Dataverse SDK for Python — Real-World Use Cases & Templates - Copilot Instruction

Dataverse SDK for Python — Real-World Use Cases & Templates

Based on official Dataverse data migration and integration patterns.

1. Data Migration from Legacy Systems

Migration Architecture

text

Legacy System → Staging Database → Dataverse
    (Extract)    (Transform)        (Load)

Complete Migration Example

python

import pandas as pd
import time
from PowerPlatform.Dataverse.client import DataverseClient
from PowerPlatform.Dataverse.core.errors import DataverseError
from azure.identity import DefaultAzureCredential

class DataMigrationPipeline:
    """Migrate data from legacy system to Dataverse."""
    
    def __init__(self, org_url: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
        self.success_records = []
        self.failed_records = []
    
    def extract_from_legacy(self, legacy_db_connection, query: str):
        """Extract data from source system."""
        return pd.read_sql(query, legacy_db_connection)
    
    def transform_accounts(self, df: pd.DataFrame) -> list:
        """Transform source data to Dataverse schema."""
        payloads = []
        
        for _, row in df.iterrows():
            # Map source fields to Dataverse
            payload = {
                "name": row["company_name"][:100],  # Limit to 100 chars
                "telephone1": row["phone"],
                "websiteurl": row["website"],
                "revenue": float(row["annual_revenue"]) if row["annual_revenue"] else None,
                "numberofemployees": int(row["employees"]) if row["employees"] else None,
                # Track source ID for reconciliation
                "new_sourcecompanyid": str(row["legacy_id"]),
                "new_importsequencenumber": row["legacy_id"]
            }
            payloads.append(payload)
        
        return payloads
    
    def load_to_dataverse(self, payloads: list, batch_size: int = 200):
        """Load data to Dataverse with error tracking."""
        total = len(payloads)
        
        for i in range(0, total, batch_size):
            batch = payloads[i:i + batch_size]
            
            try:
                ids = self.client.create("account", batch)
                self.success_records.extend(ids)
                print(f"✓ Created {len(ids)} records ({len(self.success_records)}/{total})")
                
                # Prevent rate limiting
                time.sleep(0.5)
                
            except DataverseError as e:
                self.failed_records.extend(batch)
                print(f"✗ Batch failed: {e.message}")
    
    def reconcile_migration(self, df: pd.DataFrame):
        """Verify migration and track results."""
        
        # Query created records
        created_accounts = self.client.get(
            "account",
            filter="new_importsequencenumber ne null",
            select=["accountid", "new_sourcecompanyid", "new_importsequencenumber"],
            top=10000
        )
        
        created_df = pd.DataFrame(list(created_accounts))
        
        # Update source table with Dataverse IDs
        merged = df.merge(
            created_df,
            left_on="legacy_id",
            right_on="new_importsequencenumber"
        )
        
        print(f"Successfully migrated {len(merged)} accounts")
        print(f"Failed: {len(self.failed_records)} records")
        
        return {
            "total_source": len(df),
            "migrated": len(merged),
            "failed": len(self.failed_records),
            "success_rate": len(merged) / len(df) * 100
        }

# Usage
pipeline = DataMigrationPipeline("https://myorg.crm.dynamics.com")

# Extract
source_data = pipeline.extract_from_legacy(
    legacy_connection,
    "SELECT id, company_name, phone, website, annual_revenue, employees FROM companies"
)

# Transform
payloads = pipeline.transform_accounts(source_data)

# Load
pipeline.load_to_dataverse(payloads, batch_size=300)

# Reconcile
results = pipeline.reconcile_migration(source_data)
print(results)

2. Data Quality & Deduplication Agent

Detect and Merge Duplicates

python

from PowerPlatform.Dataverse.client import DataverseClient
from azure.identity import DefaultAzureCredential
import difflib

class DataQualityAgent:
    """Monitor and improve data quality."""
    
    def __init__(self, org_url: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
    
    def find_potential_duplicates(self, table_name: str, match_fields: list):
        """Find potential duplicate records."""
        
        records = []
        for page in self.client.get(table_name, select=match_fields, top=10000):
            records.extend(page)
        
        duplicates = []
        seen = {}
        
        for record in records:
            # Create key from match fields
            key = tuple(
                record.get(field, "").lower().strip() 
                for field in match_fields
            )
            
            if key in seen and key != ("",) * len(match_fields):
                duplicates.append({
                    "original": seen[key],
                    "duplicate": record,
                    "fields_matched": match_fields
                })
            else:
                seen[key] = record
        
        return duplicates, len(records)
    
    def merge_records(self, table_name: str, primary_id: str, duplicate_id: str, 
                     mapping: dict):
        """Merge duplicate record into primary."""
        
