This course covers how to prepare, enrich, and serve data for analysis by consumers. Participants will focus on designing dimensional models and transforming data by using dataflows, notebooks, and T-SQL across lakehouses, warehouses, and eventhouses in Microsoft Fabric. The course also covers building and optimizing semantic models, managing the analytics development lifecycle, and enforcing security and governance across data assets.
Prerequisites:
- Experience translating business requirements into analytical measures using SQL or DAX
- Experience building semantic models and reports in Power BI
- Familiarity with KQL and Python
Purpose
| Prepare data for analysis using Microsoft Fabric |
Audience
| Data professionals with experience in data modeling, transformation, and analytics |
Role
| Data Analysts | Business Analysts |
Skill level
| Advanced |
Style
| Lecture | Hands-on Activities | Labs |
Duration
| 4 days |
Related technologies
| SQL | T-SQL | Power BI | Python | Kusto Query Language |
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Learning objectives
- Design and implement dimensional (star schema) models for analytics
- Transform and enrich data using dataflows, notebooks, and T-SQL
- Work across lakehouses, warehouses, and eventhouses within Microsoft Fabric
- Build and optimize semantic models for enterprise-scale reporting and analysis
- Apply performance-tuning techniques to semantic models
- Manage the end-to-end analytics development lifecycle
- Implement best practices for reusable, maintainable analytics assets
- Enforce security and governance controls across data assets in Fabric
