🏭🎗️🗒️Microsoft Fabric: Real-Time Intelligence (RTI) [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 9-Mar-2025

Real-Time Intelligence architecture [4]

[Microsoft Fabric] Real-Time Intelligence [RTI]

• [def]
• {goal} provide a complete real-time SaaS platform within MF
• {benefit} helps gain actionable insights from data, with the ability to ingest, transform, query, visualize, and act on it in real time [4]
• {goal} provides a single place for data-in-motion
• {benefit} allows to pull event streams from Real Time Hub
• provides a single data estate for data in motion simplifying the ingestion, curation and processing of streaming data from Microsoft and external sources [4]
• {goal} enable rapid solution development
• {benefit} provides a range of no-code, low-code and pro-code experiences for various scenarios [4]
• everything from business insight discovery to complex stream processing, and application and model development [4]
• {goal} enable real-time AI insights
• {benefit} scales beyond human monitoring and drive actions with built in, automated capabilities [4]
• allows anyone in the organization to take advantage of [4]
• service that empowers users to extract insights and visualize data in motion
• offers an end-to-end solution for 
• event-driven scenarios
• ⇐ rather than schedule-driven solutions. 
• streaming data
• data logs
• {benefit} help companies accelerate speed and precision of business by providing [4]
• {goal} operational efficiency
• by allowing to streamline processes and make data driven decisions with accurate, up to date information [4]
• {goal} end-to-end visibility
• by allowing to gain a holistic understanding of business health and discover actionable insights for timely action [4]
• {goal} competitive advantage
• by allowing to quickly react to shifting market trends, identify opportunities and mitigate risk in real time [4]
• seamlessly connects time-based data from various sources using no-code connectors [1]
• enables immediate 
• visual insights
• geospatial analysis
• trigger-based reactions 
• ⇐ all are part of an organization-wide data catalog [1]
• ⇐ time oriented data is difficult to manage, yet critical for success [4]
• {challenge} capture high throughput data from disparate sources in real time [4]
• {challenge} model scenarios using event data [4]
• {challenge} choose from an array of bespoke technologies and data formats [4]
• {challenge} leverage the power of AI against data in real time [4]
• without the ability to leverage time oriented data, businesses are vulnerable to risks [4]
• {risk} poor decision-making
• {risk} financial loss
• {risk} reduced operational efficiency
• {risk} impaired data integrity
• {risk} non-compliance
• {risk} negative user experience
• {capability} single unified SaaS solution
• in opposition to a fragmented, fragile tech stack
• {capability} accessible data and analytics tools
• in opposition to advanced skillsets required
• {capability} real-time stream processing
• in opposition to batch data processing
• once  a stream of data is connected, the entire SaaS solution becomes accessible [1]
• handles 
• data ingestion
• data transformation
• data storage
• data analytics
• data visualization
• data tracking
• AI
• real-time actions
• can be used for 
• data analysis
• immediate visual insights
• centralization of data in motion for an organization
• actions on data
• efficient querying, transformation, and storage of large volumes of structured or unstructured data [1]
• helps evaluate data from 
• IoT systems
• system logs
• free text
• semi structured data, or contribute data for consumption by others in your organization, 
• provides a versatile solution
• transforms the data into a dynamic, actionable resource that drives value across the entire organization
• its components are built on trusted, core Microsoft rather than schedule-driven solutions 
• ⇐ together they extend the overall Fabric capabilities to provide event-driven solutions [1]
• {feature} Real-Time hub 
• serves as a centralized catalog that facilitates the easy access, addition, exploration, and data sharing [1]
• expands the range of data sources
• ⇐ it enables broader insights and visual clarity across various domains [1]
• ensures that data is accessible to all [1]
• promoting quick decision-making and informed action
• the sharing of streaming data from diverse sources unlocks the potential to build BI solutions across the organization [1]
• use the data consumption tools to explore the data [1]
• {feature} Real-Time dashboards 
• come equipped with out-of-the-box interactions 
• {benefit} simplify the process of understanding data, making it accessible to anyone who wants to make decision based on data in motion using visual tools, Natural Language and Copilot
• {feature} Fabric Activator
• {benefit} allows to turn insights into actions by setting up alerts from various parts of Fabric to react to data patterns or conditions in real-time [1]
• {feature} Real-Time hub events 
• a catalog of data in motionless
• contains:
• data streams 
• all data streams that are actively running in Fabric to which the user has access to
• Microsoft sources: 
• easily discover streaming sources that the users have and quickly configure ingestion of those sources into Fabric
• e.g. Azure Event Hubs, Azure IoT Hub, Azure SQL DB CDC, Azure Cosmos DB CDC, PostgreSQL DB CDC
• Fabric events
• event-driven capabilities support real-time notifications and data processing 
• ⇒ one can monitor and react to events [1]
• e.g. Fabric Workspace Item events, Azure Blob Storage events
• ⇐ the events can be used to trigger other actions or workflows [1]
• e.g. invoking a data pipeline or sending a notification via email. 
• the events can be sent to other destinations via eventstreams [1]
• {feature} Eventstreams
• event processing capabilities 
• {benefit} allow to capture, transform, and route high volumes of real-time events to various destinations with a no-code experience [1]
• support multiple data sources and data destinations [1]
• {benefit} allow to do filtering, data cleansing, transformation, windowed aggregations, and dupe detection, to land the data in the needed shape [1]
• one can use the content-based routing capabilities to send data to different destinations based on filters [1]
• derived eventstreams allows constructing new streams as a result of transformations and/or aggregations that can be shared to consumers in Real-Time hub [1]
• {feature} Eventhouses
• the ideal analytics engine to process data in motion
• tailored to time-based, streaming events with structured, semi structured, and unstructured data [1]
• data is automatically indexed and partitioned based on ingestion time
• ⇐ provides fast and complex analytic querying capabilities on high-granularity data [1]
• the stored data can be made available in OneLake for consumption by other Fabric experiences [1]
• ⇐ the data is ready for lightning-fast query using various code, low-code, or no-code options in Fabric [1]
• the data can be queried in native KQL or in T-SQL in the KQL query set [1]

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References:

[1] Microsoft Fabric (2024) What is Real-Time Intelligence? [link]
[2] Microsoft Fabric (2024) Real-Time Intelligence documentation in Microsoft Fabric [link] 

[3] Microsoft Fabric Updates Blog (2024) Fabric workloads are now generally available! [link]

[4] Microsoft Learn (2025) Real Time Intelligence L200 Pitch Deck [link]

Acronyms:
AI - Artificial Intelligence
CDC - Change Data Capture
IoT - Internet of Things
KQL  - Kusto Query Language
MF - Microsoft Fabric
RTI - Real-Time Intelligence
SaaS - Software-as-a-Service
SQL - Structured Query Language