Microsoft Fabric Real-Time Intelligence: Streaming Analytics at Scale

Microsoft Fabric’s Real-Time Intelligence workload has matured significantly since GA. Today I’m exploring how to build streaming analytics pipelines that process millions of events per second while maintaining sub-second query latency.

Configuring Eventstreams

Eventstreams serve as the ingestion layer for real-time data. The new enhanced connector ecosystem supports over 100 source types natively:

from azure.identity import DefaultAzureCredential
import requests

# Fabric REST API client for Eventstreams
credential = DefaultAzureCredential()
token = credential.get_token("https://api.fabric.microsoft.com/.default").token

headers = {
    "Authorization": f"Bearer {token}",
    "Content-Type": "application/json"
}

workspace_id = "ws-analytics-prod"
base_url = f"https://api.fabric.microsoft.com/v1/workspaces/{workspace_id}"

# Create eventstream via REST API
eventstream_payload = {
    "displayName": "iot-telemetry-stream",
    "type": "Eventstream",
    "description": "IoT telemetry ingestion from Kafka"
}

response = requests.post(
    f"{base_url}/items",
    headers=headers,
    json=eventstream_payload
)

eventstream_id = response.json().get("id")
print(f"Created Eventstream: {eventstream_id}")

# Note: Eventstream source/destination configuration is done via the Fabric UI
# or by updating the eventstream definition. The Kafka connector, transformations,
# and KQL/Lakehouse destinations are configured in the Fabric portal.

KQL for Real-Time Analytics

Kusto Query Language powers the analytics layer. Here’s a pattern for detecting anomalies in streaming data:

RawEvents
| where ingestion_time > ago(5m)
| summarize
    avg_temp = avg(temperature),
    stddev_temp = stdev(temperature),
    count = count()
    by device_id, bin(ingestion_time, 1m)
| extend anomaly_score = abs(avg_temp - 72) / stddev_temp
| where anomaly_score > 3
| project device_id, avg_temp, anomaly_score, ingestion_time

Activator for Automated Response

The Activator component triggers actions based on real-time conditions. Connect your KQL queries to automated workflows that respond instantly to anomalies, reducing mean time to resolution from hours to seconds. This integration with Power Automate and Azure Functions creates a complete observability and response platform within Fabric.