About the Trainer – Ginger Grant
Ginger Grant is a distinguished Microsoft Data Platform MVP and Microsoft Certified Trainer (MCT), renowned for her deep expertise in advanced analytics, machine learning, AI, data warehousing, and the evolving landscape of Microsoft Fabric. As a sought-after consultant, Ginger empowers organizations to harness the full potential of their data ecosystems.
Beyond consulting, Ginger is a prolific thought leader and speaker for both keynotes and technical training. She contributes regularly as a columnist for Code Magazine, authors insightful books and shares practical knowledge on her blog, DesertIsleSQL.com. Her educational impact spans a wide range of technologies, include Azure Synapse Analytics, Python, and Azure Machine Learning, making her a trusted voice in the data community.
Whether on stage, in print, or in the classroom, Ginger’s passion for data and commitment to knowledge-sharing make her a standout figure in the world of data and AI.
Course Outline
As Microsoft Fabric contains many different components, learning how to use it can appear daunting. This comprehensive training course is designed to guide attendees from core platform fundamentals to advanced end‑to‑end analytics capabilities.
Whether you’re new to Microsoft Fabric or aiming to elevate your skills, this hands‑on program will equip you to harness the full power of the unified analytics platform. Learn what mistakes to avoid by architecting a design solution using best practices, including Medallion Architecture, CICD.
Understand how to optimize performance across Fabric’s different data storage options and how to monitor it. We will cover not only the basics on using Lakehouse, Warehouse, and SQL DBs, but why and when each component makes sense in your environment. This hands-on course will equip you to unlock the full potential of your data through effective modelling, governance, and powerful analytics.
Day 1 – Getting Started with Fabric
Introduction to Microsoft Fabric
- Describe reason for creation of the application
- Review the different applications within Fabric
- Elaborate on the different personas
- Review different components within Fabric
- Discuss relationships between Fabric and Power BI
Workspace Management and Organization
- Elaborate on the purpose and use of Workspaces inside of Fabric
- Introduce the different security roles and responsibilities
- Accessing Content in Workspaces and sharing
- Discuss Data Mesh architecture and ho Discuss Data Mesh architecture and how it can be used to limit access
One Lake, Direct Lake and Data Mesh
- Detail the architecture of One Lake
- Review methods for examining the content of One Lake
- Elaborate on the elements of One Lake security and how it uses Workspaces
- Describe Direct Lake and integration with Power BI
- Shortcuts and Mirroring
Medallion Architecture
- Describe medallion architecture
- Review contents of the Bronze Layer
- Distinguish between the content in the Bronze Layer and the Silver Layer
- Explain the purpose of the Gold layer and the different level of access to it
- Provide examples of the different content loaded in each layer
- Elaborate practical layer organization with Workspaces
- Describe the processes used to implement Medallion Architecture
Data Organization – Day 2
Data modelling
- Introduction to star schema design principles
- Define the structures which should be implemented for reporting semantic models
- Review where the different data modelling elements are located in Medallion Architecture
- Describe the design elements required in a good data warehouse
- Elaborate on the consequences of poor design on performance
Lakehouse
- Lakehouse Organization and purpose
- Source datatypes, Introduction to Parquet
- Introduction to Delta format
- Describe architectural differences between Lakehouse and Data Warehouse
- Review Delta format and its use with Lakehouse
- Contrast the different tools used to update and load each
- Differentiate uses for each Lakehouse and Data Warehouse
- Review the use of SQL Endpoint and how it can be used to examine the Lakehouse data
Data Warehouse
- Describe a data warehouse in Microsoft Fabric
- Detail methods for updating the Data Warehouse
- Contrast the methods described for the data warehouse with the methods used for a Lakehouse
Semantic Modelling for Data Engineers
- Discuss methods for creation
- Security Options and impact on other security methods
- Sharing Models
- Update Methodology for the Semantic model
SQL Server DB
- Determine when this structure is a good choice for Fabric
- Review methods for creating and developing
- Monitoring performance of SQL Server DB
Data Transformation – Day 3
Understanding and using Pipelines
- Introduction to pipelines
- Describing pipeline features
- Orchestrating repeatable processes
- Using the Copy command and notebooks
Advanced Pipeline implementation
- Pipeline design for Medallion Architecture
- Data Factory Data Flows
- Pipeline Triggers use and development
- Use of variables in pipeliens
Data Flow Gen 2
- History and Relation to Power BI Dataflows
- Data Cleansing Walkthrough
- Understanding M language
- Use of Parameters in Data Flow Gen 2
Lakehouse Deep dive
- Loading data from different file types into Lakehouses
- Creating views within Lakehouses, a best practice for Power BI?
- Understanding Lakehouse security•Data Masking
Transformation with Python and Spark – Day 4
Apache Spark
- Introduction to Spark
- Understanding the default connector
- Walkthrough through Spark environment
- Using Spark Job Definitions
Spark and notebooks
- Spark notebooks properties and features in Fabric
- Data sources and use within Spark notebooks
- Spark SQL and other design patterns
Tools for Spark development for Fabric
- Setting up VS Code for Fabric
- Git Hub Copilot
- AI development in the Fabric UI
- Data Wrangler
Parameterizing your Code
- Different techniques for creating parameters
- Passing parameters from pipelines
- Data connections and parameters
Implementation – Day 5
Tools for Migrating Code to Production
- Introduction to Development Pipelines
- Implementing Variable Libraries
- Development Pipelines techniques for moving Direct Lake models
Integrating Source control in Microsoft Fabric
- CI/CD Deployment
- Branching and Merging
- Appropriate Workspace configuration for Source Control
- Use with to Development Pipelines
Administration and Monitoring
- One Lake Data Hub
- Monitor Hub for Pipelines
- Monitoring with Real time data
Data Security in Fabric
- Using One Lake Security
- Implementing Security in all items
- Securing your data in with Fabric
Using Fabric Data to create a Chat Bot
- Using Data Agents in Fabric
- Creating a LLM model grounded with Fabric Data Agents
- Developing a chat bot in Fabric
Supporting Power BI with good semantic models
- Semantic models default and designed
- Direct Lake development
- Workspace organization for reporting
- Generating Reports with Fabric
Event Cancellation Policy
- Cancellations more than 14 days prior to the event: 25% cancellation charge applies
- Cancellations 7–14 days prior to the event: 50% cancellation charge applies
- Cancellations within 7 days of the event: 100% cancellation charge applies