DP-600 Microsoft Fabric Analytics Engineer Training

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

1. Explore the capabilities of Microsoft Fabric for analytics

2. Understand the unified analytics architecture

3. Identify key roles within data teams using Fabric

4. Learn how to enable Microsoft Fabric in your environment

5. Recognize real-world scenarios for using Fabric

Module 2: Administer Microsoft Fabric

1. Understand the architecture and components of Microsoft Fabric

2. Define the role and responsibilities of a Fabric administrator

3. Configure security settings and access control

4. Govern data using Fabric’s built-in governance tools

5. Align Fabric administration with organizational policies

Module 3: Ingest Data with Dataflows Gen2 in Microsoft Fabric

1. Understand the role of Dataflows Gen2 for data ingestion

2. Explore the Power Query Online interface in Fabric

3. Create multi-step ingestion and transformation flows

4. Integrate Dataflows Gen2 with pipeline orchestration

5. Manage and schedule dataflow executions

Module 4: Ingest Data with Spark and Microsoft Fabric Notebooks

1. Connect to data using Apache Spark in Fabric notebooks

2. Write and read data into a Fabric lakehouse

3. Use Python for scalable ingestion workflows

4. Understand the role of Spark sessions in Fabric

5. Evaluate the impact of Spark ingestion on analytics

Module 5: Use Data Factory Pipelines in Microsoft Fabric

1. Understand the structure and purpose of pipelines

2. Use the Copy Data activity for data movement

3. Leverage built-in pipeline templates

4. Run and monitor pipelines in the Fabric portal

5. Integrate pipelines with other Fabric services

Module 6: Get Started with Lakehouses in Microsoft Fabric

1. Define the concept of a lakehouse and its benefits

2. Explore the Microsoft Fabric lakehouse architecture

3. Ingest data from various sources into the lakehouse

4. Transform and prepare data for analytics

5. Manage and visualize data stored in lakehouses

Module 7: Organize a Fabric Lakehouse Using Medallion Architecture Design

1. Describe the Bronze, Silver, and Gold data layers

2. Implement medallion architecture in Microsoft Fabric

3. Transform and move data across layers

4. Query and report from curated datasets

5. Apply lakehouse best practices for governance and scaling

Module 8: Use Apache Spark in Microsoft Fabric

1. Prepare the environment for running Spark code

2. Run Spark code interactively in Fabric notebooks

3. Process data using Spark DataFrames

4. Query data using Spark SQL

5. Visualize results directly in the Spark notebook

Module 9: Work with Delta Lake Tables in Microsoft Fabric

1. Understand the Delta Lake storage format

2. Create and manage Delta tables

3. Use Delta tables for time travel and version control

4. Process streaming and batch data using Delta

5. Analyze Delta table data with Spark

Module 10: Get Started with Data Warehouses in Microsoft Fabric

1. Understand relational data warehouse principles

2. Create and configure a Fabric data warehouse

3. Query and transform structured data

4. Prepare data for downstream reporting

5. Secure and monitor access to warehouse data

Module 11: Load Data into a Microsoft Fabric Data Warehouse

1. Explore strategies for loading structured data

2. Use pipelines to ingest data into a warehouse

3. Load and transform data using T-SQL

4. Combine Dataflows Gen2 for enriched loading

5. Automate data loading processes for scalability

Module 12: Query a Data Warehouse in Microsoft Fabric

1. Use the SQL query editor to run custom queries

2. Explore data using the visual query editor

3. Connect to the warehouse with external tools

4. Write optimized queries for performance

5. Explore schema and metadata with built-in tools

Module 13: Monitor a Microsoft Fabric Data Warehouse

1. Monitor storage and capacity usage

2. Track and interpret current query activity

3. Review query performance and duration

4. Identify and address system bottlenecks

5. Use Fabric monitoring dashboards for insights

Module 14: Understand Scalability in Power BI

1. Recognize the importance of scalable models

2. Implement data modeling best practices

3. Configure Power BI for large dataset storage

4. Build and optimize star schema models

5. Scale performance for enterprise-level analytics

Module 15: Create Power BI Model Relationships

1. Understand the fundamentals of model relationships

2. Set up and manage one-to-one and many-to-many relationships

3. Use DAX to define and evaluate relationships

4. Troubleshoot ambiguous relationships and errors

5. Visualize model relationships using the diagram view

Module 16: Use Tools to Optimize Power BI Performance

1. Use Performance Analyzer to assess report performance

2. Optimize models with Best Practice Analyzer

3. Troubleshoot DAX queries using DAX Studio

4. Identify bottlenecks in Power BI reports

5. Improve query folding and memory efficiency

Module 17: Enforce Power BI Model Security

1. Implement row-level security (RLS) for data protection

2. Use object-level security (OLS) to restrict access to tables

3. Define roles and permissions for Power BI users

4. Apply modeling best practices for secure deployment

5. Validate and test security configurations in reports

Module 18: Create a Star Schema Model in Power BI

1. Understand the benefits of the star schema design

2. Create fact and dimension tables for modeling

3. Define clear relationships between data entities

4. Optimize column usage and cardinality

5. Implement naming conventions and documentation