Lakehouse (Data Lake & Enterprise Data Warehouse) – Microsoft Fabric, Data Factory, DAX & Power BI Overview
The Lakehouse (Data Lake & Enterprise Data Warehouse) – Microsoft Fabric, Data Factory, DAX & Power BI Training Course provides participants with practical knowledge and skills for designing, building, integrating, transforming, analyzing, and visualizing enterprise data using the Microsoft data and analytics ecosystem.
The course introduces modern Lakehouse architecture, combining the scalability and flexibility of a Data Lake with the structured analytics capabilities of an Enterprise Data Warehouse (EDW). Participants learn how Microsoft Fabric provides an integrated analytics environment covering data ingestion, data engineering, Lakehouse and Warehouse storage, semantic modeling, analytics, and business intelligence.
The training covers Microsoft Fabric, OneLake, Fabric Data Factory, Lakehouse, Delta tables, Fabric Warehouse, SQL, semantic models, DAX, and Power BI. Participants progress through the complete data lifecycle—from ingesting data from multiple sources and transforming it into analytics-ready structures to creating enterprise data models, DAX calculations, dashboards, and reports.
The course is designed as a general Microsoft Lakehouse program and is suitable for organizations implementing or modernizing enterprise data platforms and business intelligence solutions.
Duration 5 Days – 40 hrs.
Objectives
- Explain the principles and benefits of modern Data Lake, Data Warehouse, and Lakehouse architectures.
- Understand the architecture and major workloads of Microsoft Fabric.
- Navigate Microsoft Fabric workspaces and manage analytics assets.
- Understand the role of OneLake as the unified data foundation of Microsoft Fabric.
- Design a Lakehouse architecture for enterprise analytics.
- Ingest structured and semi-structured data into Microsoft Fabric.
- Use Fabric Data Factory pipelines and Dataflows Gen2 for data integration.
- Build repeatable ETL and ELT data pipelines.
- Organize and manage data within a Microsoft Fabric Lakehouse.
- Work with Delta tables and Lakehouse data.
- Understand medallion architecture concepts such as Bronze, Silver, and Gold data layers.
- Build and query an Enterprise Data Warehouse using Fabric Warehouse.
- Apply dimensional modeling concepts to analytical solutions.
- Create fact and dimension tables for enterprise reporting.
- Use SQL for querying and transforming analytical data.
- Build semantic models for business intelligence.
- Create calculated columns, measures, and business calculations using DAX.
- Apply filter and evaluation context concepts in DAX.
- Develop interactive reports and dashboards using Power BI.
- Connect Power BI with Microsoft Fabric data.
- Understand Direct Lake and other data connectivity approaches.
- Apply fundamental security, governance, performance, and administration practices.
- Design an end-to-end Microsoft Fabric Lakehouse and BI solution.
Target Audience
- Data Engineers
- Data Analysts
- BI Developers
- Business Intelligence Analysts
- Power BI Developers
- Database Developers
- Database Administrators
- Data Warehouse Developers
- ETL/ELT Developers
- Analytics Engineers
- Data Architects
- Solution Architects
- Cloud Data Professionals
- Application Developers working with enterprise data
- IT Professionals responsible for analytics platforms
- Technical consultants
- Reporting and MIS Professionals
- Technical team leads involved in data modernization projects
Prerequisites
- Basic understanding of databases and relational data concepts.
- Basic SQL knowledge, including SELECT statements, filtering, joins, and aggregation.
- General understanding of data analysis and reporting.
- Familiarity with Microsoft Power BI is beneficial but not mandatory.
- Basic understanding of cloud computing concepts is helpful.
- Previous exposure to ETL, data warehousing, or data integration is beneficial but not required.
- A Microsoft Fabric-enabled environment or appropriate training environment is recommended for hands-on exercises.