        # Copy data from duplicate to primary
        updates = {}
        duplicate = self.client.get(table_name, duplicate_id)
        
        for source_field, target_field in mapping.items():
            if duplicate.get(source_field) and not primary.get(target_field):
                updates[target_field] = duplicate[source_field]
        
        # Update primary
        if updates:
            self.client.update(table_name, primary_id, updates)
        
        # Delete duplicate
        self.client.delete(table_name, duplicate_id)
        
        return f"Merged {duplicate_id} into {primary_id}"
    
    def generate_quality_report(self, table_name: str) -> dict:
        """Generate data quality metrics."""
        
        records = list(self.client.get(table_name, top=10000))
        
        report = {
            "table": table_name,
            "total_records": len(records),
            "null_values": {},
            "duplicates": 0,
            "completeness_score": 0
        }
        
        # Check null values
        all_fields = set()
        for record in records:
            all_fields.update(record.keys())
        
        for field in all_fields:
            null_count = sum(1 for r in records if not r.get(field))
            completeness = (len(records) - null_count) / len(records) * 100
            
            if completeness < 100:
                report["null_values"][field] = {
                    "null_count": null_count,
                    "completeness": completeness
                }
        
        # Check duplicates
        duplicates, _ = self.find_potential_duplicates(
            table_name, 
            ["name", "emailaddress1"]
        )
        report["duplicates"] = len(duplicates)
        
        # Overall completeness
        avg_completeness = sum(
            100 - ((d["null_count"] / len(records)) * 100)
            for d in report["null_values"].values()
        ) / len(report["null_values"]) if report["null_values"] else 100
        report["completeness_score"] = avg_completeness
        
        return report

# Usage
agent = DataQualityAgent("https://myorg.crm.dynamics.com")

# Find duplicates
duplicates, total = agent.find_potential_duplicates(
    "account",
    match_fields=["name", "emailaddress1"]
)

print(f"Found {len(duplicates)} potential duplicates out of {total} accounts")

# Merge if confident
for dup in duplicates[:5]:  # Process top 5
    result = agent.merge_records(
        "account",
        primary_id=dup["original"]["accountid"],
        duplicate_id=dup["duplicate"]["accountid"],
        mapping={"telephone1": "telephone1", "websiteurl": "websiteurl"}
    )
    print(result)

# Quality report
report = agent.generate_quality_report("account")
print(f"Data Quality: {report['completeness_score']:.1f}%")

3. Contact & Account Enrichment

Enrich CRM Data from External Sources

python

import requests
from PowerPlatform.Dataverse.client import DataverseClient
from azure.identity import DefaultAzureCredential

class DataEnrichmentAgent:
    """Enrich CRM records with external data."""
    
    def __init__(self, org_url: str, external_api_key: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
        self.api_key = external_api_key
    
    def enrich_accounts_with_industry_data(self):
        """Enrich accounts with industry classification."""
        
        accounts = self.client.get(
            "account",
            select=["accountid", "name", "websiteurl"],
            filter="new_industrydata eq null",
            top=500
        )
        
        enriched_count = 0
        for page in accounts:
            for account in page:
                try:
                    # Call external API
                    industry = self._lookup_industry(account["name"])
                    
                    if industry:
                        self.client.update(
                            "account",
                            account["accountid"],
                            {"new_industrydata": industry}
                        )
                        enriched_count += 1
                
                except Exception as e:
                    print(f"Failed to enrich {account['name']}: {e}")
        
        return enriched_count
    
    def enrich_contacts_with_social_profiles(self):
        """Find and link social media profiles."""
        
        contacts = self.client.get(
            "contact",
            select=["contactid", "fullname", "emailaddress1"],
            filter="new_linkedinurl eq null",
            top=500
        )
        
        for page in contacts:
            for contact in page:
                try:
                    # Find social profiles
                    profiles = self._find_social_profiles(
                        contact["fullname"],
                        contact["emailaddress1"]
                    )
                    
                    if profiles:
                        self.client.update(
                            "contact",
                            contact["contactid"],
                            {
                                "new_linkedinurl": profiles.get("linkedin"),
                                "new_twitterhandle": profiles.get("twitter")
                            }
                        )
                
                except Exception as e:
                    print(f"Failed to enrich {contact['fullname']}: {e}")
    
    def _lookup_industry(self, company_name: str) -> str:
        """Call external industry API."""
        response = requests.get(
            "https://api.example.com/industry",
            params={"company": company_name},
            headers={"Authorization": f"Bearer {self.api_key}"}
        )
        
        if response.status_code == 200:
            return response.json().get("industry")
        return None
    
    def _find_social_profiles(self, name: str, email: str) -> dict:
        """Find social media profiles for person."""
        response = requests.get(
            "https://api.example.com/social",
            params={"name": name, "email": email},
            headers={"Authorization": f"Bearer {self.api_key}"}
        )
        
        if response.status_code == 200:
            return response.json()
        return {}

