name	dataverse-sdk-use
description	Guidance for using the PowerPlatform Dataverse Client Python SDK. Use when calling the SDK like creating CRUD operations, SQL queries, table metadata management, relationships, and upload files.

PowerPlatform Dataverse SDK Guide

Overview

Use the PowerPlatform Dataverse Client Python SDK to interact with Microsoft Dataverse.

Key Concepts

Schema Names vs Display Names

Standard tables: lowercase (e.g., "account", "contact")
Custom tables: include customization prefix (e.g., "new_Product", "cr123_Invoice")
Custom columns: include customization prefix (e.g., "new_Price", "cr123_Status")
ALWAYS use schema names (logical names), NOT display names

Operation Namespaces

client.records -- CRUD and OData queries
client.query -- query and search operations
client.tables -- table metadata, columns, and relationships
client.files -- file upload operations
client.batch -- batch multiple operations into a single HTTP request

Bulk Operations

The SDK supports Dataverse's native bulk operations: Pass lists to create(), update() for automatic bulk processing, for delete(), set use_bulk_delete when passing lists to use bulk operation

Paging

Control page size with page_size parameter
Use top parameter to limit total records returned

DataFrame Support

DataFrame operations are accessed via the client.dataframe namespace: client.dataframe.get(), client.dataframe.create(), client.dataframe.update(), client.dataframe.delete()

Common Operations

Import

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

Client Initialization

# Development options
credential = InteractiveBrowserCredential()
credential = AzureCliCredential()

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

# Create client with context manager (recommended -- enables HTTP connection pooling)
# No trailing slash on URL!
with DataverseClient("https://yourorg.crm.dynamics.com", credential) as client:
    ...  # all operations here
# Session closed, caches cleared automatically

# Or without context manager:
client = DataverseClient("https://yourorg.crm.dynamics.com", credential)

CRUD Operations

Create Records

# Single record
account_id = client.records.create("account", {"name": "Contoso Ltd", "telephone1": "555-0100"})

# Bulk create (uses CreateMultiple API automatically)
contacts = [
    {"firstname": "John", "lastname": "Doe"},
    {"firstname": "Jane", "lastname": "Smith"}
]
contact_ids = client.records.create("contact", contacts)

Read Records

# Get single record by ID
account = client.records.get("account", account_id, select=["name", "telephone1"])

# Query with filter (paginated)
for page in client.records.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 record in page:
        print(record["name"])

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

Create Records with Lookup Bindings (@odata.bind)

# Set lookup fields using @odata.bind with PascalCase navigation property names
# CORRECT: use the navigation property name (case-sensitive, must match $metadata)
guid = client.records.create("new_ticket", {
    "new_name": "TKT-001",
    "new_CustomerId@odata.bind": f"/new_customers({customer_id})",
    "new_AgentId@odata.bind": f"/new_agents({agent_id})",
})

# WRONG: lowercase navigation property causes 400 error
# "new_customerid@odata.bind" -> ODataException: undeclared property 'new_customerid'

Update Records

# Single update
client.records.update("account", account_id, {"telephone1": "555-0200"})

# Bulk update (broadcast same change to multiple records)
client.records.update("account", [id1, id2, id3], {"industry": "Technology"})

Upsert Records

Creates or updates records identified by alternate keys. Single item -> PATCH; multiple items -> UpsertMultiple bulk action.

Prerequisite: The table must have an alternate key configured in Dataverse for the columns used in alternate_key. Without it, Dataverse will reject the request with a 400 error.

from PowerPlatform.Dataverse.models.upsert import UpsertItem

# Single upsert
client.records.upsert("account", [
    UpsertItem(
        alternate_key={"accountnumber": "ACC-001"},
        record={"name": "Contoso Ltd", "telephone1": "555-0100"},
    )
])

# Bulk upsert (uses UpsertMultiple API automatically)
client.records.upsert("account", [
    UpsertItem(alternate_key={"accountnumber": "ACC-001"}, record={"name": "Contoso Ltd"}),
    UpsertItem(alternate_key={"accountnumber": "ACC-002"}, record={"name": "Fabrikam Inc"}),
])

# Composite alternate key
client.records.upsert("account", [
    UpsertItem(
        alternate_key={"accountnumber": "ACC-001", "address1_postalcode": "98052"},
        record={"name": "Contoso Ltd"},
    )
])

# Plain dict syntax (no import needed)
client.records.upsert("account", [
    {"alternate_key": {"accountnumber": "ACC-001"}, "record": {"name": "Contoso Ltd"}}
])

