PowerPlatform-Dataverse-Client 0.1.0b3

Python SDK for Microsoft Dataverse

PowerPlatform Dataverse Client for Python

A Python client library for Microsoft Dataverse that provides a unified interface for CRUD operations, SQL queries, table metadata management, and file uploads through the Dataverse Web API.

Source code | Package (PyPI) | API reference documentation | Product documentation | Samples

[!IMPORTANT] This library is currently in preview. Preview versions are provided for early access to new features and may contain breaking changes.

Table of contents

• Key features
• Getting started
  • Prerequisites
  • Install the package
  • Authenticate the client
• Key concepts
• Examples
  • Quick start
  • Basic CRUD operations
  • Bulk operations
  • Query data
  • Table management
  • File operations
• Next steps
• Troubleshooting
• Contributing

Key features

• 🔄 CRUD Operations: Create, read, update, and delete records with support for bulk operations and automatic retry
• ⚡ True Bulk Operations: Automatically uses Dataverse's native CreateMultiple, UpdateMultiple, and BulkDelete Web API operations for maximum performance and transactional integrity
• 📊 SQL Queries: Execute read-only SQL queries via the Dataverse Web API ?sql= parameter
• 🏗️ Table Management: Create, inspect, and delete custom tables and columns programmatically
• 📎 File Operations: Upload files to Dataverse file columns with automatic chunking for large files
• 🔐 Azure Identity: Built-in authentication using Azure Identity credential providers with comprehensive support
• 🛡️ Error Handling: Structured exception hierarchy with detailed error context and retry guidance

Getting started

Prerequisites

• Python 3.10+ (3.10, 3.11, 3.12, 3.13 supported)
• Microsoft Dataverse environment with appropriate permissions
• OAuth authentication configured for your application

Install the package

Install the PowerPlatform Dataverse Client using pip:

# Install the latest stable release
pip install PowerPlatform-Dataverse-Client

For development from source:

git clone https://github.com/microsoft/PowerPlatform-DataverseClient-Python.git
cd PowerPlatform-DataverseClient-Python
pip install -e .

Authenticate the client

The client requires any Azure Identity TokenCredential implementation for OAuth authentication with Dataverse:

from azure.identity import (
    InteractiveBrowserCredential, 
    ClientSecretCredential,
    ClientCertificateCredential,
    AzureCliCredential
)
from PowerPlatform.Dataverse.client import DataverseClient

# Development options
credential = InteractiveBrowserCredential()  # Browser authentication
# credential = AzureCliCredential()          # If logged in via 'az login'

# Production options  
# credential = ClientSecretCredential(tenant_id, client_id, client_secret)
# credential = ClientCertificateCredential(tenant_id, client_id, cert_path)

client = DataverseClient("https://yourorg.crm.dynamics.com", credential)

Complete authentication setup: See Use OAuth with Dataverse for app registration, all credential types, and security configuration.

Key concepts

The SDK provides a simple, pythonic interface for Dataverse operations:

Concept	Description
DataverseClient	Main entry point for all operations with environment connection
Records	Dataverse records represented as Python dictionaries with column schema names
Schema names	Use table schema names ("account", "new_MyTestTable") and column schema names ("name", "new_MyTestColumn"). See: Table definitions in Microsoft Dataverse
Bulk Operations	Efficient bulk processing for multiple records with automatic optimization
Paging	Automatic handling of large result sets with iterators
Structured Errors	Detailed exception hierarchy with retry guidance and diagnostic information
Customization prefix values	Custom tables and columns require a customization prefix value to be included for all operations (e.g., "new_MyTestTable", not "MyTestTable"). See: Table definitions in Microsoft Dataverse

Examples

Quick start

from azure.identity import InteractiveBrowserCredential
from PowerPlatform.Dataverse.client import DataverseClient

# Connect to Dataverse
credential = InteractiveBrowserCredential()
client = DataverseClient("https://yourorg.crm.dynamics.com", credential)

# Create a contact
contact_id = client.create("contact", {"firstname": "John", "lastname": "Doe"})[0]

# Read the contact back
contact = client.get("contact", contact_id, select=["firstname", "lastname"])
print(f"Created: {contact['firstname']} {contact['lastname']}")

# Clean up
client.delete("contact", contact_id)

Basic CRUD operations

# Create a record
account_ids = client.create("account", {"name": "Contoso Ltd"})
account_id = account_ids[0]

# Read a record
account = client.get("account", account_id)
print(account["name"])

# Update a record
client.update("account", account_id, {"telephone1": "555-0199"})

# Delete a record
client.delete("account", account_id)

Bulk operations

# Bulk create
payloads = [
    {"name": "Company A"},
    {"name": "Company B"},
    {"name": "Company C"}
]
ids = client.create("account", payloads)