# Usage
enricher = DataEnrichmentAgent(
    "https://myorg.crm.dynamics.com",
    api_key="your-api-key"
)

enriched = enricher.enrich_accounts_with_industry_data()
print(f"Enriched {enriched} accounts")

4. Automated Report Data Export

Export CRM Data to Excel

python

import pandas as pd
from PowerPlatform.Dataverse.client import DataverseClient
from azure.identity import DefaultAzureCredential
from datetime import datetime

class ReportExporter:
    """Export Dataverse data to reports."""
    
    def __init__(self, org_url: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
    
    def export_sales_summary(self, output_file: str):
        """Export sales data for reporting."""
        
        accounts = []
        for page in self.client.get(
            "account",
            select=["accountid", "name", "revenue", "numberofemployees", 
                   "createdon", "modifiedon"],
            filter="statecode eq 0",  # Active only
            orderby=["revenue desc"],
            top=10000
        ):
            accounts.extend(page)
        
        # Opportunities
        opportunities = []
        for page in self.client.get(
            "opportunity",
            select=["opportunityid", "name", "estimatedvalue", 
                   "statuscode", "parentaccountid", "createdon"],
            top=10000
        ):
            opportunities.extend(page)
        
        # Create DataFrames
        df_accounts = pd.DataFrame(accounts)
        df_opportunities = pd.DataFrame(opportunities)
        
        # Generate report
        with pd.ExcelWriter(output_file) as writer:
            df_accounts.to_excel(writer, sheet_name="Accounts", index=False)
            df_opportunities.to_excel(writer, sheet_name="Opportunities", index=False)
            
            # Summary sheet
            summary = pd.DataFrame({
                "Metric": [
                    "Total Accounts",
                    "Total Opportunities",
                    "Total Revenue",
                    "Export Date"
                ],
                "Value": [
                    len(df_accounts),
                    len(df_opportunities),
                    df_accounts["revenue"].sum() if "revenue" in df_accounts else 0,
                    datetime.now().isoformat()
                ]
            })
            summary.to_excel(writer, sheet_name="Summary", index=False)
        
        return output_file
    
    def export_activity_log(self, days_back: int = 30) -> str:
        """Export recent activity for audit."""
        
        from_date = pd.Timestamp.now(tz='UTC') - pd.Timedelta(days=days_back)
        
        activities = []
        for page in self.client.get(
            "activitypointer",
            select=["activityid", "subject", "activitytypecode", 
                   "createdon", "ownerid"],
            filter=f"createdon gt {from_date.isoformat()}",
            orderby=["createdon desc"],
            top=10000
        ):
            activities.extend(page)
        
        df = pd.DataFrame(activities)
        output = f"activity_log_{datetime.now():%Y%m%d}.csv"
        df.to_csv(output, index=False)
        
        return output

# Usage
exporter = ReportExporter("https://myorg.crm.dynamics.com")
report_file = exporter.export_sales_summary("sales_report.xlsx")
print(f"Report saved to {report_file}")

5. Workflow Integration - Bulk Operations

Process Records Based on Conditions

python

from PowerPlatform.Dataverse.client import DataverseClient
from azure.identity import DefaultAzureCredential
from enum import IntEnum

class AccountStatus(IntEnum):
    PROSPECT = 1
    ACTIVE = 2
    CLOSED = 3

class BulkWorkflow:
    """Automate bulk operations."""
    
    def __init__(self, org_url: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
    
    def mark_accounts_as_inactive_if_no_activity(self, days_no_activity: int = 90):
        """Deactivate accounts with no recent activity."""
        
        from_date = f"2025-{datetime.now().month:02d}-01T00:00:00Z"
        
        inactive_accounts = self.client.get(
            "account",
            select=["accountid", "name"],
            filter=f"modifiedon lt {from_date} and statecode eq 0",
            top=5000
        )
        
        accounts_to_deactivate = []
        for page in inactive_accounts:
            accounts_to_deactivate.extend([a["accountid"] for a in page])
        
        # Bulk update
        if accounts_to_deactivate:
            self.client.update(
                "account",
                accounts_to_deactivate,
                {"statecode": AccountStatus.CLOSED}
            )
            print(f"Deactivated {len(accounts_to_deactivate)} inactive accounts")
    
    def update_opportunity_status_based_on_amount(self):
        """Update opportunity stage based on estimated value."""
        
        opportunities = self.client.get(
            "opportunity",
            select=["opportunityid", "estimatedvalue"],
            filter="statuscode ne 7",  # Not closed
            top=5000
        )
        
        updates = []
        ids = []
        
        for page in opportunities:
            for opp in page:
                value = opp.get("estimatedvalue", 0)
                
                # Determine stage
                if value < 10000:
                    stage = 1  # Qualification
                elif value < 50000:
                    stage = 2  # Proposal
                else:
                    stage = 3  # Proposal Review
                
                updates.append({"stageid": stage})
                ids.append(opp["opportunityid"])
        
