Microsoft Fabric Foundations Workshop

Power BI users often face confusion when scaling to larger, faster, or more complex data. Terms like Lakehouse, Notebooks, Direct Lake, OneLake, and Pipelines feel overwhelming, especially after glimpsing Azure's vast portal.

Jessica Jolly likens Fabric to a local hardware store versus Azure's big-box chaos. It pulls key Azure services like Data Factory and Data Lake Gen2 into a unified, easier-to-navigate SaaS platform. This workshop clears terminology barriers and explains Fabric's role for Power BI pros.

What You Will Learn

- Fabric's core analogy as a simplified Azure experience with lifted services like Azure Data Factory and Data Lake Gen2

- Key components including Lakehouses for structured and unstructured data using Parquet and Delta Lake formats

- Notebooks for multi-language data work with Python, SQL, and Spark, outperforming Power Query for exploration

- Pipelines for data orchestration and automation, comparing to Dataflow Gen1 versus Gen2

- Semantic models in Fabric, addressing cardinality in relationships and Direct Lake mode for better performance over DirectQuery

- Power BI integration, loading Lakehouse data and building reports directly in Fabric workspaces

- Security features like Row-Level Security, Object-Level Security, and workspace roles for governance

Key Takeaways

1. Assess data scale first. Use Fabric when Power BI hits limits on volume, velocity, or variety. Jessica stresses you can stick with Power BI otherwise.

2. Master terminology basics. Grasp Lakehouse versus Warehouse, OneLake storage, and Direct Lake querying to avoid Azure overwhelm and communicate effectively.

3. Prioritize Lakehouse setup. Start with Lakehouses for flexible storage, then layer Notebooks and Pipelines for ingestion and transformation.

4. Leverage Git and Delta Logs. Track changes via Git integration and Delta time travel, compensating for Fabric's version control gaps.

5. Choose Direct Lake wisely. Opt for it over DirectQuery for semantic models when performance matters, but test cardinality issues in relationships.

About the Speaker

Jessica Jolly is a Microsoft Certified Trainer specializing in Power BI since 2015. She shifted from facilities management at Unilever to training Microsoft Office at public libraries, then focused on data visualization as Power BI launched. Preparing for Fabric certifications like DP-600, she shares practical insights from her journey and helps demystify rapid Microsoft changes.

Who Should Watch This

Power BI users ready to scale data practices will benefit most from this conceptual overview. It suits admins and analysts confused by Fabric terms or evaluating it against Power BI limits.

Skip if you are deep in Azure Synapse or enterprise data engineering, as this avoids code-heavy demos. Experienced Fabric users may still pick up governance tips on security and version control.

This 45-minute foundations session equips you to decide if Fabric fits your org without forcing adoption.

1. Understanding Microsoft Fabric and Its Components:
   • What Microsoft Fabric is and how it differs from Power BI and Azure.
   • Key tools available in Fabric, including Lakehouses, Notebooks, Pipelines, and Semantic Models.
   • How Microsoft rebranded and lifted various Azure services into Fabric.
2. Fabric Notebooks & Multi-Language Capabilities:
   • How to use Python, SQL, and Spark in Fabric Notebooks.
   • Why Notebooks provide a better user experience than the Power Query Editor.
   • Using Fabric Notebooks for data exploration and transformation.
3. Lakehouse & Data Management in Fabric:
   • How to set up and use a Lakehouse for structured and unstructured data.
   • Working with Parquet and Delta Lake for efficient data storage.
   • The differences between Lakehouses and traditional data warehouses.
4. Version Control & Data Governance in Fabric:
   • Understanding Fabric’s version control limitations and the role of Git integration.
   • How to track changes in datasets using the Delta Log.
   • Managing security and governance through Row-Level Security (RLS) and Object-Level Security (OLS).
5. Data Pipelines & Automation in Fabric:
   • How Fabric Pipelines compare to Azure Data Factory.
   • When to use Dataflow Gen 1 vs Gen 2 for data transformation.
   • Automating data ingestion and transformations in Fabric.
6. Building & Optimizing a Fabric Semantic Model:
   • How to create semantic models (formerly data models) in Fabric.
   • Understanding cardinality issues in Fabric model relationships.
   • How Direct Lake mode improves performance over DirectQuery.
7. Power BI & Fabric Integration:
   • Loading data from a Fabric Lakehouse into Power BI.
   • Creating Fabric-based reports directly in Power BI Service.
   • When to use Direct Lake vs Import Mode for best performance.
8. Security & Access Control in Fabric:
   • Best practices for securing data in Fabric Workspaces.
   • The difference between workspace roles and Fabric permissions.
   • Ensuring data privacy and access control across teams.
9. Challenges & Practical Considerations for Fabric Adoption:
   • The learning curve for Power BI users transitioning to Fabric.
   • Whether Fabric is right for all organizations or just large enterprises.
   • Future roadmap for Fabric and Microsoft’s vision.
10. Fabric Certifications & Learning Paths:

• Overview of DP-600 and DP-700 certification exams.
• Best resources to learn Fabric.
• How to stay up-to-date with Microsoft’s rapid feature rollouts.