Microsoft ODBC Driver for Microsoft Fabric Data Engineering (Preview)

Important

This feature is in preview.

ODBC (Open Database Connectivity) is a widely adopted standard that enables client applications to connect to and work with data from databases and big data platforms.

The Microsoft ODBC Driver for Fabric Data Engineering lets you connect, query, and manage Spark workloads in Microsoft Fabric with the reliability and simplicity of the ODBC standard. Built on Microsoft Fabric's Livy APIs, the driver provides secure and flexible Spark SQL connectivity to your .NET, Python, and other ODBC-compatible applications and BI tools.

Key Features

ODBC 3.x Compliant: Full implementation of ODBC 3.x specification
Microsoft Entra ID Authentication: Multiple authentication flows including Azure CLI, interactive, client credentials, certificate-based, and access token authentication
Spark SQL Query Support: Direct execution of Spark SQL statements
Comprehensive Data Type Support: Support for all Spark SQL data types including complex types (ARRAY, MAP, STRUCT)
Session Reuse: Built-in session management for improved performance
Large Table Support: Optimized handling for large result sets with configurable page sizes
Async Prefetch: Background data loading for improved performance
Proxy Support: HTTP proxy configuration for enterprise environments
Multi-Schema Lakehouse Support: Connect to specific schema within a Lakehouse

Note

In open-source Apache Spark, database and schema are used synonymously. For example, running SHOW SCHEMAS or SHOW DATABASES in a Fabric Notebook returns the same result — a list of all schemas in the Lakehouse.

Prerequisites

Before using the Microsoft ODBC Driver for Microsoft Fabric Data Engineering, ensure you have:

Operating System: Windows 10/11 or Windows Server 2016+
Microsoft Fabric Access: Access to a Microsoft Fabric workspace
Azure Entra ID Credentials: Appropriate credentials for authentication
Workspace and Lakehouse IDs: GUID identifiers for your Fabric workspace and lakehouse
Azure CLI (optional): Required for Azure CLI authentication method

Download and MSI Installation

Microsoft ODBC Driver for Microsoft Fabric Data Engineering version 1.0.0 is in public preview which you can download from this download center link.

Download Microsoft ODBC Driver for Microsoft Fabric Data Engineering (zip)

Download the Microsoft ODBC Driver for Microsoft Fabric Data Engineering MSI package
Double-click MicrosoftFabricODBCDriver-1.0.msi
Follow the installation wizard and accept the license agreement
Choose installation directory (default: C:\Program Files\Microsoft ODBC Driver for Microsoft Fabric Data Engineering\)
Complete the installation

Silent Installation

# Silent installation
msiexec /i "MicrosoftFabricODBCDriver-1.0.msi" /quiet

# Installation with logging
msiexec /i "MicrosoftFabricODBCDriver-1.0.msi" /l*v install.log

Verify Installation

After installation, verify the driver is registered:

Run odbcad32.exe (ODBC Data Source Administrator)
Navigate to the Drivers tab
Verify "Microsoft ODBC Driver for Microsoft Fabric Data Engineering" is listed

Quick Start Example

This example demonstrates how to connect to Microsoft Fabric and execute a query using the Microsoft ODBC Driver for Microsoft Fabric Data Engineering. Before running this code, ensure you have completed the prerequisites and installed the driver.

Python Example

import pyodbc

# Connection string with required parameters
connection_string = (
    "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};"
    "WorkspaceId=<workspace-id>;"
    "LakehouseId=<lakehouse-id>;"
    "AuthFlow=AZURE_CLI;"
)

# Connect and execute query
conn = pyodbc.connect(connection_string, timeout=30)
cursor = conn.cursor()

cursor.execute("SELECT 'Hello from Fabric!' as message")
row = cursor.fetchone()
print(row.message)

conn.close()

.NET Example

using System.Data.Odbc;

// Connection string with required parameters
string connectionString = 
    "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};" +
    "WorkspaceId=<workspace-id>;" +
    "LakehouseId=<lakehouse-id>;" +
    "AuthFlow=AZURE_CLI;";

using var connection = new OdbcConnection(connectionString);
await connection.OpenAsync();

Console.WriteLine("Connected successfully!");

using var command = new OdbcCommand("SELECT 'Hello from Fabric!' as message", connection);
using var reader = await command.ExecuteReaderAsync();

if (await reader.ReadAsync())
{
    Console.WriteLine(reader.GetString(0));
}

Connection String Format

Basic Connection String

The Microsoft ODBC Driver for Microsoft Fabric Data Engineering uses the following connection string format:

DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};<parameter1>=<value1>;<parameter2>=<value2>;...