        # Bulk update
        if ids:
            self.client.update("opportunity", ids, updates)
            print(f"Updated {len(ids)} opportunities")

# Usage
workflow = BulkWorkflow("https://myorg.crm.dynamics.com")
workflow.mark_accounts_as_inactive_if_no_activity(days_no_activity=90)
workflow.update_opportunity_status_based_on_amount()

6. Scheduled Job Template

Azure Function for Scheduled Operations

python

# scheduled_migration_job.py
import azure.functions as func
from datetime import datetime
from DataMigrationPipeline import DataMigrationPipeline
import logging

def main(timer: func.TimerRequest) -> None:
    """Run migration job on schedule (e.g., daily)."""
    
    if timer.past_due:
        logging.info('The timer is past due!')
    
    try:
        logging.info(f'Migration job started at {datetime.utcnow()}')
        
        # Run migration
        pipeline = DataMigrationPipeline("https://myorg.crm.dynamics.com")
        
        # Extract, transform, load
        source_data = pipeline.extract_from_legacy(...)
        payloads = pipeline.transform_accounts(source_data)
        pipeline.load_to_dataverse(payloads)
        
        # Get results
        results = pipeline.reconcile_migration(source_data)
        
        logging.info(f'Migration completed: {results}')
        
    except Exception as e:
        logging.error(f'Migration failed: {e}')
        raise

# function_app.py - Azure Functions setup
app = func.FunctionApp()

@app.schedule_trigger(schedule="0 0 * * *")  # Daily at midnight
def migration_job(timer: func.TimerRequest) -> None:
    main(timer)

7. Complete Starter Template

python

#!/usr/bin/env python3
"""
Dataverse SDK for Python - Complete Starter Template
"""

from azure.identity import DefaultAzureCredential
from PowerPlatform.Dataverse.client import DataverseClient
from PowerPlatform.Dataverse.core.config import DataverseConfig
from PowerPlatform.Dataverse.core.errors import DataverseError
import logging

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

class DataverseApp:
    """Base class for Dataverse applications."""
    
    def __init__(self, org_url: str):
        self.org_url = org_url
        self.client = self._create_client()
    
    def _create_client(self) -> DataverseClient:
        """Create authenticated client."""
        cfg = DataverseConfig()
        cfg.logging_enable = False
        
        return DataverseClient(
            base_url=self.org_url,
            credential=DefaultAzureCredential(),
            config=cfg
        )
    
    def create_account(self, name: str, phone: str = None) -> str:
        """Create account record."""
        try:
            payload = {"name": name}
            if phone:
                payload["telephone1"] = phone
            
            id = self.client.create("account", payload)[0]
            logger.info(f"Created account: {id}")
            return id
        
        except DataverseError as e:
            logger.error(f"Failed to create account: {e.message}")
            raise
    
    def get_accounts(self, filter_expr: str = None, top: int = 100) -> list:
        """Get account records."""
        try:
            accounts = self.client.get(
                "account",
                filter=filter_expr,
                select=["accountid", "name", "telephone1", "createdon"],
                orderby=["createdon desc"],
                top=top
            )
            
            all_accounts = []
            for page in accounts:
                all_accounts.extend(page)
            
            logger.info(f"Retrieved {len(all_accounts)} accounts")
            return all_accounts
        
        except DataverseError as e:
            logger.error(f"Failed to get accounts: {e.message}")
            raise
    
    def update_account(self, account_id: str, **kwargs) -> None:
        """Update account record."""
        try:
            self.client.update("account", account_id, kwargs)
            logger.info(f"Updated account: {account_id}")
        
        except DataverseError as e:
            logger.error(f"Failed to update account: {e.message}")
            raise

if __name__ == "__main__":
    # Usage
    app = DataverseApp("https://myorg.crm.dynamics.com")
    
    # Create
    account_id = app.create_account("Acme Inc", "555-0100")
    
    # Get
    accounts = app.get_accounts(filter_expr="statecode eq 0", top=50)
    print(f"Found {len(accounts)} active accounts")
    
    # Update
    app.update_account(account_id, telephone1="555-0199")

8. See Also

Dataverse SDK for Python — Real-World Use Cases & Templates

Based on official Dataverse data migration and integration patterns.

