Real-Time Analytics in Microsoft Fabric: Use Cases and Implementation

Real-Time Intelligence in Microsoft Fabric enables organizations to analyze streaming data as it arrives, transforming raw event streams into actionable insights within seconds. For enterprises dealing with IoT telemetry, financial transactions, operational monitoring, or customer behavior tracking, the ability to react to data in real time is no longer optional—it is a competitive necessity. Fabric's Real-Time Intelligence brings enterprise-grade streaming analytics into a unified platform alongside your existing data warehouse, lakehouse, and Power BI assets.

What is Real-Time Intelligence?

Real-Time Intelligence is Fabric's streaming analytics engine built on Azure Data Explorer (ADX) technology. It uses Kusto Query Language (KQL) for lightning-fast queries on time-series and event data, capable of scanning billions of records in sub-second response times. Unlike traditional batch-oriented analytics that process data in hourly or daily intervals, Real-Time Intelligence processes events as they arrive, enabling organizations to detect anomalies, trigger alerts, and update dashboards within moments of an event occurring.

The platform integrates natively with the broader Fabric ecosystem, meaning data flowing through Real-Time Intelligence can be combined with historical data in lakehouses, enriched with dimensional data from warehouses, and visualized in Power BI—all within a single governance and security boundary managed by OneLake.

Key Components

Eventstreams Eventstreams are the data ingestion layer for Real-Time Intelligence. They capture, transform, and route streaming data from diverse sources: - Azure Event Hubs: Connect to existing event-driven architectures handling millions of events per second - Azure IoT Hub: Ingest telemetry from thousands of IoT devices with built-in device management - Apache Kafka: Integrate with Kafka clusters using native Kafka protocol support without code changes - Custom applications: Use REST APIs or SDKs to send events from any application - Database change data capture (CDC): Stream changes from Azure SQL, Cosmos DB, or PostgreSQL databases

Eventstreams include a visual no-code editor for in-flight transformations—filtering, field projection, aggregation windows, and schema mapping—before data reaches its destination.

KQL Databases KQL databases are purpose-built for time-series and event data analysis. Key capabilities include: - Sub-second query performance: Columnar storage with automatic indexing delivers results in milliseconds even on billion-row tables - Native time-series functions: Built-in operators for time bucketing, anomaly detection, forecasting, and gap filling - Automatic data management: Configurable retention and caching policies that manage hot, warm, and cold storage tiers automatically - Free-text search: Full-text indexing on string columns enables searching logs and events without pre-defined schemas

Real-Time Dashboards Real-Time Dashboards provide live visualization of streaming data: - Auto-refresh intervals: Configure dashboards to refresh every few seconds for true real-time monitoring - KQL-powered tiles: Each visualization is backed by a KQL query, giving analysts full control over aggregations and filters - Parameterized dashboards: Create interactive dashboards where users filter by device, region, time range, or custom dimensions - Alerting integration: Connect dashboard queries to Data Activator for automated alerts when thresholds are breached

Implementation Guide

Step 1: Create an Eventstream Set up your data source connection in the Fabric workspace. Navigate to Real-Time Intelligence, create a new Eventstream, and configure your source. For testing purposes, Fabric provides sample data generators that simulate IoT sensor readings or clickstream events—ideal for proof-of-concept work before connecting production sources.

Step 2: Define Transformations Use the visual editor to shape data before it reaches your KQL database. Common transformations include: - Filter events: Remove noise by filtering on event type, severity, or device category - Calculate derived fields: Add computed columns such as duration calculations or geographic region lookups - Aggregate windows: Perform tumbling or hopping window aggregations to reduce event volume while preserving analytical value - Schema normalization: Map heterogeneous source schemas into a consistent target format

Step 3: Create KQL Database Configure the destination for your processed stream. When creating the KQL database, define the table schema, set retention policies (how long data is kept), and configure caching policies (how much data stays in hot SSD storage for fastest queries). A typical configuration might retain 90 days of data with 7 days cached in hot storage.

Step 4: Write KQL Queries Analyze streaming data using KQL's rich operator set: ``` Events | where timestamp > ago(1h) | summarize count() by bin(timestamp, 5m), category | render timechart ```

Advanced patterns include anomaly detection using `series_decompose_anomalies()`, forecasting with `series_decompose_forecast()`, and pattern matching with `scan` for sequence detection across events.

Step 5: Build Dashboards and Alerts Create real-time visualizations that update as data flows. Pin frequently-used queries as dashboard tiles, configure auto-refresh intervals, and connect critical metrics to Data Activator triggers that send email, Teams, or Power Automate notifications when anomalies are detected.

Industry Use Cases

**Manufacturing**: Monitor production line sensors in real time, detect equipment degradation patterns before failures occur, and track quality metrics at the point of production. Real-time dashboards give plant managers immediate visibility into throughput, defect rates, and energy consumption.

**Retail**: Track inventory levels as point-of-sale transactions occur, analyze customer traffic patterns in real time, and detect pricing anomalies across thousands of SKUs. Retailers use streaming analytics to optimize staffing and stock replenishment dynamically.

**Financial Services**: Detect fraudulent transactions within milliseconds of occurrence using pattern matching and anomaly detection. Monitor trading activity, payment processing latency, and regulatory compliance metrics in real time.

**Healthcare**: Monitor patient vitals from connected medical devices, alert clinical staff on anomalous readings, and track operational metrics like bed utilization and emergency department wait times with HIPAA-compliant streaming analytics.

**Energy**: Ingest telemetry from smart grid sensors, wind turbines, and pipeline monitoring systems. Detect equipment anomalies, optimize load distribution, and ensure regulatory compliance with real-time operational dashboards.

Performance and Cost Considerations

Real-Time Intelligence pricing is based on compute capacity (CU-seconds consumed by queries) and storage (GB of data retained). To optimize costs: - Set appropriate retention and caching policies based on query patterns - Use materialized views for frequently-aggregated data - Configure Eventstream transformations to filter unnecessary events before ingestion - Monitor capacity metrics using Fabric's Capacity Metrics app to right-size your Fabric SKU

Related Resources

• Fabric Eventstream Patterns
• Data Activator Alerts
• Fabric Real-Time Intelligence
• Microsoft Fabric Services