The Seven Fabric Workloads (Part 2)

Introduction to Microsoft Fabric

Alex Kuntz

Technical Instructional Designer

The Seven Fabric Experiences

• Data Factory, Data Engineering, and Data Warehouse focused on ingesting data
• The remaining workloads focus more on data analysis and reporting

Data Science

• Write Spark code for data exploration, manipulation
• Supports a variety of languages like Python (PySpark), Scala, SparkSQL, and SparkR

Data Science

• Used to create ML models and evaluate their strengths

• Prepares insights from models for a tool like Power BI

Data Science

Power BI

• Released in 2011, but integrated into Fabric environment.
• Used to create dashboards and visualizations to drive business impact
• Used by Business Intelligence Developers
• Pulls data from OneLake Lakehouses and Warehouses

Real Time Intelligence

• Storage option built to handle real-time event streams
• Uses OneLake and the Delta-Parquet format, just like Lakehouses and Warehouses

Real Time Intelligence

• Storage option built to handle real-time event streams
• Uses OneLake and the Delta-Parquet format, just like Lakehouses and Warehouses

• Ingests data in real time and routes it to an Eventhouse
• Similar to DataFlow, but for a stream of data

Real Time Intelligence

• Define actions to react to patterns or conditions that are detected in data
• For example,
  • IoT tool monitors items in a physical warehouse
  • Number of items passes a threshold; the warehouse is too full!
  • An email to the manager is triggered using Activator

Industry Solutions

• Tools built for specific use cases like working with retail or governance data.

Let's practice!

Introduction to Microsoft Fabric