Dataverse Python Usecase Builder

Dataverse Python use case builder

Transform a Dataverse business need into a production-ready Python architecture with table design, pattern selection, SDK code, performance guidance, error handling, monitoring, and tests.

When to invoke

• "Build a Python Dataverse solution for this use case."
• "Design tables and code for a Dataverse document workflow."
• "Generate PowerPlatform-Dataverse-Client SDK code for bulk sync."
• "Create a Dataverse scheduled job in Python."
• "Recommend Dataverse architecture for this business process."

Prerequisites and context

• Target Python 3.10+ and PEP 8 style.
• Use PowerPlatform.Dataverse.client.DataverseClient, PowerPlatform.Dataverse.core.config.DataverseConfig, and azure.identity.ClientSecretCredential when authentication is needed.
• Use Dataverse table logical names, relationship names, choice values, and file columns supplied by the user or discovered from metadata. Do not invent production schema names without labeling them as proposed.

Procedure

1. Analyze requirements: operations, data volume, frequency, performance, error tolerance, and audit needs.
2. Design the data model: tables, columns, relationships, lookups, files, and option sets.
3. Select the implementation pattern from the pattern table.
4. Generate complete Python code with configuration, service class, operations, error handling, logging, and usage examples.
5. Add optimization recommendations for the expected volume and latency.
6. Document monitoring, metrics, test strategy, and recovery behavior.

Requirement analysis questions

Data model design

Use proposed schema blocks like this when the real schema is not provided:

tables = {
    "account": {
        "custom_fields": ["new_documentcount", "new_lastdocumentdate"]
    },
    "new_document": {
        "primary_key": "new_documentid",
        "columns": {
            "new_name": "string",
            "new_documenttype": "enum",
            "new_parentaccount": "lookup(account)",
            "new_uploadedby": "lookup(user)",
            "new_uploadeddate": "datetime",
            "new_documentfile": "file"
        }
    }
}

Pattern selection

Implementation skeleton

import logging
from enum import IntEnum
from typing import Optional, List, Dict, Any
from datetime import datetime
from pathlib import Path
from PowerPlatform.Dataverse.client import DataverseClient
from PowerPlatform.Dataverse.core.config import DataverseConfig
from PowerPlatform.Dataverse.core.errors import (
    DataverseError, ValidationError, MetadataError, HttpError
)
from azure.identity import ClientSecretCredential

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Status(IntEnum):
    DRAFT = 1
    ACTIVE = 2
    ARCHIVED = 3

class DataverseService:
    _instance = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance._initialize()
        return cls._instance

    def _initialize(self):
        config = DataverseConfig()
        credential = ClientSecretCredential(
            tenant_id=config.tenant_id,
            client_id=config.client_id,
            client_secret=config.client_secret,
        )
        self.client = DataverseClient(config=config, credential=credential)

Include CRUD, bulk, query, file, or scheduled methods after this skeleton based on the selected pattern.

Optimization rules

for page in client.get(
    "table",
    filter="status eq 1",
    select=["id", "name", "amount"],
    orderby="name",
    top=500
):
    pass

client.upload_file(
    table_name="table",
    record_id=id,
    file_column_name="new_file",
    file_path=path,
    chunk_size=4 * 1024 * 1024
)

Use case categories

Dataverse implementation labels

Use these labels when structuring generated code and architecture notes: Backup/restore, CLASS, CONFIGURATION, CONSTANTS, ENUMS, ERROR, EXAMPLE, HANDLING, OPERATIONS, PATTERN, RECOVERY, SERVICE, SETUP, SINGLETON, SPECIFIC, USAGE, Upload/store, and relationships/lookups.

Output template

## Dataverse Python solution - <use case>

**Status:** complete | needs details | blocked
**Pattern:** Transactional CRUD Operations | Batch Processing | Query & Analytics | File Management | Scheduled Jobs | Real-time Integration

### Architecture overview
<2-3 sentence design summary>

### Data model
| Table | Relationship | Key columns | Notes |
| --- | --- | --- | --- |
| `<logical name>` | `<relationship>` | `<columns>` | `<constraints>` |

### Implementation code
```python
<complete Python 3.10+ code>

Usage instructions

Performance notes

• <throughput, batch, paging, or chunking guidance>

Error handling

Monitoring

Testing

## Quality gate

- [ ] The solution states operations, volume, frequency, performance, error tolerance, and audit assumptions.
- [ ] Proposed table and column names are labeled when not user-provided.
- [ ] Code includes all imports, type hints, logging, and Dataverse error handling.
- [ ] The selected pattern matches the use case category and volume.
- [ ] Bulk, paging, or chunking is used for high-volume records or files.
- [ ] Usage, monitoring, and testing guidance are included.