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Overview
This comprehensive course offers an in-depth exploration of Microsoft Fabric, a unified analytics platform. Participants will learn the essentials of end-to-end analytics, starting with foundational concepts and progressing to advanced functionalities. The course covers the design and use of lakehouses, data pipelines, Apache Spark integration, data warehousing, and Power BI semantic modeling. Additionally, it emphasizes real-time intelligence, advanced query techniques, and robust security practices. With a focus on scalability, performance optimization, and administrative management, this course equips data professionals with the skills necessary to implement and manage analytics solutions effectively using Microsoft Fabric.
Prerequisites
Learners should have a mix of data, technical, and business skills to effectively work with Microsoft Fabric and apply it to real-world analytics scenarios.
- Core analytics knowledge – Understanding of basic data analysis concepts and the end-to-end data lifecycle, including ingestion, transformation, storage, and visualisation.
- Database and query skills – Experience working with relational databases, including writing SQL queries and understanding schemas, tables, joins, and relationships.
- ETL and data integration – Familiarity with extract, transform, and load processes and how data is moved across systems.
- Cloud and data platform awareness – Basic knowledge of cloud-based data platforms, ideally Microsoft Azure, including data lakes and data warehouses.
- Power BI fundamentals – Experience creating reports and dashboards in Power BI, including measures, calculated columns, and data relationships.
- Programming and data languages – Exposure to programming concepts such as Python or R, and analytical languages such as DAX. Familiarity with Apache Spark concepts is beneficial.
- Security basics – Awareness of data security principles, including row-level security and access control mechanisms.
- Business understanding – Ability to interpret business requirements and translate them into analytics solutions.
Target audience
The primary audience for this course is data professionals with experience in data modeling and analytics. DP-600 is designed for professionals who want to use Microsoft Fabric to create and deploy enterprise-scale data analytics solutions.
This course is designed for experienced data professionals skilled at data preparation, modeling, analysis, and visualization, such as the PL-300: Power BI Data Analyst certification. Learners should have prior experience with one of the following programming languages: Structured Query Language (SQL), Kusto Query Language (KQL), or Data Analysis Expressions (DAX).
Delegates will learn how to
- Prepare and transform data for analytics using Microsoft Fabric
- Design and implement scalable analytics data models
- Build and manage enterprise analytics solutions
- Enable data consumption through semantic models and reporting
- Monitor, optimise, and manage analytics solutions lifecycle
- Apply security and governance to analytics data
Outline
Explore end-to-end analytics with Microsoft Fabric
- Explain what Microsoft Fabric is and why it unifies analytics workloads
- Identify Fabric workloads and map them to common data roles
- Describe how OneLake enables AI-powered analytics
Discover and connect to data in OneLake
- Explain how OneLake provides unified storage across Microsoft Fabric
- Browse and discover data using the OneLake catalog
- Create shortcuts to connect to data without duplication
- Discover streaming data in Real-Time hub
- Exercise: Discover data in OneLake
Get started with lakehouses in Microsoft Fabric
- Describe the core features and capabilities of lakehouses
- Create a lakehouse and ingest data
- Query lakehouse data using SQL and Spark
- Connect Power BI to lakehouse data with Direct Lake
- Exercise: Create and query a lakehouse
Get started with data warehouses in Microsoft Fabric
- Describe data warehouse concepts and dimensional modelling fundamentals
- Create tables and load data into a Fabric warehouse
- Query and transform data using T-SQL and the visual query editor
- Model warehouse data for reporting and downstream consumption
- Exercise: Analyse data in a warehouse
Get started with Real-Time Intelligence in Microsoft Fabric
- Describe real-time analytics, events, and streams
- Explain how Real-Time Intelligence components work together
- Write Kusto Query Language queries to select, filter, and aggregate streaming data
- Explain how Real-Time dashboards and activator complete the real-time analytics cycle
- Exercise: Get started with Real-Time Intelligence
Choose data stores in Microsoft Fabric
