Course Introduction
Microsoft Fabric Real-Time Intelligence Training Course provides a comprehensive and practical introduction to analyzing data while it is continuously arriving. Microsoft Fabric Real-Time Intelligence brings together capabilities for ingesting, transforming, storing, querying, visualizing, and acting on streaming data, supporting scenarios where organizations need timely insights from events, telemetry, logs, and other data in motion.
The course introduces the core components of the Real-Time Intelligence workload, including Real-Time hub, Eventstream, Eventhouse, KQL databases, KQL querysets, Real-Time dashboards, and Activator. Participants learn how these components fit together to create an end-to-end workflow that moves from event ingestion through processing and analysis to visualization and automated action.
Participants will develop practical skills in connecting streaming sources, transforming events, routing data, storing time-based information, and querying large volumes of event data. Eventstreams provide a no-code approach for ingesting, transforming, and routing real-time events, while Eventhouse is designed specifically for storing and analyzing streaming, structured, semi-structured, and unstructured data.
The training provides substantial coverage of Kusto Query Language (KQL), which is central to querying and analyzing data in Eventhouse. Participants learn how to explore event data, create analytical queries, work with time-series information, identify patterns and anomalies, and prepare queries for real-time dashboards and automated actions. The course also examines how KQL can complement SQL, notebooks, Power BI, and other Fabric experiences.
Real-time visualization and event-driven automation form another important part of the course. Participants learn how Real-Time dashboards can present continuously changing information and how Activator can monitor conditions or patterns and trigger notifications or Fabric workflows. These capabilities support practical scenarios such as operational monitoring, IoT analytics, application telemetry, anomaly detection, fraud monitoring, and other event-driven business processes.
The course also incorporates emerging topics including AI and agent telemetry, intelligent anomaly detection, geospatial analysis, event-driven orchestration, Apache Kafka integration, change data capture, and AI agents working with live Eventhouse data. Participants gain an understanding of how real-time analytics is evolving beyond dashboards toward systems that can detect conditions, reason over current information, and initiate appropriate actions.
Duration
5 days
Who Should Attend
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Data analysts seeking practical skills for analyzing streaming and continuously changing business data.
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Data engineers responsible for designing real-time ingestion, transformation, processing, and analytics pipelines.
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Business intelligence professionals who need to incorporate live operational information into analytical solutions.
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Power BI professionals interested in connecting real-time data with dashboards and decision-support applications.
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Cloud engineers implementing scalable streaming analytics and event-driven solutions within Microsoft Fabric.
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Data architects designing modern architectures for IoT, telemetry, application events, logs, and operational data.
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DevOps and observability professionals working with application, infrastructure, performance, and system telemetry.
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IoT professionals requiring real-time processing and analytics for connected devices and sensor-generated information.
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Data scientists exploring time-series data, anomaly detection, streaming analytics, and continuously updated datasets.
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Solution architects designing event-driven applications that combine analytics, automation, AI, and operational workflows.
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Technology managers overseeing real-time analytics modernization and enterprise data platform initiatives.
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Consultants implementing Microsoft Fabric Real-Time Intelligence solutions for clients across different business environments.
Course Objectives
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Explain the architecture and purpose of Microsoft Fabric Real-Time Intelligence and its role in analyzing data as events occur.
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Configure Real-Time hub capabilities to discover, manage, and work with streaming data and event-driven sources across Fabric.
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Build Eventstreams that ingest, transform, filter, enrich, and route real-time events to appropriate analytical destinations.
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Create and manage Eventhouse environments for storing, organizing, querying, and analyzing large volumes of time-based event data.
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Develop effective KQL queries for exploring streaming data, performing aggregations, identifying patterns, and generating actionable analytical insights.
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Create Real-Time dashboards that present continuously changing information through interactive visualizations suitable for operational monitoring.
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Configure Activator rules to detect thresholds, patterns, and conditions and trigger notifications, workflows, or Fabric-based actions automatically.
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Apply real-time analytics techniques to practical scenarios involving IoT, telemetry, logs, operational monitoring, anomaly detection, and event-driven processes.
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Integrate Real-Time Intelligence with OneLake, Power BI, notebooks, lakehouses, warehouses, and other Fabric workloads to create unified data solutions.
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Evaluate emerging capabilities involving AI agents, intelligent anomaly detection, Kafka integration, change data capture, geospatial analytics, and automated real-time actions.
Comprehensive Course Outline
Module 1: Real-Time Intelligence Foundations
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Understanding Microsoft Fabric Real-Time Intelligence and how it supports ingestion, processing, storage, analysis, visualization, and action on data in motion.
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Exploring common real-time analytics scenarios including IoT, application telemetry, operational monitoring, fraud detection, security events, and anomaly detection.
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Examining the Real-Time Intelligence architecture and relationships between Real-Time hub, Eventstream, Eventhouse, KQL, dashboards, and Activator.
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Comparing scheduled analytics with event-driven analytics and identifying situations where continuously evaluated data can support faster operational responses.