Connection String Components

Component	Description	Example
DRIVER	ODBC driver identifier	`{Microsoft ODBC Driver for Microsoft Fabric Data Engineering}`
WorkspaceId	Microsoft Fabric workspace identifier (GUID)	`xxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxx`
LakehouseId	Microsoft Fabric lakehouse identifier (GUID)	`xxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxx`
AuthFlow	Authentication method	`AZURE_CLI`, `INTERACTIVE`, `CLIENT_CREDENTIAL`, `CLIENT_CERTIFICATE`, `ACCESS_TOKEN`

Example Connection Strings

Basic Connection (Azure CLI Authentication)

DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};WorkspaceId=<workspace-id>;LakehouseId=<lakehouse-id>;AuthFlow=AZURE_CLI

With Performance Options

DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};WorkspaceId=<workspace-id>;LakehouseId=<lakehouse-id>;AuthFlow=AZURE_CLI;ReuseSession=true;LargeTableSupport=true;PageSizeBytes=18874368

With Logging

DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};WorkspaceId=<workspace-id>;LakehouseId=<lakehouse-id>;AuthFlow=AZURE_CLI;LogLevel=DEBUG;LogFile=odbc_driver.log

Authentication

The Microsoft ODBC Driver for Microsoft Fabric Data Engineering supports multiple authentication methods through Microsoft Entra ID (formerly Azure Active Directory). Authentication is configured using the AuthFlow parameter in the connection string.

Authentication Methods

AuthFlow Value	Description
`AZURE_CLI`	Development using Azure CLI credentials
`INTERACTIVE`	Interactive browser-based authentication
`CLIENT_CREDENTIAL`	Service principal with client secret
`CLIENT_CERTIFICATE`	Service principal with certificate
`ACCESS_TOKEN`	Pre-acquired bearer access token

Azure CLI Authentication

Best for: Development and interactive applications

# Python Example
connection_string = (
    "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};"
    "WorkspaceId=<workspace-id>;"
    "LakehouseId=<lakehouse-id>;"
    "AuthFlow=AZURE_CLI;"
    "Scope=https://api.fabric.microsoft.com/.default;"
)
conn = pyodbc.connect(connection_string)

Prerequisites:

Azure CLI installed: az --version
Logged in: az login

Interactive Browser Authentication

Best for: User-facing applications

# Python Example
connection_string = (
    "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};"
    "WorkspaceId=<workspace-id>;"
    "LakehouseId=<lakehouse-id>;"
    "AuthFlow=INTERACTIVE;"
    "TenantId=<tenant-id>;"
    "Scope=https://api.fabric.microsoft.com/.default;"
)
conn = pyodbc.connect(connection_string)

Behavior:

Opens a browser window for user authentication
Credentials are cached for subsequent connections

Client Credentials (Service Principal) Authentication

Best for: Automated services and background jobs

connection_string = (
    "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};"
    "WorkspaceId=<workspace-id>;"
    "LakehouseId=<lakehouse-id>;"
    "AuthFlow=CLIENT_CREDENTIAL;"
    f"TenantId={tenant_id};"
    f"ClientId={client_id};"
    f"ClientSecret={client_secret};"
)

Required Parameters

TenantId: Azure tenant ID
ClientId: Application (client) ID from Microsoft Entra ID
ClientSecret: Client secret from Microsoft Entra ID

Best Practices

Store secrets securely (Azure Key Vault, environment variables)
Use managed identities when possible
Rotate secrets regularly

Certificate-Based Authentication

Best for: Enterprise applications requiring certificate-based authentication

connection_string = (
    "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};"
    "WorkspaceId=<workspace-id>;"
    "LakehouseId=<lakehouse-id>;"
    "AuthFlow=CLIENT_CERTIFICATE;"
    "TenantId=<tenant-id>;"
    "ClientId=<client-id>;"
    "CertificatePath=C:\\certs\\mycert.pfx;"
    "CertificatePassword=<password>;"
)