- Identify common analytics workloads and the most suitable data store for each
- Compare lakehouse, warehouse, and eventhouse options
- Recommend the appropriate data store for specific business scenarios
Design dimensional models for analytics in Microsoft Fabric
- Explain how dimensional models support the curated analytics layer
- Design fact tables with appropriate grain and measures
- Design dimension tables using surrogate keys and denormalised attributes
- Select slowly changing dimension patterns for evolving data
- Exercise: Design and implement a star schema
Transform data using Dataflows Gen2 in Microsoft Fabric
- Explain when Dataflows Gen2 should be used instead of code-based approaches
- Apply transformations and configure output destinations
- Optimise performance using query folding
- Exercise: Transform data with Dataflows Gen2
Transform data using notebooks in Microsoft Fabric
- Describe Fabric notebooks and Spark-based processing
- Transform data using PySpark and Spark SQL
- Write and optimise Delta tables for analytics workloads
- Exercise: Transform data with notebooks
Transform data using T-SQL in Microsoft Fabric
- Explain the differences between T-SQL and Spark SQL workloads
- Create reusable objects for business logic and automated loading
- Implement loading patterns for dimensional models
- Exercise: Transform data with T-SQL
Create DAX calculations in semantic models
- Differentiate calculated tables, calculated columns, and measures
- Explain row context and filter context
- Create measures using advanced DAX patterns and iterator functions
- Exercise: Create DAX calculations
Design semantic models for scale in Microsoft Fabric
- Select storage modes based on performance and data freshness requirements
- Design efficient star schema relationships
- Create scalable calculation patterns
- Configure settings for enterprise-scale consumption
- Exercise: Design a semantic model for scale
Optimise semantic model performance
- Use Performance Analyzer to identify bottlenecks
- Optimise DAX calculations
- Reduce cardinality to improve efficiency
- Implement aggregations for large datasets
- Exercise: Diagnose and fix a slow report
Enforce semantic model security
- Implement row-level security using static and dynamic DAX filters
- Apply object-level security to tables and columns
- Test security configurations and manage role membership
- Explain how security affects AI-enabled consumption scenarios
- Exercise: Implement row-level security for a semantic model
Manage the semantic model development lifecycle
- Create reusable Power BI assets for consistency
- Version control semantic models using Power BI Project files and Git
- Validate models programmatically using SemPy
- Deploy through pipelines and monitor refresh operations
- Exercise: Validate and deploy a semantic model
Prepare the semantic layer for AI
- Explain how AI tools consume semantic model metadata through grounding
- Design AI-ready gold layers and naming conventions
- Configure Prep for AI features, including schema, verified answers, and instructions
- Describe how semantic models connect to enterprise ontology within Fabric IQ
- Exercise: Prepare a semantic model for AI
Create an ontology with Fabric IQ
- Explain the purpose of an ontology and how it differs from a semantic model
- Identify entity types, properties, keys, and relationships
- Generate an ontology from a Power BI semantic model
- Connect ontology definitions to data sources and preview results
- Exercise: Build an ontology from a semantic model
Secure data access in Microsoft Fabric
- Describe the multi-layer security model in Microsoft Fabric
- Configure workspace roles and item permissions using least-privilege principles
- Apply granular T-SQL security to lakehouse and warehouse data
- Create OneLake data access roles for folder-level role-based access control
- Exercise: Secure data access in Microsoft Fabric
Govern analytics data in Microsoft Fabric
- Apply sensitivity labels and understand lineage propagation
- Use endorsement and documentation features to improve trust and discoverability
- Describe how governance signals influence AI readiness and behaviour
- Exercise: Govern analytics data
Power BI course selector infographic
| Course code | Course title |
| QAPBIRDB | Microsoft Power BI Reports and Dashboards for Business Users |
| QAPBIDE | Microsoft Power BI Desktop Essentials |
| QAPBIDT3 | Power BI Desktop for Business Users |
| QAPBIDESK | Power BI Desktop for Technical Users |
| QAPBIDESK2 | Power BI Desktop Intermediate |
| QAPBIVIS | Power BI Data Visualisation |
| QAPBIDAX | Using DAX in Power BI |
| MPL300 | Microsoft Power BI Data Analyst |
| MDP600 | Microsoft Fabric Analytics Engineer |
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