surfdataverse 2.0.1

A Python package for ionysis Microsoft Dataverse integration

SurfDataverse

A Python package for Microsoft Dataverse integration, providing a clean, object-oriented interface for connecting to, reading from, and writing to Microsoft Dataverse environments.

Features

• Easy Authentication: Simplified MSAL-based authentication with token caching
• Dynamic Table Class Generation: Automatically generates Python classes from Dataverse table metadata
• Type Safety: Built-in validation and error handling
• Configurable Prefixes: Support for custom table/column naming conventions
• Extensible: Easy to work with any Dataverse table structure

Quick Start

1. Configuration

Create a configuration JSON file with your Dataverse connection details:

{
    "authorityBase": "https://login.microsoftonline.com/",
    "tenantID": "your-tenant-id",
    "clientID": "your-client-id", 
    "environmentURI": "https://yourorg.crm.dynamics.com/",
    "scopeSuffix": "/.default"
}

2. Basic Usage

from surfdataverse import DataverseClient, DataverseTable
from pathlib import Path

# Initialize client
config_path = Path("connection_configs/your_config.json")
client = DataverseClient(config_path=config_path)

# Authenticate
client.get_authenticated_session()

# Test connection
client.test_connection()

# Create a table instance using the logical table name
product_table = DataverseTable("logical_table_name_1")

# Set data using dynamic properties (auto-generated from table metadata)
product_table.name = "My Product"
product_table.company = "My Company"
product_table.articlenr = "EXT-001"

# Write to Dataverse
guid = product_table.write_to_dataverse()
print(f"Created/updated product with GUID: {guid}")

Dynamic Table Class Generation

The core feature of SurfDataverse is its ability to automatically generate Python properties from Dataverse table metadata. When you create a DataverseTable instance, it:

1. Fetches table metadata from Dataverse
2. Analyzes column types (text, choice, lookup, file, etc.)
3. Creates dynamic properties with appropriate getters/setters
4. Handles type conversion automatically

Example: Working with Different Field Types

# Create table instances
product = DataverseTable("logical_table_name_1")
formula = DataverseTable("logical_table_name_2")

# Text properties (auto-generated from table metadata)
product.name = "Product Name"
product.company = "Company Name"

# Choice properties (option sets - converts text to numeric values)
formula.type = "Production"  # Automatically maps to numeric choice value

# Lookup properties (relationships to other tables)
formula.product = product.guid  # Links formula to product

# File properties (JSON data stored as files)
product.specifications = {
    "weight": 100,
    "dimensions": {"length": 50, "width": 30}
}

# Save changes
product.write_to_dataverse()
formula.write_to_dataverse()

Table Prefix Configuration

SurfDataverse supports custom table/column prefixes to work with different naming conventions:

# Default prefix (commonly "prefix_")
default_table = DataverseTable("prefix_tablename")

# Custom prefix
custom_table = DataverseTable("myorg_product", table_prefix="myorg_")

# The system automatically:
# - Filters columns starting with your prefix
# - Generates property names by removing the prefix
# - Handles relationships between tables with the same prefix

Data Retrieval

Fetch data from Dataverse tables as pandas DataFrames:

# Get table data as pandas DataFrame
df = client.get_table_data(logical_name="logical_table_name_1")

# Get specific record
entity_set_name = client.get_table_entity_set_name(logical_name="logical_table_name_1")
record = client.get_record(entity_set_name, "guid-here")

# Get table metadata
metadata = client.get_table_metadata("logical_table_name_1")

# Download multiple tables (optionally filtered by schema)
definitions, data, metadata = client.download_tables_as_df(schema_filter="prefix")

Property Types

The system automatically creates different property types based on Dataverse field metadata:

Data Properties

Simple text, numeric, and date fields:

table.name = "Some Value"
table.quantity = 100
table.price = 29.99

Choice Properties

Option set fields (automatically converts labels to/from numeric values):

table.status = "Active"  # Converts to numeric value
current_status = table.status  # Returns "Active" (readable label)

Lookup Properties

Relationships to other tables:

table.parent_record = "parent-guid-here"
table.related_item = related_table.guid

File Properties

Virtual file fields for storing complex data:

table.metadata = {
    "tags": ["important", "production"],
    "config": {"setting1": "value1"}
}

Error Handling

The package provides comprehensive error handling:

from surfdataverse import (
    AuthenticationError,
    ConnectionError, 
    DataverseAPIError,
    EntityError,
    ValidationError
)

try:
    client.get_authenticated_session()
    table.write_to_dataverse()
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except DataverseAPIError as e:
    print(f"API error (status {e.status_code}): {e}")
except ValidationError as e:
    print(f"Data validation error: {e}")

Advanced Usage

Manual Property Creation

For custom scenarios, you can create properties manually:

from surfdataverse import DataverseTable

# Extend the base class
class CustomTable(DataverseTable):
    def __init__(self, logical_name, prefix="prefix_"):
        super().__init__(logical_name, table_prefix=prefix)
    
    # Add custom business logic
    def validate_data(self):
        if not self.name:
            raise ValueError("Name is required")
        return True

Batch Operations

Work with multiple records efficiently:

# Create multiple records
products = []
for i in range(10):
    product = DataverseTable("logical_table_name_1")
    product.name = f"Product {i}"
    product.company = "ACME Corp"
    products.append(product)

# Write all records
for product in products:
    product.write_to_dataverse()

Session Management

The DataverseClient uses a singleton pattern for connection management:

# First initialization
client1 = DataverseClient(config_path="config1.json")

# Later access (returns same instance)  
client2 = DataverseClient()  # Same as client1

Project Structure

surfdataverse/
├── __init__.py          # Package initialization
├── core.py              # Core client and table classes
└── exceptions.py        # Custom exceptions

examples/
├── basic_usage.py       # Basic usage examples
├── example_auto_usage.py # Auto-generation examples
└── schema_visualization.py # Schema analysis tools

connection_configs/      # Configuration files (not tracked)
├── dev.json
└── production.json

Dependencies

• msal: Microsoft Authentication Library
• requests: HTTP client
• pandas: Data manipulation and analysis

Testing

python -m pytest tests/ -v

Code Style

uv run ruff format src/
uv run ruff check src/

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

For issues and questions:

• GitHub Issues: https://github.com/FriedemannHeinz/SurfDataverse/issues
• Documentation: This README and inline code documentation