1. Data Migration from Legacy Systems

Migration Architecture

text

Legacy System → Staging Database → Dataverse
    (Extract)    (Transform)        (Load)

Complete Migration Example

python

import pandas as pd
import time
from PowerPlatform.Dataverse.client import DataverseClient
from PowerPlatform.Dataverse.core.errors import DataverseError
from azure.identity import DefaultAzureCredential

class DataMigrationPipeline:
    """Migrate data from legacy system to Dataverse."""
    
    def __init__(self, org_url: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
        self.success_records = []
        self.failed_records = []
    
    def extract_from_legacy(self, legacy_db_connection, query: str):
        """Extract data from source system."""
        return pd.read_sql(query, legacy_db_connection)
    
    def transform_accounts(self, df: pd.DataFrame) -> list:
        """Transform source data to Dataverse schema."""
        payloads = []
        
        for _, row in df.iterrows():
            # Map source fields to Dataverse
            payload = {
                "name": row["company_name"][:100],  # Limit to 100 chars
                "telephone1": row["phone"],
                "websiteurl": row["website"],
                "revenue": float(row["annual_revenue"]) if row["annual_revenue"] else None,
                "numberofemployees": int(row["employees"]) if row["employees"] else None,
                # Track source ID for reconciliation
                "new_sourcecompanyid": str(row["legacy_id"]),
                "new_importsequencenumber": row["legacy_id"]
            }
            payloads.append(payload)
        
        return payloads
    
    def load_to_dataverse(self, payloads: list, batch_size: int = 200):
        """Load data to Dataverse with error tracking."""
        total = len(payloads)
        
        for i in range(0, total, batch_size):
            batch = payloads[i:i + batch_size]
            
            try:
                ids = self.client.create("account", batch)
                self.success_records.extend(ids)
                print(f"✓ Created {len(ids)} records ({len(self.success_records)}/{total})")
                
                # Prevent rate limiting
                time.sleep(0.5)
                
            except DataverseError as e:
                self.failed_records.extend(batch)
                print(f"✗ Batch failed: {e.message}")
    
    def reconcile_migration(self, df: pd.DataFrame):
        """Verify migration and track results."""
        
        # Query created records
        created_accounts = self.client.get(
            "account",
            filter="new_importsequencenumber ne null",
            select=["accountid", "new_sourcecompanyid", "new_importsequencenumber"],
            top=10000
        )
        
        created_df = pd.DataFrame(list(created_accounts))
        
        # Update source table with Dataverse IDs
        merged = df.merge(
            created_df,
            left_on="legacy_id",
            right_on="new_importsequencenumber"
        )
        
        print(f"Successfully migrated {len(merged)} accounts")
        print(f"Failed: {len(self.failed_records)} records")
        
        return {
            "total_source": len(df),
            "migrated": len(merged),
            "failed": len(self.failed_records),
            "success_rate": len(merged) / len(df) * 100
        }

# Usage
pipeline = DataMigrationPipeline("https://myorg.crm.dynamics.com")

# Extract
source_data = pipeline.extract_from_legacy(
    legacy_connection,
    "SELECT id, company_name, phone, website, annual_revenue, employees FROM companies"
)

# Transform
payloads = pipeline.transform_accounts(source_data)

# Load
pipeline.load_to_dataverse(payloads, batch_size=300)

# Reconcile
results = pipeline.reconcile_migration(source_data)
print(results)

2. Data Quality & Deduplication Agent

Detect and Merge Duplicates

python

from PowerPlatform.Dataverse.client import DataverseClient
from azure.identity import DefaultAzureCredential
import difflib

class DataQualityAgent:
    """Monitor and improve data quality."""
    
    def __init__(self, org_url: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
    
    def find_potential_duplicates(self, table_name: str, match_fields: list):
        """Find potential duplicate records."""
        
        records = []
        for page in self.client.get(table_name, select=match_fields, top=10000):
            records.extend(page)
        
        duplicates = []
        seen = {}
        
        for record in records:
            # Create key from match fields
            key = tuple(
                record.get(field, "").lower().strip() 
                for field in match_fields
            )
            
            if key in seen and key != ("",) * len(match_fields):
                duplicates.append({
                    "original": seen[key],
                    "duplicate": record,
                    "fields_matched": match_fields
                })
            else:
                seen[key] = record
        
        return duplicates, len(records)
    
    def merge_records(self, table_name: str, primary_id: str, duplicate_id: str, 
                     mapping: dict):
        """Merge duplicate record into primary."""
        
        # Copy data from duplicate to primary
        updates = {}
        duplicate = self.client.get(table_name, duplicate_id)
        
        for source_field, target_field in mapping.items():
            if duplicate.get(source_field) and not primary.get(target_field):
                updates[target_field] = duplicate[source_field]
        
        # Update primary
        if updates:
            self.client.update(table_name, primary_id, updates)
        
        # Delete duplicate
        self.client.delete(table_name, duplicate_id)
        
        return f"Merged {duplicate_id} into {primary_id}"
    
    def generate_quality_report(self, table_name: str) -> dict:
        """Generate data quality metrics."""
        
        records = list(self.client.get(table_name, top=10000))
        
        report = {
            "table": table_name,
            "total_records": len(records),
            "null_values": {},
            "duplicates": 0,
            "completeness_score": 0
        }
        