Delete Records

# Single delete
client.records.delete("account", account_id)

# Bulk delete (uses BulkDelete API)
client.records.delete("account", [id1, id2, id3], use_bulk_delete=True)

DataFrame Operations

The SDK provides DataFrame wrappers for all CRUD operations via the client.dataframe namespace, using pandas DataFrames and Series as input/output.

import pandas as pd

# Query records -- returns a single DataFrame
df = client.dataframe.get("account", filter="statecode eq 0", select=["name"])
print(f"Got {len(df)} rows")

# Limit results with top for large tables
df = client.dataframe.get("account", select=["name"], top=100)

# Fetch single record as one-row DataFrame
df = client.dataframe.get("account", record_id=account_id, select=["name"])

# Create records from a DataFrame (returns a Series of GUIDs)
new_accounts = pd.DataFrame([
    {"name": "Contoso", "telephone1": "555-0100"},
    {"name": "Fabrikam", "telephone1": "555-0200"},
])
new_accounts["accountid"] = client.dataframe.create("account", new_accounts)

# Update records from a DataFrame (id_column identifies the GUID column)
new_accounts["telephone1"] = ["555-0199", "555-0299"]
client.dataframe.update("account", new_accounts, id_column="accountid")

# Clear a field by setting clear_nulls=True (by default, NaN/None fields are skipped)
df = pd.DataFrame([{"accountid": "guid-1", "websiteurl": None}])
client.dataframe.update("account", df, id_column="accountid", clear_nulls=True)

# Delete records by passing a Series of GUIDs
client.dataframe.delete("account", new_accounts["accountid"])

SQL Queries

SQL queries are read-only and support limited SQL syntax. A single SELECT statement with optional WHERE, TOP (integer literal), ORDER BY (column names only), and a simple table alias after FROM is supported. But JOIN and subqueries may not be. Refer to the Dataverse documentation for the current feature set.

results = client.query.sql(
    "SELECT TOP 10 accountid, name FROM account WHERE statecode = 0"
)
for record in results:
    print(record["name"])

Table Management

Create Custom Tables

# Create table with columns (include customization prefix!)
table_info = client.tables.create(
    "new_Product",
    {
        "new_Code": "string",
        "new_Price": "decimal",
        "new_Active": "bool",
        "new_Quantity": "int",
    },
)

# With solution assignment and custom primary column
table_info = client.tables.create(
    "new_Product",
    {"new_Code": "string", "new_Price": "decimal"},
    solution="MyPublisher",
    primary_column="new_ProductCode",
)

Supported Column Types

Types on the same line map to the same exact format under the hood

"string" or "text" - Single line of text
"memo" or "multiline" - Multiple lines of text (4000 character default)
"int" or "integer" - Whole number
"decimal" or "money" - Decimal number
"float" or "double" - Floating point number
"bool" or "boolean" - Yes/No
"datetime" or "date" - Date
"file" - File column
Enum subclass - Local option set (picklist)

Manage Columns

# Add columns to existing table (must include customization prefix!)
client.tables.add_columns("new_Product", {
    "new_Category": "string",
    "new_InStock": "bool",
})

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

Inspect Tables

# Get single table information
table_info = client.tables.get("new_Product")
print(f"Logical name: {table_info['table_logical_name']}")
print(f"Entity set: {table_info['entity_set_name']}")

# List all tables
tables = client.tables.list()
for table in tables:
    print(table)

Delete Tables

client.tables.delete("new_Product")

Relationship Management

Create One-to-Many Relationship

from PowerPlatform.Dataverse.models.relationship import (
    LookupAttributeMetadata,
    OneToManyRelationshipMetadata,
    Label,
    LocalizedLabel,
    CascadeConfiguration,
)
from PowerPlatform.Dataverse.common.constants import CASCADE_BEHAVIOR_REMOVE_LINK

lookup = LookupAttributeMetadata(
    schema_name="new_DepartmentId",
    display_name=Label(
        localized_labels=[LocalizedLabel(label="Department", language_code=1033)]
    ),
)

relationship = OneToManyRelationshipMetadata(
    schema_name="new_Department_Employee",
    referenced_entity="new_department",
    referencing_entity="new_employee",
    referenced_attribute="new_departmentid",
    cascade_configuration=CascadeConfiguration(
        delete=CASCADE_BEHAVIOR_REMOVE_LINK,
    ),
)

result = client.tables.create_one_to_many_relationship(lookup, relationship)
print(f"Created lookup field: {result['lookup_schema_name']}")

Create Many-to-Many Relationship

from PowerPlatform.Dataverse.models.relationship import ManyToManyRelationshipMetadata

relationship = ManyToManyRelationshipMetadata(
    schema_name="new_employee_project",
    entity1_logical_name="new_employee",
    entity2_logical_name="new_project",
)

result = client.tables.create_many_to_many_relationship(relationship)
print(f"Created: {result['relationship_schema_name']}")

