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Microsoft Data & Power BIImplement analytics solutions using Microsoft Fabric (DP-600T00/ DP-600 Certification Prep)
Implement analytics end to end in Microsoft Fabric.
4 daysIntermediate22 modulesDP-600T00
About this course
Implement analytics end to end in Microsoft Fabric. You will connect to data in OneLake, build lakehouses and warehouses, prepare and model data, build semantic models and manage analytics at scale, preparing for DP-600.
Curriculum
22 modules
- Explore end-to-end analytics with Microsoft Fabric
- Explore data teams and Microsoft Fabric
- Enable and use Microsoft Fabric
- Module assessment
- Understand OneLake
- Browse and connect to data in OneLake
- Discover streaming data in Real-Time hub
- Describe lakehouse features and capabilities
- Ingest and transform data in a lakehouse
- Query and analyze lakehouse data
- Module assessment
- Understand data warehouses
- Understand data warehouses in Fabric
- Query and transform data
- Model data in a warehouse
- Secure and monitor a warehouse
- Module assessment
- What is real-time data analytics?
- Real-Time Intelligence in Microsoft Fabric
- Ingest and transform real-time data
- Store and query real-time data
- Visualize real-time data
- Automate actions
- Module assessment
- Describe analytical data store options
- Evaluate lakehouse capabilities
- Evaluate warehouse capabilities
- Evaluate eventhouse capabilities
- Case study - Choose data stores for an integrated analytics solution
- Module assessment
- Describe dimensional schema types
- Design fact tables
- Design dimension tables
- Implement slowly changing dimensions
- Understand Dataflows Gen2
- Transform data with Power Query
- Optimize Dataflows Gen2 performance
- Describe notebooks in Fabric
- Shape and clean data
- Combine and aggregate data
- Write and size Delta tables
- Transform data with T-SQL queries
- Create views for reusable logic
- Build stored procedures
- Implement dimensional tables
- Create calculated tables
- Create calculated columns
- Understand implicit measures
- Create explicit measures
- Use iterator functions
- Choose a storage mode
- Design star schema for semantic models
- Design scalable calculations
- Configure settings for scale
- Module assessment
- Use Performance analyzer to diagnose issues
- Optimize DAX calculations
- Reduce cardinality for better performance
- Implement aggregations
- Troubleshoot common performance issues
- Implement row-level security
- Apply object-level security
- Test security and manage roles
- Module assessment
- Create reusable Power BI assets
- Manage Power BI content in version control
- Manage semantic models with the XMLA endpoint
- Deploy content through stages
- Maintain and monitor semantic models
- Module assessment
- Understand what AI needs from your data
- Design gold layers with AI in mind
- Prepare a semantic model for AI
- From semantic models to enterprise ontology
- Validate AI readiness
- Module assessment
- Get started with Fabric IQ
- Explore Microsoft Fabric IQ components
- Understand the ontology modeling paradigm
- Module assessment
- Choose an ontology creation approach
- Build an ontology manually
- Generate an ontology from a Power BI semantic model
- Connect an ontology to data
- Configure ontology relationships
- Preview the ontology
- Module assessment
- Understand the Fabric security model
- Configure workspace and item permissions
- Apply granular permissions
- Module assessment
- Explore dynamic data masking
- Implement row-level security
- Implement column-level security
- Configure SQL granular permissions using T-SQL
- Module assessment
- Govern data in Microsoft Fabric
- Why use Microsoft Purview with Microsoft Fabric?
- Govern data in the Microsoft Purview hub
- Module assessment
- Classify and protect data in Microsoft Fabric
- Endorse and document data assets
- Govern data for AI consumption
Who it's for
Analytics engineers working toward Fabric Analytics Engineer Associate.
Before you start
- PL-300 certification or equivalent Power BI expertise
- Experience with SQL, KQL, or DAX
- Familiarity with enterprise-level data modeling, transformation, and deployment
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