        # Check null values
        all_fields = set()
        for record in records:
            all_fields.update(record.keys())
        
        for field in all_fields:
            null_count = sum(1 for r in records if not r.get(field))
            completeness = (len(records) - null_count) / len(records) * 100
            
            if completeness < 100:
                report["null_values"][field] = {
                    "null_count": null_count,
                    "completeness": completeness
                }
        
        # Check duplicates
        duplicates, _ = self.find_potential_duplicates(
            table_name, 
            ["name", "emailaddress1"]
        )
        report["duplicates"] = len(duplicates)
        
        # Overall completeness
        avg_completeness = sum(
            100 - ((d["null_count"] / len(records)) * 100)
            for d in report["null_values"].values()
        ) / len(report["null_values"]) if report["null_values"] else 100
        report["completeness_score"] = avg_completeness
        
        return report

# Usage
agent = DataQualityAgent("https://myorg.crm.dynamics.com")

# Find duplicates
duplicates, total = agent.find_potential_duplicates(
    "account",
    match_fields=["name", "emailaddress1"]
)

print(f"Found {len(duplicates)} potential duplicates out of {total} accounts")

# Merge if confident
for dup in duplicates[:5]:  # Process top 5
    result = agent.merge_records(
        "account",
        primary_id=dup["original"]["accountid"],
        duplicate_id=dup["duplicate"]["accountid"],
        mapping={"telephone1": "telephone1", "websiteurl": "websiteurl"}
    )
    print(result)

# Quality report
report = agent.generate_quality_report("account")
print(f"Data Quality: {report['completeness_score']:.1f}%")

3. Contact & Account Enrichment

Enrich CRM Data from External Sources

python

import requests
from PowerPlatform.Dataverse.client import DataverseClient
from azure.identity import DefaultAzureCredential

class DataEnrichmentAgent:
    """Enrich CRM records with external data."""
    
    def __init__(self, org_url: str, external_api_key: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
        self.api_key = external_api_key
    
    def enrich_accounts_with_industry_data(self):
        """Enrich accounts with industry classification."""
        
        accounts = self.client.get(
            "account",
            select=["accountid", "name", "websiteurl"],
            filter="new_industrydata eq null",
            top=500
        )
        
        enriched_count = 0
        for page in accounts:
            for account in page:
                try:
                    # Call external API
                    industry = self._lookup_industry(account["name"])
                    
                    if industry:
                        self.client.update(
                            "account",
                            account["accountid"],
                            {"new_industrydata": industry}
                        )
                        enriched_count += 1
                
                except Exception as e:
                    print(f"Failed to enrich {account['name']}: {e}")
        
        return enriched_count
    
    def enrich_contacts_with_social_profiles(self):
        """Find and link social media profiles."""
        
        contacts = self.client.get(
            "contact",
            select=["contactid", "fullname", "emailaddress1"],
            filter="new_linkedinurl eq null",
            top=500
        )
        
        for page in contacts:
            for contact in page:
                try:
                    # Find social profiles
                    profiles = self._find_social_profiles(
                        contact["fullname"],
                        contact["emailaddress1"]
                    )
                    
                    if profiles:
                        self.client.update(
                            "contact",
                            contact["contactid"],
                            {
                                "new_linkedinurl": profiles.get("linkedin"),
                                "new_twitterhandle": profiles.get("twitter")
                            }
                        )
                
                except Exception as e:
                    print(f"Failed to enrich {contact['fullname']}: {e}")
    
    def _lookup_industry(self, company_name: str) -> str:
        """Call external industry API."""
        response = requests.get(
            "https://api.example.com/industry",
            params={"company": company_name},
            headers={"Authorization": f"Bearer {self.api_key}"}
        )
        
        if response.status_code == 200:
            return response.json().get("industry")
        return None
    
    def _find_social_profiles(self, name: str, email: str) -> dict:
        """Find social media profiles for person."""
        response = requests.get(
            "https://api.example.com/social",
            params={"name": name, "email": email},
            headers={"Authorization": f"Bearer {self.api_key}"}
        )
        
        if response.status_code == 200:
            return response.json()
        return {}

# Usage
enricher = DataEnrichmentAgent(
    "https://myorg.crm.dynamics.com",
    api_key="your-api-key"
)

enriched = enricher.enrich_accounts_with_industry_data()
print(f"Enriched {enriched} accounts")

4. Automated Report Data Export

Export CRM Data to Excel

python

import pandas as pd
from PowerPlatform.Dataverse.client import DataverseClient
from azure.identity import DefaultAzureCredential
from datetime import datetime

class ReportExporter:
    """Export Dataverse data to reports."""
    
    def __init__(self, org_url: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
    
    def export_sales_summary(self, output_file: str):
        """Export sales data for reporting."""
        
        accounts = []
        for page in self.client.get(
            "account",
            select=["accountid", "name", "revenue", "numberofemployees", 
                   "createdon", "modifiedon"],
            filter="statecode eq 0",  # Active only
            orderby=["revenue desc"],
            top=10000
        ):
            accounts.extend(page)
        
