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Mastering Real Time Intelligence in Microsoft Fabric®
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Who is this course for?
Who is Greg?
What will I learn in this course?
Overview of online lab setup
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
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
Module introduction
OneLake
Lakehouses
Warehouses
KQL databases
What are eventhouses ?
KQL database OneLake availability
Lab 3 video walkthroughs
Lab 3
Quiz 3
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 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
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 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
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
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
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 introduction
Problem to solve
Solution architecture
Implementation notes
Module introduction
Problem to solve
Solution architecture
Implementation notes
Module introduction
Problem to solve
Solution architecture
Implementation notes
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
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 an Eventhouse as a vector database
Quiz 15
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
Quiz 16
Summary and further steps
Module 0: Getting started
Who is this course for?
Preview
Who is Greg?
Preview
What will I learn in this course?
Preview
Overview of online lab setup
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
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
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
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
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
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
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
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
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 an 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
Quiz 16
Module 17: Next steps
Summary and further steps
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