Microsoft Fabric 2025: Roadmap and New Capabilities

Microsoft Fabric has evolved significantly since its GA in November 2023. Let’s explore the 2025 roadmap and new capabilities that make Fabric the comprehensive data platform for the AI era.

Fabric’s 2025 Vision

Microsoft’s vision for Fabric in 2025 centers on:

1. AI-native analytics - Copilot everywhere
2. Real-time everything - Sub-second latency
3. Governance by default - Security built-in
4. Developer productivity - Modern tooling

New Capabilities for 2025

1. Enhanced Copilot Integration

Copilot is now available across all Fabric experiences:

# Using Fabric Copilot in notebooks
# Simply describe what you want in natural language

# %copilot
# "Load the sales data from the lakehouse,
# clean null values, calculate monthly trends,
# and create a visualization"

# Copilot generates:
from pyspark.sql import functions as F
import matplotlib.pyplot as plt

# Load data
df = spark.read.table("lakehouse.sales")

# Clean nulls
df_clean = df.dropna(subset=["amount", "date"])

# Calculate monthly trends
monthly = (df_clean
    .withColumn("month", F.date_trunc("month", "date"))
    .groupBy("month")
    .agg(F.sum("amount").alias("total_sales"))
    .orderBy("month"))

# Visualize
pdf = monthly.toPandas()
plt.figure(figsize=(12, 6))
plt.plot(pdf['month'], pdf['total_sales'])
plt.title('Monthly Sales Trend')
plt.xlabel('Month')
plt.ylabel('Total Sales')
plt.show()

2. Real-Time Intelligence GA

Real-Time Intelligence is now generally available with enhanced features:

# Create Eventstream via REST API, configure via portal
from azure.identity import DefaultAzureCredential
import requests

credential = DefaultAzureCredential()
token = credential.get_token("https://api.fabric.microsoft.com/.default").token
headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

workspace_id = "your-workspace-id"

# Create eventstream item
eventstream_payload = {
    "displayName": "sales_events",
    "type": "Eventstream",
    "description": "Real-time sales event processing"
}

response = requests.post(
    f"https://api.fabric.microsoft.com/v1/workspaces/{workspace_id}/items",
    headers=headers,
    json=eventstream_payload
)

# Note: Configure source (Event Hub), transformations, and destinations
# in the Fabric portal visual designer

# Query real-time data using KQL (in Fabric notebook or KQL Queryset)
kql_query = """
realtime_sales
| where processed_time > ago(1h)
| summarize total_sales = sum(amount) by bin(processed_time, 1m), region
| render timechart
"""

# Execute KQL via Azure Data Explorer SDK or Kusto Python client
from azure.kusto.data import KustoClient, KustoConnectionStringBuilder

cluster_uri = "https://<kql-database>.kusto.fabric.microsoft.com"
kcsb = KustoConnectionStringBuilder.with_aad_device_authentication(cluster_uri)
client = KustoClient(kcsb)
response = client.execute("sales_analytics", kql_query)

3. Data Warehouse Enhancements

-- New T-SQL capabilities in Fabric Data Warehouse

-- Automated statistics management
ALTER DATABASE SCOPED CONFIGURATION SET AUTO_CREATE_STATISTICS = ON;
ALTER DATABASE SCOPED CONFIGURATION SET AUTO_UPDATE_STATISTICS = ON;

-- Workload management
CREATE WORKLOAD GROUP high_priority
WITH (
    MIN_PERCENTAGE_RESOURCE = 30,
    MAX_PERCENTAGE_RESOURCE = 70,
    IMPORTANCE = HIGH
);

-- Query hints for optimization
SELECT /*+ QUERY_HINT(FORCE_HASH_JOIN) */
    c.customer_name,
    SUM(s.amount) as total_sales
FROM customers c
JOIN sales s ON c.customer_id = s.customer_id
GROUP BY c.customer_name;

-- Clone tables for development
CREATE TABLE sales_dev
AS CLONE OF sales
WITH (DATA_CONSISTENCY = EVENTUAL);

4. Lakehouse Improvements

# New Delta Lake 3.0 features in Fabric

from delta.tables import DeltaTable

# Liquid clustering (replaces partitioning and Z-ordering)
spark.sql("""
    CREATE TABLE lakehouse.sales_optimized
    CLUSTER BY (date, region)
    AS SELECT * FROM lakehouse.sales
""")

# Automatic optimization
spark.sql("""
    ALTER TABLE lakehouse.sales_optimized
    SET TBLPROPERTIES (
        'delta.autoOptimize.autoCompact' = 'true',
        'delta.autoOptimize.optimizeWrite' = 'true'
    )
""")

# UniForm for cross-engine compatibility
spark.sql("""
    ALTER TABLE lakehouse.sales_optimized
    SET TBLPROPERTIES (
        'delta.universalFormat.enabledFormats' = 'iceberg'
    )
""")