        # Opportunities
        opportunities = []
        for page in self.client.get(
            "opportunity",
            select=["opportunityid", "name", "estimatedvalue", 
                   "statuscode", "parentaccountid", "createdon"],
            top=10000
        ):
            opportunities.extend(page)
        
        # Create DataFrames
        df_accounts = pd.DataFrame(accounts)
        df_opportunities = pd.DataFrame(opportunities)
        
        # Generate report
        with pd.ExcelWriter(output_file) as writer:
            df_accounts.to_excel(writer, sheet_name="Accounts", index=False)
            df_opportunities.to_excel(writer, sheet_name="Opportunities", index=False)
            
            # Summary sheet
            summary = pd.DataFrame({
                "Metric": [
                    "Total Accounts",
                    "Total Opportunities",
                    "Total Revenue",
                    "Export Date"
                ],
                "Value": [
                    len(df_accounts),
                    len(df_opportunities),
                    df_accounts["revenue"].sum() if "revenue" in df_accounts else 0,
                    datetime.now().isoformat()
                ]
            })
            summary.to_excel(writer, sheet_name="Summary", index=False)
        
        return output_file
    
    def export_activity_log(self, days_back: int = 30) -> str:
        """Export recent activity for audit."""
        
        from_date = pd.Timestamp.now(tz='UTC') - pd.Timedelta(days=days_back)
        
        activities = []
        for page in self.client.get(
            "activitypointer",
            select=["activityid", "subject", "activitytypecode", 
                   "createdon", "ownerid"],
            filter=f"createdon gt {from_date.isoformat()}",
            orderby=["createdon desc"],
            top=10000
        ):
            activities.extend(page)
        
        df = pd.DataFrame(activities)
        output = f"activity_log_{datetime.now():%Y%m%d}.csv"
        df.to_csv(output, index=False)
        
        return output

# Usage
exporter = ReportExporter("https://myorg.crm.dynamics.com")
report_file = exporter.export_sales_summary("sales_report.xlsx")
print(f"Report saved to {report_file}")

5. Workflow Integration - Bulk Operations

Process Records Based on Conditions

python

from PowerPlatform.Dataverse.client import DataverseClient
from azure.identity import DefaultAzureCredential
from enum import IntEnum

class AccountStatus(IntEnum):
    PROSPECT = 1
    ACTIVE = 2
    CLOSED = 3

class BulkWorkflow:
    """Automate bulk operations."""
    
    def __init__(self, org_url: str):
        self.client = DataverseClient(
            base_url=org_url,
            credential=DefaultAzureCredential()
        )
    
    def mark_accounts_as_inactive_if_no_activity(self, days_no_activity: int = 90):
        """Deactivate accounts with no recent activity."""
        
        from_date = f"2025-{datetime.now().month:02d}-01T00:00:00Z"
        
        inactive_accounts = self.client.get(
            "account",
            select=["accountid", "name"],
            filter=f"modifiedon lt {from_date} and statecode eq 0",
            top=5000
        )
        
        accounts_to_deactivate = []
        for page in inactive_accounts:
            accounts_to_deactivate.extend([a["accountid"] for a in page])
        
        # Bulk update
        if accounts_to_deactivate:
            self.client.update(
                "account",
                accounts_to_deactivate,
                {"statecode": AccountStatus.CLOSED}
            )
            print(f"Deactivated {len(accounts_to_deactivate)} inactive accounts")
    
    def update_opportunity_status_based_on_amount(self):
        """Update opportunity stage based on estimated value."""
        
        opportunities = self.client.get(
            "opportunity",
            select=["opportunityid", "estimatedvalue"],
            filter="statuscode ne 7",  # Not closed
            top=5000
        )
        
        updates = []
        ids = []
        
        for page in opportunities:
            for opp in page:
                value = opp.get("estimatedvalue", 0)
                
                # Determine stage
                if value < 10000:
                    stage = 1  # Qualification
                elif value < 50000:
                    stage = 2  # Proposal
                else:
                    stage = 3  # Proposal Review
                
                updates.append({"stageid": stage})
                ids.append(opp["opportunityid"])
        
        # Bulk update
        if ids:
            self.client.update("opportunity", ids, updates)
            print(f"Updated {len(ids)} opportunities")

# Usage
workflow = BulkWorkflow("https://myorg.crm.dynamics.com")
workflow.mark_accounts_as_inactive_if_no_activity(days_no_activity=90)
workflow.update_opportunity_status_based_on_amount()

6. Scheduled Job Template

Azure Function for Scheduled Operations

python

# scheduled_migration_job.py
import azure.functions as func
from datetime import datetime
from DataMigrationPipeline import DataMigrationPipeline
import logging

def main(timer: func.TimerRequest) -> None:
    """Run migration job on schedule (e.g., daily)."""
    