Required Parameters:

TenantId: Azure tenant ID
ClientId: Application (client) ID
CertificatePath: Path to PFX/PKCS12 certificate file
CertificatePassword: Certificate password

Access Token Authentication

Best for: Custom authentication scenarios

# Acquire token through custom mechanism
access_token = acquire_token_from_custom_source()

connection_string = (
    "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};"
    "WorkspaceId=<workspace-id>;"
    "LakehouseId=<lakehouse-id>;"
    "AuthFlow=ACCESS_TOKEN;"
    f"AccessToken={access_token};"
)

Configuration Parameters

Required Parameters

These parameters must be present in every connection string:

Parameter	Type	Description	Example
WorkspaceId	UUID	Microsoft Fabric workspace identifier	`4bbf89a8-...`
LakehouseId	UUID	Microsoft Fabric lakehouse identifier	`d8faa650-...`
AuthFlow	String	Authentication flow type	`AZURE_CLI`

Optional Parameters

Connection Settings

Parameter	Type	Default	Description
Database	String	None	Specific database to connect to
Scope	String	`https://api.fabric.microsoft.com/.default`	OAuth scope

Performance Settings

Parameter	Type	Default	Description
ReuseSession	Boolean	`true`	Reuse existing Spark session
LargeTableSupport	Boolean	`false`	Enable optimizations for large result sets
EnableAsyncPrefetch	Boolean	`false`	Enable background data prefetching
PageSizeBytes	Integer	`18874368` (18 MB)	Page size for result pagination (1-18 MB)

Logging Settings

Parameter	Type	Default	Description
LogLevel	String	`INFO`	Log level: `TRACE`, `DEBUG`, `INFO`, `WARN`, `ERROR`
LogFile	String	`odbc_driver.log`	Log file path (absolute or relative)

Proxy Settings

Parameter	Type	Default	Description
UseProxy	Boolean	`false`	Enable proxy
ProxyHost	String	None	Proxy hostname
ProxyPort	Integer	None	Proxy port
ProxyUsername	String	None	Proxy authentication username
ProxyPassword	String	None	Proxy authentication password

DSN Configuration

Create a System DSN

Open ODBC Administrator
```
%SystemRoot%\System32\odbcad32.exe
```
Create New System DSN
- Go to "System DSN" tab
- Click "Add"
- Select "Microsoft ODBC Driver for Microsoft Fabric Data Engineering"
- Click "Finish"
Configure DSN Settings
- Data Source Name: Enter a unique name (e.g., FabricODBC)
- Description: Optional description
- Workspace ID: Your Fabric workspace GUID
- Lakehouse ID: Your Fabric lakehouse GUID
- Authentication: Select authentication method
- Configure additional settings as needed
Test Connection
- Click "Test Connection" to verify settings
- Click "OK" to save

Use DSN in Applications

# Python - Connect using DSN
conn = pyodbc.connect("DSN=FabricODBC")

// .NET - Connect using DSN
using var connection = new OdbcConnection("DSN=FabricODBC");
await connection.OpenAsync();

Usage Examples

Basic Connection and Query

Python

import pyodbc

def main():
    connection_string = (
        "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};"
        "WorkspaceId=<workspace-id>;"
        "LakehouseId=<lakehouse-id>;"
        "AuthFlow=AZURE_CLI;"
        "ReuseSession=true;"
    )
    
    conn = pyodbc.connect(connection_string, timeout=30)
    cursor = conn.cursor()
    
    print("Connected successfully!")
    
    # Show available tables
    print("\nAvailable tables:")
    cursor.execute("SHOW TABLES")
    for row in cursor.fetchall():
        print(f"  {row}")
    
    # Query data
    print("\nQuery results:")
    cursor.execute("SELECT * FROM employees LIMIT 10")
    
    # Print column names
    columns = [desc[0] for desc in cursor.description]
    print(f"Columns: {columns}")
    
    # Print rows
    for row in cursor.fetchall():
        print(row)
    
    conn.close()

if __name__ == "__main__":
    main()

.NET

using System.Data.Odbc;

class Program
{
    static async Task Main(string[] args)
    {
        string connectionString = 
            "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};" +
            "WorkspaceId=<workspace-id>;" +
            "LakehouseId=<lakehouse-id>;" +
            "AuthFlow=AZURE_CLI;" +
            "ReuseSession=true;";

        using var connection = new OdbcConnection(connectionString);
        await connection.OpenAsync();
        
