Introduction
Most modern data analytics solutions enable two common patterns for data analysis:
- Batch data analytics, in which data is loaded into an analytical data store at periodic intervals as a batch operation; enabling historical analysis of data from past events.
- Real-time data analytics, in which data from events is ingested in real-time (or near real-time) as events occur in a stream of data that can be analyzed, visualized, and used to trigger automated responses.
Batch data analytics is generally well understood, and is commonly implemented using data warehouse or lakehouse architectures. Real-time analytics may be considered more specialist, but increasingly it's being incorporated into large-scale data analytics solutions in the form of a lambda architecture that combines the periodic loading of batch data for historical analysis with the ingestion of data streams for real-time analysis.
Microsoft Fabric provides capabilities for both batch and real-time analytics. In this module, we'll focus on the Real-Time Intelligence features of Microsoft Fabric to explore how you can build real-time data analytics solutions with minimal coding that scale to huge volumes of data from a diverse range of sources.
The topics covered in this module include:
- Understanding core concepts related to real-time data analytics.
- Understanding Microsoft Fabric's Real-Time Intelligence capabilities.
- Exploring core components of Real-Time Intelligence in Microsoft Fabric.
- Ingesting real-time data by using an eventstream.
- Using an eventhouse and a KQL database for real-time data analysis in Microsoft Fabric.
- Visualizing data in real-time dashboards.
- Using Activator in Microsoft Fabric to define alerts that trigger automated actions.
By the end of this module, you'll be able to understand the capabilities of Microsoft Fabric's Real-Time Intelligence features. You'll also get hands-on experience through a practical exercise.