    if timer.past_due:
        logging.info('The timer is past due!')
    
    try:
        logging.info(f'Migration job started at {datetime.utcnow()}')
        
        # Run migration
        pipeline = DataMigrationPipeline("https://myorg.crm.dynamics.com")
        
        # Extract, transform, load
        source_data = pipeline.extract_from_legacy(...)
        payloads = pipeline.transform_accounts(source_data)
        pipeline.load_to_dataverse(payloads)
        
        # Get results
        results = pipeline.reconcile_migration(source_data)
        
        logging.info(f'Migration completed: {results}')
        
    except Exception as e:
        logging.error(f'Migration failed: {e}')
        raise

# function_app.py - Azure Functions setup
app = func.FunctionApp()

@app.schedule_trigger(schedule="0 0 * * *")  # Daily at midnight
def migration_job(timer: func.TimerRequest) -> None:
    main(timer)

7. Complete Starter Template

python

#!/usr/bin/env python3
"""
Dataverse SDK for Python - Complete Starter Template
"""

from azure.identity import DefaultAzureCredential
from PowerPlatform.Dataverse.client import DataverseClient
from PowerPlatform.Dataverse.core.config import DataverseConfig
from PowerPlatform.Dataverse.core.errors import DataverseError
import logging

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

class DataverseApp:
    """Base class for Dataverse applications."""
    
    def __init__(self, org_url: str):
        self.org_url = org_url
        self.client = self._create_client()
    
    def _create_client(self) -> DataverseClient:
        """Create authenticated client."""
        cfg = DataverseConfig()
        cfg.logging_enable = False
        
        return DataverseClient(
            base_url=self.org_url,
            credential=DefaultAzureCredential(),
            config=cfg
        )
    
    def create_account(self, name: str, phone: str = None) -> str:
        """Create account record."""
        try:
            payload = {"name": name}
            if phone:
                payload["telephone1"] = phone
            
            id = self.client.create("account", payload)[0]
            logger.info(f"Created account: {id}")
            return id
        
        except DataverseError as e:
            logger.error(f"Failed to create account: {e.message}")
            raise
    
    def get_accounts(self, filter_expr: str = None, top: int = 100) -> list:
        """Get account records."""
        try:
            accounts = self.client.get(
                "account",
                filter=filter_expr,
                select=["accountid", "name", "telephone1", "createdon"],
                orderby=["createdon desc"],
                top=top
            )
            
            all_accounts = []
            for page in accounts:
                all_accounts.extend(page)
            
            logger.info(f"Retrieved {len(all_accounts)} accounts")
            return all_accounts
        
        except DataverseError as e:
            logger.error(f"Failed to get accounts: {e.message}")
            raise
    
    def update_account(self, account_id: str, **kwargs) -> None:
        """Update account record."""
        try:
            self.client.update("account", account_id, kwargs)
            logger.info(f"Updated account: {account_id}")
        
        except DataverseError as e:
            logger.error(f"Failed to update account: {e.message}")
            raise

if __name__ == "__main__":
    # Usage
    app = DataverseApp("https://myorg.crm.dynamics.com")
    
    # Create
    account_id = app.create_account("Acme Inc", "555-0100")
    
    # Get
    accounts = app.get_accounts(filter_expr="statecode eq 0", top=50)
    print(f"Found {len(accounts)} active accounts")
    
    # Update
    app.update_account(account_id, telephone1="555-0199")

Dataverse SDK for Python — Real-World Use Cases & Templates

Dataverse SDK for Python — Real-World Use Cases & Templates

1. Data Migration from Legacy Systems

Migration Architecture

Complete Migration Example

2. Data Quality & Deduplication Agent

Detect and Merge Duplicates

3. Contact & Account Enrichment

Enrich CRM Data from External Sources

4. Automated Report Data Export

Export CRM Data to Excel

5. Workflow Integration - Bulk Operations

Process Records Based on Conditions

6. Scheduled Job Template

Azure Function for Scheduled Operations

7. Complete Starter Template

8. See Also

Tags

Related Instructions

API Route Security

WordPress Development — Copilot Instructions

VueJS 3 Development Instructions

Update Code from Shorthand

TypeScript MCP Server Development

TypeScript Development

Dataverse SDK for Python — Real-World Use Cases & Templates

1. Data Migration from Legacy Systems

Migration Architecture

Complete Migration Example

2. Data Quality & Deduplication Agent

Detect and Merge Duplicates

3. Contact & Account Enrichment

Enrich CRM Data from External Sources

4. Automated Report Data Export

Export CRM Data to Excel

5. Workflow Integration - Bulk Operations

Process Records Based on Conditions

6. Scheduled Job Template

Azure Function for Scheduled Operations

7. Complete Starter Template

8. See Also