Convenience Method for Lookup Fields

result = client.tables.create_lookup_field(
    referencing_table="new_order",
    lookup_field_name="new_AccountId",
    referenced_table="account",
    display_name="Account",
    required=True,
)

Query and Delete Relationships

# Get relationship metadata
rel = client.tables.get_relationship("new_Department_Employee")
if rel:
    print(f"Found: {rel['SchemaName']}")

# Delete relationship
client.tables.delete_relationship(result["relationship_id"])

File Operations

# Upload file to a file column
client.files.upload(
    table="account",
    record_id=account_id,
    file_column="new_Document",  # If the file column doesn't exist, it will be created automatically
    path="/path/to/document.pdf",
)

Batch Operations

Use client.batch to send multiple operations in one HTTP request. All batch methods return None; results arrive via BatchResult after execute().

# Build a batch request
batch = client.batch.new()
batch.records.create("account", {"name": "Contoso"})
batch.records.update("account", account_id, {"telephone1": "555-0100"})
batch.records.get("account", account_id, select=["name"])
batch.query.sql("SELECT TOP 5 name FROM account")

result = batch.execute()
for item in result.responses:
    if item.is_success:
        print(f"[OK] {item.status_code} entity_id={item.entity_id}")
        if item.data:
            # GET responses populate item.data with the parsed JSON record
            print(item.data.get("name"))
    else:
        print(f"[ERR] {item.status_code}: {item.error_message}")

# Transactional changeset (all succeed or roll back)
with batch.changeset() as cs:
    ref = cs.records.create("contact", {"firstname": "Alice"})
    cs.records.update("account", account_id, {"primarycontactid@odata.bind": ref})

# Continue on error
result = batch.execute(continue_on_error=True)
print(f"Succeeded: {len(result.succeeded)}, Failed: {len(result.failed)}")

BatchResult properties:

result.responses -- list of BatchItemResponse in submission order
result.succeeded -- responses with 2xx status codes
result.failed -- responses with non-2xx status codes
result.has_errors -- True if any response failed
result.entity_ids -- GUIDs from OData-EntityId headers (creates and updates)

Batch limitations:

Maximum 1000 operations per batch
Paginated records.get() (without record_id) is not supported in batch
flush_cache() is not supported in batch

Error Handling

The SDK provides structured exceptions with detailed error information:

from PowerPlatform.Dataverse.core.errors import (
    DataverseError,
    HttpError,
    ValidationError,
    MetadataError,
    SQLParseError
)
from PowerPlatform.Dataverse.client import DataverseClient

try:
    client.records.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}")

Common Error Patterns

Authentication failures:

Check environment URL format (no trailing slash)
Verify credentials have Dataverse permissions
Ensure app registration is properly configured

404 Not Found:

Verify table schema name is correct (lowercase for standard tables)
Check record ID exists
Ensure using schema names, not display names
Cache issue could happen, so retry might help, especially for metadata creation

400 Bad Request:

Check filter/expand parameters use correct case
Verify column names exist and are spelled correctly
Ensure custom columns include customization prefix
For @odata.bind errors ("undeclared property"): the navigation property name before @odata.bind is case-sensitive and must match the entity's $metadata exactly (e.g., new_CustomerId@odata.bind for custom lookups, parentaccountid@odata.bind for system lookups). The SDK preserves @odata.bind key casing.

Best Practices

Performance Optimization

Use bulk operations - Pass lists to create/update/delete for automatic optimization
Specify select fields - Limit returned columns to reduce payload size
Control page size - Use top and page_size parameters appropriately
Reuse client instances - Don't create new clients for each operation
Use production credentials - ClientSecretCredential or CertificateCredential for unattended operations
Error handling - Implement retry logic for transient errors (e.is_transient)
Always include customization prefix for custom tables/columns
Use lowercase for column names, match $metadata for navigation properties - Column names in $select/$filter/record payloads use lowercase LogicalNames. Navigation properties in $expand and @odata.bind keys are case-sensitive and must match the entity's $metadata (PascalCase for custom lookups like new_CustomerId, lowercase for system lookups like parentaccountid)
Test in non-production environments first
Use named constants - Import cascade behavior constants from PowerPlatform.Dataverse.common.constants

Additional Resources

Load these resources as needed during development:

Key Reminders

Schema names are required - Never use display names
Custom tables need prefixes - Include customization prefix (e.g., "new_")
Filter is case-sensitive - Use lowercase logical names
Bulk operations are encouraged - Pass lists for optimization
No trailing slashes in URLs - Format: https://org.crm.dynamics.com
Structured errors - Check is_transient for retry logic

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