Mastering Real-Time Intelligence in Microsoft Fabric

Turn data into insights the moment it happens because yesterday’s data is already too late !

Course Summary

Do you need to master Real-Time Intelligence in Microsoft Fabric?

Do you want to understand how data can be processed the moment it’s created — rather than hours or days later?
Would you like to build systems that respond instantly to new information, such as customer actions, sensor readings, or transactions?
Are you curious how tools in Microsoft Fabric can connect, process, and analyze data as it flows in real time?
Do you want to design data pipelines that handle both live streaming and scheduled batch data with ease?
Would you like to create dashboards that update automatically the moment new data arrives?
Are you already experimenting with Microsoft Fabric’s real-time features and want to learn the right way to put them into practice?
Or are you completely new to real-time data and want a clear, step-by-step introduction from an expert who makes complex ideas simple?
Do you want to learn from an expert?

If any of this sounds like you, this course is the perfect place to start. You’ll get clear explanations, hands-on exercises, and short quizzes to make sure you not only understand the concepts—but can confidently apply them in real projects.

We encourage you to complete the practical exercises. We have tried to make this as easy as possible. The main thing you’ll need is a trial (or real) Fabric account.

Enroll now for $295 USD !

Modules and Lessons

Module 0: Getting started

Who is this course for ?
Who is Greg ?
What will I learn in this course ?
Configuring your lab environment

Module 1: Introduction to Real-Time Intelligence

Module Introduction
What is Real-Time Intelligence ?
Why Real-Time Matters
Latency vs Freshness Trade-Offs
Batch Processing vs Streaming
What does Fabric Real-Time Intelligence Provide ?
What are Events ?
What are Streams ?
What is Ingestion ?
What is Processing ?
What are Actions ?
What is the Real-Time Hub ?
Lab 1 video walkthroughs
Lab 1
Quiz 1

Lab exercises:

Exercise 1: Create a workspace named sdu_rti_lab_1
Exercise 2: Create an eventstream using the stock sample
Exercise 3: Create an eventhouse named stock-events
Exercise 4: Open the KQL database
Exercise 5: Query the KQL database
Exercise 6: Create a real-time dashboard
Exercise 7: Use Activator to create an alert
Exercise 8: Delete the workspace and objects

Module 2: Common Real-Time Data Sources

Module Introduction
Event-Driven vs Request-Driven Systems
Message Brokers and Event Streams
Azure Event Hubs
Azure IoT Hub
Using AMQP vs HTTP
Apache and Confluent Kafka
Rabbit MQ
Message Queue Comparisons
Database CDC Sources
Database CES Sources
CDC vs CES Comparison
Azure and AWS Storage Events
Azure and Fabric Events
How Fabric Connects to External Sources
Lab 2 video walkthroughs
Lab 2
Quiz 2

Lab exercises:

Exercise 1: Create a workspace named sdu_real_time_lab
Exercise 2: Create an EventHub based eventstream

Module 3: Fabric Storage Options

Module Introduction
OneLake
Lakehouses
Warehouses
KQL Databases
What are Eventhouses ?
KQL Database OneLake Availability
Lab 3 video walkthroughs
Lab 3
Quiz 3

Lab exercises:

Exercise 1: Create an eventhouse named ppk_eventhouse
Exercise 2: Replace the default KQL database
Exercise 3: Import a table into a KQL database
Exercise 4: Create a table by connecting the eventsource

Module 4: Event Ingestion in Fabric

Module Introduction
What are Eventstreams ?
Event Processing Inputs
Event Processing Outputs
Ingestion Modes
Filtering Events
Mapping Events
Routing Events
Designing Eventstream Pipelines
Data Quality in Real-Time
What are Event Schema Sets ?
Quiz 4

Module 5: Transforming Real-Time Data

Module Introduction
Filtering
Managing Fields
Aggregating
Joining with Other Streams
Grouping
Applying Temporal Windows
Tumbling
Sliding
Session
Hopping
Snapshot
Applying Union
Expanding
Using Custom SQL Code
Lab 5 video walkthroughs
Lab 5
Quiz 5

Lab exercises:

Exercise 1: Filter Events
Exercise 2: Manage Fields
Exercise 3: Store the Abnormal Temperature Events
Exercise 4: Adding Temporal Window Aggregation
Exercise 5: Store the Truck Aggregates
Exercise 6: Review the Destination Tables
Exercise 7: Delete the workspace and objects

