Microsoft Fabric for Data Analysts – Lakehouse, Power BI, SQL & End-to-end Data Analysis

In this course, you'll get to know Microsoft Fabric as an integrated platform for modern data analysis. You'll learn how to collect, clean, structure, and transform data into semantic models and visualisations. Through realistic hands-on assignments, you'll work with Lakehouses, Warehouses, and Data Pipelines to build a complete workflow — from raw data to Power BI dashboards. Microsoft Fabric brings together the power of Azure, Synapse, Power BI, and data engineering in a single environment, making it the ideal platform for data analysts and BI professionals looking to modernise their approach.

What you'll learn

• The architecture and core components of Microsoft Fabric.
• Loading and transforming data with Data Pipelines and Dataflows.
• Working with Lakehouses and Warehouses as storage and processing layers.
• Designing and optimising semantic models with DAX.
• Visualising and sharing insights with Power BI within Fabric.

After this course you'll be able to:

• Independently run an end-to-end data analysis process in Microsoft Fabric.
• Integrate, transform, and model data from multiple sources.
• Build and visualise a semantic model with Power BI.
• Use Fabric as a central environment for data-driven decision-making.

Who is this for

• Data engineers, BI specialists, and data analysts working with Microsoft technologies.
• Data scientists looking to scale their analyses within a modern data platform.
• Professionals who want to modernise their data architecture with Microsoft Fabric.

Prerequisites

• Basic knowledge of SQL and Power BI.
• Understanding of data architecture or ETL processes is recommended.
• Experience with Python is a plus, but not required.

Programme

Part 1 – Introduction to Microsoft Fabric

• Architecture and components: OneLake, Lakehouse, Warehouse, and Power BI.

Part 2 – Loading and Transforming Data

• Data Pipelines and Dataflows (theory + practice).

Part 3 – Working with Lakehouses and Warehouses

• Structuring and organising data for analytics.

Part 4 – Building and Optimising Semantic Models

• Relationships, DAX measures, model structure, and performance.

Part 5 – Visualisation with Power BI & End-to-end Workflow

• From raw data to dashboard and evaluation.