        Console.WriteLine("Connected successfully!");

        // Show available tables
        Console.WriteLine("\nAvailable tables:");
        using (var cmd = new OdbcCommand("SHOW TABLES", connection))
        using (var reader = await cmd.ExecuteReaderAsync())
        {
            while (await reader.ReadAsync())
            {
                Console.WriteLine($"  {reader.GetString(0)}");
            }
        }

        // Query data
        Console.WriteLine("\nQuery results:");
        using (var cmd = new OdbcCommand("SELECT * FROM employees LIMIT 10", connection))
        using (var reader = await cmd.ExecuteReaderAsync())
        {
            // Print column names
            var columns = new List<string>();
            for (int i = 0; i < reader.FieldCount; i++)
            {
                columns.Add(reader.GetName(i));
            }
            Console.WriteLine($"Columns: {string.Join(", ", columns)}");

            // Print rows
            while (await reader.ReadAsync())
            {
                var values = new object[reader.FieldCount];
                reader.GetValues(values);
                Console.WriteLine(string.Join("\t", values));
            }
        }
    }
}

Working with Large Result Sets

import pyodbc

connection_string = (
    "DRIVER={Microsoft ODBC Driver for Microsoft Fabric Data Engineering};"
    "WorkspaceId=<workspace-id>;"
    "LakehouseId=<lakehouse-id>;"
    "AuthFlow=AZURE_CLI;"
    "LargeTableSupport=true;"
    "PageSizeBytes=18874368;"  # 18 MB pages
    "EnableAsyncPrefetch=1;"
)

conn = pyodbc.connect(connection_string)
cursor = conn.cursor()

# Execute large query
cursor.execute("SELECT * FROM large_table")

# Process in batches
row_count = 0
while True:
    rows = cursor.fetchmany(1000)  # Fetch 1000 rows at a time
    if not rows:
        break
    
    for row in rows:
        # Process row
        row_count += 1
        
    if row_count % 10000 == 0:
        print(f"Processed {row_count} rows")

print(f"Total rows processed: {row_count}")
conn.close()

Schema Discovery

import pyodbc

conn = pyodbc.connect(connection_string)
cursor = conn.cursor()

# List all tables
print("Tables in current default schema / database:")
cursor.execute("SHOW TABLES")
tables = cursor.fetchall()
for table in tables:
    print(f"  {table}")

# Describe table structure
print("\nTable structure for 'employees':")
cursor.execute("DESCRIBE employees")
for col in cursor.fetchall():
    print(f"  {col}")

# List schemas (for multi-schema Lakehouses)
print("\nAvailable schemas:")
cursor.execute("SHOW SCHEMAS")
for db in cursor.fetchall():
    print(f"  {db}")

conn.close()

Data Type Mapping

The driver maps Spark SQL data types to ODBC SQL types:

Spark SQL Type	ODBC SQL Type	C/C++ Type	Python Type	.NET Type
BOOLEAN	SQL_BIT	SQLCHAR	bool	bool
BYTE	SQL_TINYINT	SQLSCHAR	int	sbyte
SHORT	SQL_SMALLINT	SQLSMALLINT	int	short
INT	SQL_INTEGER	SQLINTEGER	int	int
LONG	SQL_BIGINT	SQLBIGINT	int	long
FLOAT	SQL_REAL	SQLREAL	float	float
DOUBLE	SQL_DOUBLE	SQLDOUBLE	float	double
DECIMAL	SQL_DECIMAL	SQLCHAR*	decimal.Decimal	decimal
STRING	SQL_VARCHAR	SQLCHAR*	str	string
VARCHAR(n)	SQL_VARCHAR	SQLCHAR*	str	string
CHAR(n)	SQL_CHAR	SQLCHAR*	str	string
BINARY	SQL_BINARY	SQLCHAR*	bytes	byte[]
DATE	SQL_TYPE_DATE	SQL_DATE_STRUCT	datetime.date	DateTime
TIMESTAMP	SQL_TYPE_TIMESTAMP	SQL_TIMESTAMP_STRUCT	datetime.datetime	DateTime
ARRAY	SQL_VARCHAR	SQLCHAR*	str (JSON)	string
MAP	SQL_VARCHAR	SQLCHAR*	str (JSON)	string
STRUCT	SQL_VARCHAR	SQLCHAR*	str (JSON)	string