Module 2: Real-Time Hub and Streaming Data Discovery
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Exploring Real-Time hub as a centralized experience for discovering and managing streaming data sources and events available across the organization.
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Connecting Microsoft and external streaming sources while understanding connectors, event sources, data formats, and ingestion requirements.
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Working with database change data capture and event-driven sources to identify changes that can be processed without relying exclusively on scheduled workloads.
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Examining emerging data discovery requirements involving organizational data catalogs, streaming governance, event visibility, and cross-workload accessibility.
Module 3: Eventstream Ingestion and Processing
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Creating Eventstreams to ingest real-time events from supported sources and route continuously arriving information to analytical destinations.
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Applying no-code transformations such as filtering, cleansing, aggregation, duplicate detection, and content-based routing within event processing workflows.
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Working with Eventstream destinations including Eventhouse, Lakehouse, custom endpoints, and other supported Fabric components for downstream processing.
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Exploring emerging Eventstream capabilities involving Apache Kafka protocols, SQL-based transformation logic, CDC streams, and expanding connector ecosystems.
Module 4: Eventhouse and KQL Databases
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Creating Eventhouse environments and understanding how KQL databases provide scalable storage and analytical capabilities for real-time event data.
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Organizing structured, semi-structured, and unstructured event information for efficient querying, investigation, monitoring, and analytical consumption.
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Understanding how Eventhouse supports time-based event data and high-volume analytical workloads while integrating with broader Fabric experiences.
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Applying practical Eventhouse management, monitoring, capacity, storage, ingestion, and performance concepts for enterprise real-time analytics.
Module 5: Kusto Query Language for Real-Time Analytics
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Learning KQL syntax and query structures for filtering, projecting, sorting, aggregating, joining, and analyzing event-driven datasets.
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Working with time-series data, windows, timestamps, event sequences, and other analytical patterns commonly required in real-time environments.
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Creating reusable KQL functions, materialized views, and optimized analytical queries for frequently accessed real-time datasets.
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Exploring AI-assisted KQL development and natural-language query experiences that can help analysts and data professionals interact with event data more efficiently.
Module 6: Real-Time Dashboards and Visualization
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Creating interactive Real-Time dashboards that display continuously refreshed information and support operational awareness across business functions.
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Designing dashboard tiles and visualizations using KQL-based analytical results to highlight trends, exceptions, events, and changing operational conditions.
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Applying filtering, organization, performance, and dashboard management techniques to create clear and responsive real-time analytical experiences.
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Exploring geospatial visualization, mapping, anomaly analysis, and emerging intelligent visualization capabilities for operational and location-based analytics.
Module 7: Activator and Event-Driven Automation
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Understanding Activator as an event detection and automation capability that monitors data conditions and initiates actions when defined rules are satisfied.
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Creating threshold-based and pattern-based rules that identify important events, changes, anomalies, or operational conditions within real-time datasets.
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Configuring actions such as notifications, Fabric jobs, pipelines, Spark jobs, Dataflows, and Power Automate workflows for automated responses.
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Designing event-driven business processes that connect real-time insights with operational intervention, workflow automation, and intelligent decision-making.
Module 8: Real-Time Analytics Integration Across Fabric
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Integrating Eventhouse and Real-Time Intelligence with OneLake to support broader analytics, data sharing, and unified organizational data experiences.
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Connecting real-time data with Power BI, Fabric notebooks, Lakehouse, and other workloads for advanced analysis, reporting, transformation, and machine learning.
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Understanding how real-time data can complement historical information to support combined operational and analytical decision-making.
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Designing unified architectures that combine streaming ingestion, real-time analytics, batch processing, business intelligence, and data science workflows.
Module 9: Monitoring, Security, Governance, and Performance
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Monitoring Eventhouse health, ingestion activity, query performance, storage usage, resource consumption, and operational behavior across real-time workloads.
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Applying security and governance principles to protect streaming data while controlling access, maintaining lineage, and supporting responsible organizational data use.
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Optimizing Eventstream processing, Eventhouse storage, KQL queries, dashboards, and analytical workflows for scalable real-time performance.
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Addressing emerging challenges involving streaming data quality, privacy, regulatory requirements, capacity planning, observability, and governance of continuously changing information.
Module 10: Emerging Real-Time Intelligence, AI, and Industry Applications
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Exploring AI and agentic application scenarios where live telemetry, safety signals, conversations, and operational events can be analyzed for immediate responses.
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Examining AI agents, MCP-based connectivity, natural-language interaction, and intelligent reasoning over live and historical Eventhouse data.
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Applying real-time intelligence concepts to emerging use cases involving smart manufacturing, connected devices, cybersecurity, financial monitoring, logistics, and digital operations.
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Developing an end-to-end real-time solution integrating Eventstream, Eventhouse, KQL, Real-Time Dashboard, Activator, governance, monitoring, and AI-enabled capabilities.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