# Table now readable by Iceberg-compatible engines

5. Data Engineering Pipelines

# Create and manage pipelines via Fabric REST API
from azure.identity import DefaultAzureCredential
import requests
import json

credential = DefaultAzureCredential()
token = credential.get_token("https://api.fabric.microsoft.com/.default").token
headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
base_url = "https://api.fabric.microsoft.com/v1"

workspace_id = "your-workspace-id"

# Pipeline definition (created/managed via portal or REST API)
pipeline_config = {
    "displayName": "daily_sales_pipeline",
    "type": "DataPipeline",
    "description": "Daily sales data ingestion and transformation pipeline"
}

# Create pipeline item
response = requests.post(
    f"{base_url}/workspaces/{workspace_id}/items",
    headers=headers,
    json=pipeline_config
)
pipeline_id = response.json().get("id")

# Pipeline activities are configured in the pipeline definition
# This is typically done via Fabric portal visual designer
# Activities include: Copy, Notebook, Dataflow Gen2, Semantic Model Refresh

# Example: Trigger pipeline run via REST API
def run_pipeline(workspace_id: str, pipeline_id: str, parameters: dict = None):
    """Trigger a pipeline run."""
    payload = {"parameters": parameters} if parameters else {}

    response = requests.post(
        f"{base_url}/workspaces/{workspace_id}/items/{pipeline_id}/jobs/instances?jobType=Pipeline",
        headers=headers,
        json=payload
    )
    return response.json()

# Trigger the pipeline
run_result = run_pipeline(workspace_id, pipeline_id, {"date": "2025-01-10"})
print(f"Pipeline run ID: {run_result.get('id')}")

# Note: Schedule triggers and event-based triggers are configured
# in the Fabric portal under the pipeline's trigger settings

6. OneLake Enhancements

# Direct OneLake file access via REST API and ADLS Gen2 compatible endpoints
from azure.identity import DefaultAzureCredential
from azure.storage.filedatalake import DataLakeServiceClient
import requests

credential = DefaultAzureCredential()

# Option 1: Use ADLS Gen2 SDK with OneLake endpoint
onelake_account_url = "https://onelake.dfs.fabric.microsoft.com"
service_client = DataLakeServiceClient(account_url=onelake_account_url, credential=credential)

# Access lakehouse files
workspace_name = "your-workspace"
lakehouse_name = "your-lakehouse"
file_system_client = service_client.get_file_system_client(f"{workspace_name}/{lakehouse_name}.Lakehouse")

# List files
paths = file_system_client.get_paths(path="Files/")
for path in paths:
    print(f"  {path.name}")

# Read file
file_client = file_system_client.get_file_client("Files/config.json")
content = file_client.download_file().readall()

# Option 2: Create shortcuts via REST API
token = credential.get_token("https://api.fabric.microsoft.com/.default").token
headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

workspace_id = "target-workspace-id"
lakehouse_id = "target-lakehouse-id"

shortcut_payload = {
    "path": "Tables/shared_customers",
    "target": {
        "oneLake": {
            "workspaceId": "source-workspace-id",
            "itemId": "source-lakehouse-id",
            "path": "Tables/customers"
        }
    }
}

response = requests.post(
    f"https://api.fabric.microsoft.com/v1/workspaces/{workspace_id}/items/{lakehouse_id}/shortcuts",
    headers=headers,
    json=shortcut_payload
)

7. Governance and Security

# Enhanced Purview integration via REST APIs
from azure.identity import DefaultAzureCredential
import requests

credential = DefaultAzureCredential()
purview_account = "my-purview-account"

def get_purview_token():
    return credential.get_token("https://purview.azure.net/.default").token

def apply_sensitivity_label(asset_path: str, label: str):
    """Apply sensitivity label to a Fabric asset via Purview."""
    token = get_purview_token()
    headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

    # Get asset GUID from Purview
    search_payload = {"keywords": asset_path, "limit": 1}
    search_response = requests.post(
        f"https://{purview_account}.purview.azure.com/catalog/api/search/query",
        headers=headers,
        json=search_payload
    )
    asset_guid = search_response.json()["value"][0]["id"]

    # Apply label
    label_payload = {"typeName": "Microsoft_Information_Protection_Label", "attributes": {"label": label}}
    response = requests.put(
        f"https://{purview_account}.purview.azure.com/catalog/api/atlas/v2/entity/guid/{asset_guid}/labels",
        headers=headers,
        json=label_payload
    )
    return response.status_code

# Apply sensitivity labels
apply_sensitivity_label("workspace/lakehouse/Tables/customers", "Confidential")

# Data lineage tracking via Purview REST API
def get_lineage(asset_path: str) -> dict:
    """Get data lineage for an asset."""
    token = get_purview_token()
    headers = {"Authorization": f"Bearer {token}"}

