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- Virtual instructor Led Training
- Complete Hands-on Labs
- Softcopy of Courseware
- Learning Labs
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Certification: Microsoft Certified: Fabric Analytics Engineer Associate
Dynamics Edge courses and labs are enhanced Instructor-Led Training (ILT) materials designed specifically for live, guided instruction and follow a structured curriculum.
Our materials are intentionally different from Microsoft Learn paths in both structure and flow to better prepare for actual work, answer questions, real-time engagement, and deeper learning. Microsoft Learn paths are self-paced study resources.
You will Learn:
- Design and Implement Data Analytics Solutions in Microsoft Fabric
- Use Lakehouses and Medallion Architecture for Scalable Analytics
- Develop and Manage Data Pipelines with Data Factory and Dataflows Gen2
- Analyze Data with Apache Spark and Delta Lake in Microsoft Fabric
- Model, Visualize, and Secure Data Using Power BI
- Monitor, Optimize, and Govern Data Solutions
- Implement Advanced Analytics and AI Capabilities
Course Outline
Module 1: Introduction to End-to-End Analytics Using Microsoft Fabric
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Explore the capabilities of Microsoft Fabric for analytics
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Understand the unified analytics architecture
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Identify key roles within data teams using Fabric
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Learn how to enable Microsoft Fabric in your environment
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Recognize real-world scenarios for using Fabric
Module 2: Administer Microsoft Fabric
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Understand the architecture and components of Microsoft Fabric
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Define the role and responsibilities of a Fabric administrator
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Configure security settings and access control
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Govern data using Fabric’s built-in governance tools
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Align Fabric administration with organizational policies
Module 3: Ingest Data with Dataflows Gen2 in Microsoft Fabric
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Understand the role of Dataflows Gen2 for data ingestion
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Explore the Power Query Online interface in Fabric
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Create multi-step ingestion and transformation flows
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Integrate Dataflows Gen2 with pipeline orchestration
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Manage and schedule dataflow executions
Module 4: Ingest Data with Spark and Microsoft Fabric Notebooks
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Connect to data using Apache Spark in Fabric notebooks
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Write and read data into a Fabric lakehouse
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Use Python for scalable ingestion workflows
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Understand the role of Spark sessions in Fabric
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Evaluate the impact of Spark ingestion on analytics
Module 5: Use Data Factory Pipelines in Microsoft Fabric
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Understand the structure and purpose of pipelines
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Use the Copy Data activity for data movement
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Leverage built-in pipeline templates
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Run and monitor pipelines in the Fabric portal
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Integrate pipelines with other Fabric services
Module 6: Get Started with Lakehouses in Microsoft Fabric
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Define the concept of a lakehouse and its benefits
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Explore the Microsoft Fabric lakehouse architecture
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Ingest data from various sources into the lakehouse
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Transform and prepare data for analytics
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Manage and visualize data stored in lakehouses
Module 7: Organize a Fabric Lakehouse Using Medallion Architecture Design
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Describe the Bronze, Silver, and Gold data layers
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Implement medallion architecture in Microsoft Fabric
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Transform and move data across layers
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Query and report from curated datasets
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Apply lakehouse best practices for governance and scaling
Module 8: Use Apache Spark in Microsoft Fabric
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Prepare the environment for running Spark code
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Run Spark code interactively in Fabric notebooks
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Process data using Spark DataFrames
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Query data using Spark SQL
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Visualize results directly in the Spark notebook
Module 9: Work with Delta Lake Tables in Microsoft Fabric
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Understand the Delta Lake storage format
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Create and manage Delta tables
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Use Delta tables for time travel and version control
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Process streaming and batch data using Delta
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Analyze Delta table data with Spark
Module 10: Get Started with Data Warehouses in Microsoft Fabric
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Understand relational data warehouse principles
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Create and configure a Fabric data warehouse
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Query and transform structured data
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Prepare data for downstream reporting
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Secure and monitor access to warehouse data
Module 11: Load Data into a Microsoft Fabric Data Warehouse
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Explore strategies for loading structured data
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Use pipelines to ingest data into a warehouse
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Load and transform data using T-SQL
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Combine Dataflows Gen2 for enriched loading
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Automate data loading processes for scalability
Module 12: Query a Data Warehouse in Microsoft Fabric
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Use the SQL query editor to run custom queries
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Explore data using the visual query editor
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Connect to the warehouse with external tools
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Write optimized queries for performance
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Explore schema and metadata with built-in tools
Module 13: Monitor a Microsoft Fabric Data Warehouse
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Monitor storage and capacity usage
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Track and interpret current query activity
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Review query performance and duration
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Identify and address system bottlenecks
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Use Fabric monitoring dashboards for insights
Module 14: Understand Scalability in Power BI
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Recognize the importance of scalable models
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Implement data modeling best practices
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Configure Power BI for large dataset storage
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Build and optimize star schema models
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Scale performance for enterprise-level analytics
Module 15: Create Power BI Model Relationships
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Understand the fundamentals of model relationships
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Set up and manage one-to-one and many-to-many relationships
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Use DAX to define and evaluate relationships
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Troubleshoot ambiguous relationships and errors
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Visualize model relationships using the diagram view
Module 16: Use Tools to Optimize Power BI Performance
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Use Performance Analyzer to assess report performance
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Optimize models with Best Practice Analyzer
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Troubleshoot DAX queries using DAX Studio
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Identify bottlenecks in Power BI reports
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Improve query folding and memory efficiency
Module 17: Enforce Power BI Model Security
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Implement row-level security (RLS) for data protection
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Use object-level security (OLS) to restrict access to tables
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Define roles and permissions for Power BI users
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Apply modeling best practices for secure deployment
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Validate and test security configurations in reports
Module 18: Create a Star Schema Model in Power BI
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Understand the benefits of the star schema design
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Create fact and dimension tables for modeling
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Define clear relationships between data entities
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Optimize column usage and cardinality
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Implement naming conventions and documentation
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