Module 6: Querying and Processing Real-Time Data

Module Introduction
Eventstream Destinations
KQL Databases
Using Visual Exploration
Querying using KQL
What are KQL Querysets ?
Joining using KQL
Querying using SQL
Joining using SQL
When to use KQL or SQL
Handling Late-Arriving or Out-of-Order Data
Quiz 6

Module 7: Querying with KQL

Module Introduction
What is KQL ?
Core Concepts of KQL
Basic Query Syntax
Filtering
Projections, Extensions, and Aliases
Working with Time Series
Aggregations
Joins and Lookups
Advanced Operators
Functions in KQL
Anomaly Detection
Trend Analysis
Lab 7 video walkthroughs
Lab 7
Quiz 7

Lab exercises:

Exercise 1: Set up Bike-Related Resources
Exercise 2: Core KQL Query Techniques
Exercise 3: Advanced Operations

Module 8: Integrating Analytics and Visualization

Module Introduction
Creating Real-Time Dashboards
Streaming to Lakehouses
Streaming to Warehouses
Building Alert-Driven Workflows
Thresholds
Anomalies
Creating Power BI Reports on Real-Time Data
Real-time APIs and Operational Integration
Using Fabric Maps with Real-Time Intelligence
Building Digital Twins
Lab 8 video walkthroughs
Lab 8
Quiz 8

Lab exercises:

Exercise 1: Create a Real-Time Dashboard with a Live KQL Query Tile
Exercise 2: Implement a Base Query
Exercise 3: Add a Summary Tile
Exercise 4: Create a Power BI Report

Module 9: Acting on Real-Time Events

Module Introduction
What is Fabric Activator ?
Understanding Objects and Signals
What are Rules ?
Detecting Patterns
Triggering Actions
Integrating Power Automate
Integrating Logic Apps
Triggering Teams, Emails, and Workflows
Monitoring and Managing Triggers
Lab 9 video walkthroughs
Lab 9
Quiz 9

Lab exercises:

Exercise 1: Create an Activator and Define a Data Source
Exercise 2: Define an Event-Stream Based Alert
Exercise 3: Define a Real-Time Dashboard Based Alert

Module 10: Architecture and Best Practices

Module Introduction
Designing for Scale
Designing for Reliability and Resilience
Handling Backpressure
Handling Retries
Providing Fault Tolerance
Replay and Reprocessing
Security and Governance for Pipelines
Governance and Compliance
Cost Considerations for Streaming vs Batch
Cost and Performance Optimization
Monitoring and Observability
Common Pitfalls in Real-Time Projects
Quiz 10

Module 11: Real-Time Fraud Detection Example

Module Introduction
Problem to Solve
Solution Architecture
Implementation Notes

Module 12: IoT Telemetry Dashboard Example

Module Introduction
Problem to Solve
Solution Architecture
Implementation Notes

Module 13: Real-Time Sentiment Analysis Example

Module Introduction
Problem to Solve
Solution Architecture
Implementation Notes

Module 14: Advanced Concepts

Module Introduction
Hybrid Architectures
Data Retention Strategies
Scaling Eventstreams
KQL Materialized Views
Partitioning Strategies
Cross-Database and Cross-Cluster Queries
Using Wildcards with Union
Using Special Functions
Using Update Policies to Transform Data
Lab 14 video walkthroughs
Lab 14
Quiz 14

Lab exercises:

Exercise 1: Create and Test a Materialized View
Exercise 2: Create a Roll-up Materialized View
Exercise 3: Create a Reference Database
Exercise 4: Test Cross-Database Queries and Objects

Module 15: Real-Time AI Integration

Module introduction
Integrating with Machine Learning
AI-Powered Anomaly Detection
AI Anomaly Detector - How it works
AI Anomaly Detector - Available models
AI Anomaly Detector - Use cases
AI Anomaly Detector - Limitations
Integrating with MCP-based agents
Building real-time RAG with Eventhouse
Using Eventhouse as a Vector Database
Quiz 15

Module 16: Advanced Integration and Future Trends

Module Introduction
Integrating with Kafka Streams
Integrating with Flink
Integrating with Spark Structured Streaming
Integrating with Google Cloud Pub-Sub
Integrating with MQTT
Integrating KQL Follower Databases
Fabric RTI vs Azure Stream Analytics
Emerging Patterns for Real-Time
Digital Twins
Edge scenarios
Lab 16 video walkthroughs
Lab 16
Quiz 16

Lab exercises:

Exercise 1: Create a Follower Database in Another Eventhouse
Exercise 2: Validate and Share the Follower Database
Exercise 3: Delete the Workspace and Objects

Module 17: Next steps

Summary and further steps

Enroll now for $295 USD !