BI Tool Integration

Microsoft Excel

Open Excel -> Data -> Get Data -> From Other Sources -> From ODBC
Select your configured DSN (e.g., FabricODBC)
Authenticate if prompted
Browse and select tables
Load data into Excel worksheet

Power BI Desktop

Open Power BI Desktop -> Get Data -> ODBC
Select your configured DSN
Browse data catalog and select tables
Transform data as needed
Create visualizations

SQL Server Management Studio (Linked Server)

-- Create linked server
EXEC sp_addlinkedserver 
    @server = 'FABRIC_LINKED_SERVER',
    @srvproduct = 'Microsoft Fabric',
    @provider = 'MSDASQL',
    @datasrc = 'FabricODBC'

-- Configure RPC
EXEC master.dbo.sp_serveroption 
    @server = N'FABRIC_LINKED_SERVER',
    @optname = N'rpc out',
    @optvalue = N'true';

-- Query via linked server
SELECT * FROM OPENQUERY(FABRIC_LINKED_SERVER, 'SHOW TABLES');
SELECT * FROM OPENQUERY(FABRIC_LINKED_SERVER, 'SELECT * FROM employees LIMIT 20');

-- Execute statements
EXEC('SELECT * FROM employees LIMIT 10') AT FABRIC_LINKED_SERVER;

Troubleshooting

Common Issues

Connection Failures

Problem: Cannot connect to Microsoft Fabric

Solutions:

Verify Workspace ID and Lakehouse ID are correct GUIDs
Check Azure CLI authentication: az account show
Ensure you have appropriate Fabric workspace permissions
Check network connectivity and proxy settings

Authentication Errors

Problem: Authentication fails with Azure CLI

Solutions:

Run az login to refresh credentials
Verify correct tenant: az account set --subscription <subscription-id>
Check token validity: az account get-access-token --resource https://api.fabric.microsoft.com

Query Timeouts

Problem: Queries timing out on large tables

Solutions:

Enable LargeTableSupport=true
Adjust PageSizeBytes for optimal chunk size
Enable async prefetch: EnableAsyncPrefetch=1
Use LIMIT clause to restrict result size

Enable Logging

For troubleshooting, enable detailed logging:

LogLevel=DEBUG;LogFile=C:\temp\odbc_driver_debug.log;

Log levels:

TRACE: Most verbose, includes all API calls
DEBUG: Detailed debugging information
INFO: General information (default)
WARN: Warnings only
ERROR: Errors only

ODBC Tracing

Enable Windows ODBC tracing for low-level diagnostics - and turn it off when not needed for optimal performance:

Open odbcad32.exe
Go to "Tracing" tab
Set trace file path (e.g., C:\temp\odbctrace.log)
Click "Start Tracing Now"
Reproduce the issue
Click "Stop Tracing Now"

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Last updated on 2026-02-18

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Microsoft ODBC Driver for Microsoft Fabric Data Engineering (Preview)

Key Features

Prerequisites

Download and MSI Installation

Silent Installation

Verify Installation

Quick Start Example

Python Example

.NET Example

Connection String Format

Basic Connection String

Connection String Components

Example Connection Strings

Basic Connection (Azure CLI Authentication)

With Performance Options

With Logging

Authentication

Authentication Methods

Azure CLI Authentication

Interactive Browser Authentication

Client Credentials (Service Principal) Authentication

Certificate-Based Authentication

Access Token Authentication

Configuration Parameters

Required Parameters

Optional Parameters

Connection Settings

Performance Settings

Logging Settings

Proxy Settings

DSN Configuration

Create a System DSN

Use DSN in Applications

Usage Examples

Basic Connection and Query

Python

.NET

Working with Large Result Sets

Schema Discovery

Data Type Mapping

BI Tool Integration

Microsoft Excel

Power BI Desktop

SQL Server Management Studio (Linked Server)

Troubleshooting

Common Issues

Connection Failures

Authentication Errors

Query Timeouts

Enable Logging

ODBC Tracing

Related Content

Feedback

Additional resources