🏭🎗️🗒️Microsoft Fabric: Real-Time Hub (RTH) [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 [2]

[Microsoft Fabric] Real-Time Hub [RTH]

• [def]  single, tenant-wide, unified, logical place for streaming data-in-motion [1]
• enables to easily discover, ingest, manage, and consume data-in-motion from a wide variety of sources [1]
• each user in the tenant can view and edit all the events or streams that they have access to
• makes it easy to collaborate and develop streaming applications within one place [1]
• allows to blend data in motion and data at rest for accelerated insight discovery [2]
• provides out-of-box connectors that make it easy to ingest data into MF from a wide variety of sources [1]
• allows to create streams for the supported sources
• after the streams are created, users can 
• process them by 
• opening the parent eventstream in an editor [1]
• adding transformations to transform or process the data that's streaming into MF [1]
• e.g. aggregate, expand, filter, group by, manage fields, and union, 
• sending the output data from transformations into supported destinations [1]
• analyze them
• set alerts on them [1]
• {goal} provide a large catalog of data in motion 
• ⇐ brought in with just a few clicks in Get Events [2]
• {feature} subscribe to internal and external discrete events [2] 
• {feature} quickly connect experiences for Microsoft streaming sources [2]
• e.g. IoT Hub
• {feature} access system events emitted by MF and Azure storage [2]
• {feature} ingest data streams from all clouds 
• e.g. AWS, Kinesis, Google Pub Sub, etc.
• {goal} work across full event life cycle
• allows to analyze, build lightweight models, and trigger actions over all data in motion, working across the life cycle of events to create derived and enriched streams [2]
• {feature} create triggers through simple, embedded experiences [2]
• {feature} pen event streams to process and route events without writing any code [2]
• {feature} land in Eventhouse for further analysis [2]
• {feature} analyze and build lightweight models [2]
• {feature} create derived and enriched streams [2]
• {goal} consume data from anywhere
• allows to view all time-oriented data in motion, readily available for cross workload use in OneLake [2]
• {feature} discover events and seamlessly consume them from across organization [2]
• {feature} expose and use well-established open source APIs, standards, protocols and connectors [2]
• {feature} maintain data ownership data is not trapped in Microsoft’s proprietary formats [2]
• {feature} integrate seamlessly with other experiences in MF [2]
• {goal} simplify integration of stream processing frameworks [2]

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References:

[1] Microsoft Learn (2025) Microsoft Fabric: Introduction to Fabric Real-Time hub [link] 

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

Resources:

[R1] Microsoft Learn (2024) Microsoft Fabric exercises [link]

Acronyms:
API - Application Programming Interface
AWS - Amazon Web Services
IoT - Internet of Things
KQL  - Kusto Query Language
MF - Microsoft Fabric
RT - Real-Time
RTH - Real-Time Hub