# Bulk update (broadcast same change to all)
client.update("account", ids, {"industry": "Technology"})

# Bulk delete
client.delete("account", ids, use_bulk_delete=True)

Query data

# SQL query (read-only)
results = client.query_sql(
    "SELECT TOP 10 accountid, name FROM account WHERE statecode = 0"
)
for record in results:
    print(record["name"])

# OData query with paging
# Note: filter and expand parameters are case sensitive
pages = client.get(
    "account",
    select=["accountid", "name"],  # select is case-insensitive (automatically lowercased)
    filter="statecode eq 0",       # filter must use lowercase logical names (not transformed)
    top=100
)
for page in pages:
    for record in page:
        print(record["name"])

# Query with navigation property expansion (case-sensitive!)
pages = client.get(
    "account",
    select=["name"],
    expand=["primarycontactid"],  # Navigation property names are case-sensitive
    filter="statecode eq 0"       # Column names must be lowercase logical names
)
for page in pages:
    for account in page:
        contact = account.get("primarycontactid", {})
        print(f"{account['name']} - Contact: {contact.get('fullname', 'N/A')}")

Important: When using filter and expand parameters:

• filter: Column names must use exact lowercase logical names (e.g., "statecode eq 0", not "StateCode eq 0")
• expand: Navigation property names are case-sensitive and must match the exact server names
• select and orderby: Case-insensitive; automatically converted to lowercase

Table management

# Create a custom table, including the customization prefix value in the schema names for the table and columns.
table_info = client.create_table("new_Product", {
    "new_Code": "string",
    "new_Price": "decimal", 
    "new_Active": "bool"
})

# Create with custom primary column name and solution assignment
table_info = client.create_table(
    table_schema_name="new_Product",
    columns={
        "new_Code": "string",
        "new_Price": "decimal"
    },
    solution_unique_name="MyPublisher",  # Optional: add to specific solution
    primary_column_schema_name="new_ProductName"  # Optional: custom primary column (default is "{customization prefix value}_Name")
)

# Add columns to existing table (columns must include customization prefix value)
client.create_columns("new_Product", {"new_Category": "string"})

# Remove columns
client.delete_columns("new_Product", ["new_Category"])

# Clean up
client.delete_table("new_Product")

Important: All custom column names must include the customization prefix value (e.g., "new_"). This ensures explicit, predictable naming and aligns with Dataverse metadata requirements.

File operations

# Upload a file to a record
client.upload_file(
    table_schema_name="account",
    record_id=account_id,
    file_name_attribute="new_document",
    path="/path/to/document.pdf"
)

Next steps

More sample code

Explore our comprehensive examples in the examples/ directory:

🌱 Getting Started:

• Installation & Setup - Validate installation and basic usage patterns
• Functional Testing - Test core functionality in your environment

🚀 Advanced Usage:

• Complete Walkthrough - Full feature demonstration with production patterns
• File Upload - Upload files to Dataverse file columns

📖 See the examples README for detailed guidance and learning progression.

Additional documentation

For comprehensive information on Microsoft Dataverse and related technologies:

Resource	Description
Dataverse Developer Guide	Complete developer documentation for Microsoft Dataverse
Dataverse Web API Reference	Detailed Web API reference and examples
Azure Identity for Python	Authentication library documentation and credential types
Power Platform Developer Center	Broader Power Platform development resources
Dataverse SDK for .NET	Official .NET SDK for Microsoft Dataverse

Troubleshooting

General

The client raises structured exceptions for different error scenarios:

from PowerPlatform.Dataverse.client import DataverseClient
from PowerPlatform.Dataverse.core.errors import HttpError, ValidationError

try:
    client.get("account", "invalid-id")
except HttpError as e:
    print(f"HTTP {e.status_code}: {e.message}")
    print(f"Error code: {e.code}")
    print(f"Subcode: {e.subcode}")
    if e.is_transient:
        print("This error may be retryable")
except ValidationError as e:
    print(f"Validation error: {e.message}")

Authentication issues

Common fixes:

• Verify environment URL format: https://yourorg.crm.dynamics.com (no trailing slash)
• Ensure Azure Identity credentials have proper Dataverse permissions
• Check app registration permissions are granted and admin-consented

Performance considerations

For optimal performance in production environments:

Best Practice	Description
Bulk Operations	Pass lists to create(), update(), and delete() for automatic bulk processing
Select Fields	Specify select parameter to limit returned columns and reduce payload size
Page Size Control	Use top and page_size parameters to control memory usage
Connection Reuse	Reuse DataverseClient instances across operations
Production Credentials	Use ClientSecretCredential or ClientCertificateCredential for unattended operations
Error Handling	Implement retry logic for transient errors (e.is_transient)

Limitations

• SQL queries are read-only and support a limited subset of SQL syntax
• Create Table supports a limited number of column types. Lookup columns are not yet supported.
• Creating relationships between tables is not yet supported.
• File uploads are limited by Dataverse file size restrictions (default 128MB per file)

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit Contributor License Agreements.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.