DP-600T00-A - Implement analytics solutions using Microsoft Fabric
Course Description
This course covers how to prepare, enrich, and serve data for analysis by consumers such as data analysts, report developers and AI agents. It focuses on designing dimensional models and transforming data using dataflows, notebooks, and T-SQL across lakehouses, warehouses and eventhouses in Microsoft Fabric. The course also covers building and optimizing semantic models, managing the analytics development lifecycle and enforcing security and governance across data assets.
This advanced four-day course is intended for data professionals with experience in data modeling, transformation and analytics. Experience with SQL or DAX and with Power BI semantic models and reports is recommended; familiarity with KQL and Python is helpful.
4 Days
€1850.00
Who should attend
This course is intended for data professionals with experience in data modeling, transformation, and analytics. Learners should have experience translating business requirements into analytical measures using SQL or DAX. Experience building semantic models and reports in Power BI is recommended. Familiarity with KQL and Python is helpful but not required.Introduction to end-to-end analytics using Microsoft Fabric
Describe Microsoft Fabric and its end-to-end analytics capabilitiesExplore core workloads and how they work together
Identify how Fabric can meet enterprise analytics requirements
Discover and connect to data in OneLake
Understand Microsoft OneLake unified storageBrowse and discover data with the OneLake catalog
Create shortcuts to reference existing data
Discover streaming sources in Real-Time hub
Get started with lakehouses in Microsoft Fabric
Describe lakehouse features and capabilitiesIngest and transform data in a lakehouse
Query and analyze lakehouse data with SQL and Spark
Create a Microsoft Fabric lakehouse
Get started with data warehouses in Microsoft Fabric
Understand data warehouse fundamentals and Fabric warehousesQuery and transform data with T-SQL
Model data in a warehouse
Secure and monitor a Fabric warehouse
Get started with Real-Time Intelligence in Microsoft Fabric
Understand real-time data analytics and Real-Time IntelligenceIngest and transform real-time data
Store and query data in an eventhouse
Visualize real-time data and automate actions
Choose data stores in Microsoft Fabric
Compare lakehouse, warehouse, and eventhouse capabilitiesEvaluate analytical workload and data requirements
Select the appropriate Fabric data store for a business scenario
Design dimensional models for analytics in Microsoft Fabric
Describe dimensional schema typesDesign fact tables and dimension tables
Implement slowly changing dimensions
Design and implement a dimensional model
Transform data using Dataflows Gen2 in Microsoft Fabric
Understand and configure Dataflows Gen2Transform data with Power Query
Optimize performance with query folding
Load transformed data to lakehouse or warehouse destinations
Transform data using notebooks in Microsoft Fabric
Describe notebooks in Microsoft FabricShape and clean data with Spark SQL and PySpark
Combine and aggregate data
Write and size Delta tables
Transform data using T-SQL in Microsoft Fabric
Transform data with T-SQL queriesCreate views for reusable logic
Build stored procedures
Implement dimensional tables in a Fabric warehouse
Create DAX calculations in semantic models
Create calculated tables and calculated columnsUnderstand implicit and explicit measures
Create measures for analytical requirements
Use iterator functions in DAX
Design semantic models for scale in Microsoft Fabric
Choose an appropriate semantic model storage modeDesign star-schema relationships for clarity and performance
Design scalable calculation patterns
Configure settings for large datasets and concurrent use
Optimize semantic model performance
Use Performance analyzer to diagnose bottlenecksOptimize DAX calculations
Reduce cardinality and implement aggregations
Troubleshoot common semantic model performance issues
Enforce semantic model security
Implement row-level security (RLS)Apply object-level security (OLS)
Configure dynamic security patterns
Test security and manage roles
Manage the semantic model development lifecycle
Create reusable Power BI assetsManage Power BI content with Git version control
Inspect and manage models with the XMLA endpoint and SemPy
Deploy through pipelines and monitor downstream impact
Prepare semantic models for AI in Power BI and Microsoft Fabric
Explain how semantic model context guides Copilot and data-agent answersDesign a focused gold layer for AI consumption
Select and apply appropriate preparation-for-AI controls
Validate semantic model AI readiness
Understand Microsoft Fabric IQ fundamentals
Understand Fabric IQ as an enterprise intelligence layerExplore ontologies, data agents, Graph, semantic models, operations agents, and planning
Apply an ontology modelling approach based on business concepts
Create an ontology with Fabric IQ
Choose between manual and generated ontology approachesBuild an ontology manually or generate one from a semantic model
Connect an ontology to OneLake data sources
Configure relationships and preview the ontology
Secure data access in Microsoft Fabric
Understand the layered Fabric security modelConfigure workspace roles and item permissions
Apply granular permissions with T-SQL
Use OneLake security roles to control data access
Secure a Microsoft Fabric data warehouse
Explore dynamic data maskingImplement row-level and column-level security
Configure granular SQL permissions using T-SQL
Protect sensitive warehouse data
Govern analytics data in Microsoft Fabric
Classify and protect data with sensitivity labelsUse endorsement and documentation to improve trust and discoverability
Govern analytics data for AI consumption
Use OneLake catalog features for data-estate management
Microsoft FabricOneLakeLakehouseData WarehouseEventhouseReal-Time IntelligenceDataflows Gen2Power QueryApache SparkPySparkT-SQLDAXPower BISemantic ModelsFabric IQAIData GovernanceData Security