dataverse-python-apps

This skill provides guidance on building Python applications that use Microsoft Dataverse as a database. Use when users ask about "Python Dataverse app", "Flask Dataverse", "FastAPI Dataverse", "Dataverse backend", "Python API with Dataverse", "Dataverse data pipeline", or need help building Python applications with Dataverse.

Architecture Patterns

Basic Application Structure

my-dataverse-app/
├── app/
│   ├── __init__.py
│   ├── dataverse_client.py    # Dataverse connection
│   ├── models.py              # Data models
│   ├── services.py            # Business logic
│   └── api/
│       └── routes.py          # API endpoints
├── config.py
├── requirements.txt
└── main.py

Singleton Client Pattern

# dataverse_client.py
from PowerPlatform.Dataverse.client import DataverseClient
from azure.identity import ClientSecretCredential
import os

_client = None

def get_client() -> DataverseClient:
    global _client
    if _client is None:
        credential = ClientSecretCredential(
            tenant_id=os.environ["AZURE_TENANT_ID"],
            client_id=os.environ["AZURE_CLIENT_ID"],
            client_secret=os.environ["AZURE_CLIENT_SECRET"]
        )
        _client = DataverseClient(
            os.environ["DATAVERSE_URL"],
            credential
        )
    return _client

Flask Integration

from flask import Flask, jsonify, request
from dataverse_client import get_client

app = Flask(__name__)

@app.route('/accounts', methods=['GET'])
def list_accounts():
    client = get_client()
    pages = client.get(
        "account",
        select=["accountid", "name"],
        filter="statecode eq 0",
        top=100
    )
    accounts = []
    for page in pages:
        accounts.extend(page)
    return jsonify(accounts)

@app.route('/accounts', methods=['POST'])
def create_account():
    data = request.json
    client = get_client()
    ids = client.create("account", data)
    return jsonify({"id": ids[0]}), 201

@app.route('/accounts/<account_id>', methods=['GET'])
def get_account(account_id):
    client = get_client()
    account = client.get("account", account_id)
    return jsonify(account)

FastAPI Integration

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional, List
from dataverse_client import get_client

app = FastAPI()

class AccountCreate(BaseModel):
    name: str
    telephone1: Optional[str] = None

class Account(BaseModel):
    accountid: str
    name: str
    telephone1: Optional[str]

@app.get("/accounts", response_model=List[Account])
async def list_accounts(top: int = 100):
    client = get_client()
    pages = client.get("account", select=["accountid", "name", "telephone1"], top=top)
    accounts = []
    for page in pages:
        accounts.extend(page)
    return accounts

@app.post("/accounts", response_model=dict)
async def create_account(account: AccountCreate):
    client = get_client()
    ids = client.create("account", account.model_dump(exclude_none=True))
    return {"id": ids[0]}

@app.get("/accounts/{account_id}", response_model=Account)
async def get_account(account_id: str):
    client = get_client()
    try:
        account = client.get("account", account_id)
        return account
    except Exception:
        raise HTTPException(status_code=404, detail="Account not found")

Data Pipeline Pattern

# ETL pipeline with Dataverse
from dataverse_client import get_client
import pandas as pd

def extract_accounts():
    """Extract accounts from Dataverse."""
    client = get_client()
    pages = client.get(
        "account",
        select=["accountid", "name", "revenue", "industrycode"],
        filter="statecode eq 0"
    )
    records = []
    for page in pages:
        records.extend(page)
    return pd.DataFrame(records)

def transform_data(df: pd.DataFrame) -> pd.DataFrame:
    """Transform the data."""
    df['revenue_millions'] = df['revenue'] / 1_000_000
    df['has_revenue'] = df['revenue'] > 0
    return df

def load_to_dataverse(df: pd.DataFrame, table: str):
    """Load data back to Dataverse."""
    client = get_client()
    records = df.to_dict('records')
    ids = client.create(table, records)
    return ids

# Pipeline execution
def run_pipeline():
    df = extract_accounts()
    df = transform_data(df)
    # Save transformed data somewhere
    df.to_csv('accounts_processed.csv', index=False)

Best Practices

1. Use service principals for production apps
2. Reuse client instances - expensive to create
3. Handle errors gracefully - wrap calls in try/except
4. Use environment variables for configuration
5. Implement retry logic for transient errors
6. Limit query results with select and top
7. Use connection pooling for high-throughput apps

References

• See references/flask-patterns.md for Flask examples
• See references/fastapi-patterns.md for FastAPI examples
• See references/pipeline-patterns.md for data pipeline patterns