier for non-DBA users to design schemas
+ Generates both visual diagrams and executable migration scripts
+ Handles normalization decisions with trade-off analysis

Cons

- Complex domain relationships may require manual refinement after initial generation
- Only available on claude-code and openclaw platforms
- Generated schemas reflect AI interpretation which may miss domain-specific nuances

Frequently Asked Questions

What does Data Model Designer do?

Design data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures.

What platforms support Data Model Designer?

Data Model Designer is available on Claude Code, OpenClaw.

What are the use cases for Data Model Designer?

Design database schemas from natural language descriptions of business requirements. Generate entity-relationship diagrams showing table structures and foreign keys. Create migration scripts for PostgreSQL, MySQL, or SQLite from designed models.

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