    # Search for asset
    search_response = requests.post(
        f"https://{purview_account}.purview.azure.com/catalog/api/search/query",
        headers=headers,
        json={"keywords": asset_path, "limit": 1}
    )
    asset_guid = search_response.json()["value"][0]["id"]

    # Get lineage
    lineage_response = requests.get(
        f"https://{purview_account}.purview.azure.com/catalog/api/atlas/v2/lineage/{asset_guid}",
        headers=headers,
        params={"direction": "BOTH", "depth": 3}
    )
    return lineage_response.json()

lineage = get_lineage("workspace/report/sales_dashboard")
print(f"Lineage relations: {lineage.get('relations', [])}")

# Access policies via Purview Policy API
def create_access_policy(policy_name: str, scope: str, rules: list):
    """Create access policy in Purview."""
    token = get_purview_token()
    headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

    policy_payload = {
        "name": policy_name,
        "decisionRules": [{
            "effect": rule["access"],
            "dnfCondition": [[
                {"attributeName": "column", "attributeValueIncludes": col}
                for col in rule["columns"]
            ]]
        } for rule in rules]
    }

    response = requests.put(
        f"https://{purview_account}.purview.azure.com/policystore/policies/{policy_name}",
        headers=headers,
        json=policy_payload
    )
    return response.json()

# Create access policies
create_access_policy(
    "PII_access_policy",
    "workspace/lakehouse",
    [
        {"columns": ["email", "phone"], "access": "mask", "roles": ["analyst"]},
        {"columns": ["email", "phone"], "access": "full", "roles": ["admin"]}
    ]
)

Migration Paths

From Azure Synapse

# Migrate Synapse Dedicated Pool to Fabric Warehouse using REST APIs and T-SQL
from azure.identity import DefaultAzureCredential
import pyodbc
import requests

credential = DefaultAzureCredential()

# Source: Synapse Dedicated Pool connection
synapse_server = "synapse-server.sql.azuresynapse.net"
synapse_database = "dedicated_pool"

# Get token for Synapse SQL
synapse_token = credential.get_token("https://database.windows.net/.default").token

# Connect to Synapse
synapse_conn_str = (
    f"DRIVER={{ODBC Driver 18 for SQL Server}};"
    f"SERVER={synapse_server};"
    f"DATABASE={synapse_database};"
    f"Authentication=ActiveDirectoryAccessToken;"
)
synapse_conn = pyodbc.connect(synapse_conn_str, attrs_before={1256: synapse_token.encode()})

# Assess migration - get table list and sizes
cursor = synapse_conn.cursor()
cursor.execute("""
    SELECT
        s.name AS schema_name,
        t.name AS table_name,
        SUM(p.rows) AS row_count,
        SUM(a.total_pages) * 8 / 1024 AS size_mb
    FROM sys.tables t
    JOIN sys.schemas s ON t.schema_id = s.schema_id
    JOIN sys.partitions p ON t.object_id = p.object_id
    JOIN sys.allocation_units a ON p.partition_id = a.container_id
    WHERE p.index_id IN (0, 1)
    GROUP BY s.name, t.name
    ORDER BY size_mb DESC
""")

tables = cursor.fetchall()
print(f"Tables to migrate: {len(tables)}")
for table in tables:
    print(f"  {table.schema_name}.{table.table_name}: {table.row_count} rows, {table.size_mb} MB")

# Migration options:
# 1. Use COPY INTO from ADLS staging (recommended for large tables)
# 2. Use Azure Data Factory / Synapse pipelines
# 3. Use Fabric Data Pipelines with copy activity

# Example: Create target warehouse in Fabric
fabric_token = credential.get_token("https://api.fabric.microsoft.com/.default").token
fabric_headers = {"Authorization": f"Bearer {fabric_token}", "Content-Type": "application/json"}

workspace_id = "production-workspace-id"
warehouse_payload = {
    "displayName": "main_warehouse",
    "type": "Warehouse",
    "description": "Migrated from Synapse Dedicated Pool"
}

response = requests.post(
    f"https://api.fabric.microsoft.com/v1/workspaces/{workspace_id}/items",
    headers=fabric_headers,
    json=warehouse_payload
)
print(f"Warehouse created: {response.json()}")

# For actual data migration, use Fabric Data Pipelines or COPY INTO statements

What’s Next

2025 will bring:

• Q1: Enhanced Copilot, Real-Time Intelligence GA
• Q2: Advanced governance features, cross-cloud shortcuts
• Q3: Performance improvements, new connectors
• Q4: Next-gen AI features, Ignite announcements

Microsoft Fabric is becoming the unified platform for analytics and AI. If you haven’t started your Fabric journey, now is the time.