Course Outline
Day 1 – Modern Data Architecture and Microsoft Fabric Fundamentals
Module 1: Introduction to Modern Enterprise Data Platforms
- Evolution of enterprise data platforms
- Traditional databases and data warehouses
- Enterprise Data Warehouse (EDW) concepts
- Data Lake concepts
- Data Lake vs. Data Warehouse
- Introduction to the Lakehouse architecture
- Benefits and use cases of a Lakehouse
- Structured, semi-structured, and unstructured data
- Batch and analytical workloads
- Modern analytics architecture
Module 2: Introduction to Microsoft Fabric
- Microsoft Fabric overview
- Unified analytics concepts
- Microsoft Fabric architecture
- Fabric workloads and experiences
- Data Engineering
- Data Factory
- Data Warehouse
- Data Science and analytics
- Real-Time Intelligence overview
- Power BI integration
- Understanding Fabric capacities and workspaces
- Navigating the Microsoft Fabric environment
Module 3: OneLake and the Microsoft Fabric Data Foundation
- Introduction to OneLake
- OneLake architecture
- Centralized enterprise data storage
- Workspaces and data organization
- OneLake shortcuts
- Connecting distributed data sources
- Data accessibility across Fabric workloads
- Understanding open data formats
- Delta Lake concepts
- Parquet and Delta tables
Module 4: Designing a Microsoft Fabric Lakehouse
- Lakehouse components
- Files and tables
- Schemas and data organization
- Managed tables
- External data concepts
- Lakehouse SQL analytics endpoint
- Designing logical data layers
- Introduction to Medallion Architecture
- Bronze, Silver, and Gold layers
- Enterprise Lakehouse design considerations
Day 2 – Data Integration with Microsoft Fabric Data Factory
Module 5: Introduction to Fabric Data Factory
- Data Factory within Microsoft Fabric
- Data integration concepts
- ETL vs. ELT
- Data ingestion patterns
- Batch data integration
- Connecting to enterprise data sources
- Understanding connections and gateways
- Data movement within Fabric
Module 6: Building Data Pipelines
- Introduction to data pipelines
- Pipeline components
- Pipeline activities
- Copy Data activity
- Source and destination configuration
- Parameters and variables
- Expressions
- Dependencies and control flow
- Conditional processing
- Pipeline execution
- Monitoring pipeline runs
- Troubleshooting common pipeline issues
Module 7: Dataflows Gen2
- Introduction to Dataflows Gen2
- Dataflows vs. pipelines
- Connecting to data sources
- Power Query transformation concepts
- Cleaning and preparing data
- Filtering and shaping datasets
- Combining datasets
- Data type management
- Loading transformed data into Fabric
- Selecting appropriate destinations
Module 8: Building Enterprise Data Ingestion Processes
- Full data loads
- Incremental data loading concepts
- Data validation
- Handling data quality issues
- Reusable pipeline design
- Parameterized pipelines
- Pipeline orchestration
- Scheduling data workloads
- Monitoring ingestion processes
- Designing reliable ETL/ELT workflows
Day 3 – Lakehouse, Data Engineering and Enterprise Data Warehouse
Module 9: Working with Lakehouse Data
- Lakehouse data organization
- Files and folders
- Tables and schemas
- Delta table fundamentals
- Creating and managing tables
- Loading data into Lakehouse tables
- Querying Lakehouse data
- SQL analytics endpoint
- Working with Lakehouse metadata
- Data preparation for downstream analytics
Module 10: Implementing Medallion Architecture
- Medallion architecture principles
- Bronze/raw data layer
- Silver/validated and transformed data layer
- Gold/business-ready data layer
- Data cleansing strategies
- Data standardization
- Data enrichment
- Handling duplicate and invalid records
- Designing scalable transformation flows
- Preparing curated datasets for analytics
Module 11: Enterprise Data Warehouse Fundamentals
- Enterprise Data Warehouse architecture
- Lakehouse vs. Warehouse
- When to use a Lakehouse
- When to use a Fabric Warehouse
- Relational analytical workloads
- Dimensional modeling fundamentals
- Star schema
- Fact tables
- Dimension tables
- Surrogate keys
- Measures and business metrics
- Slowly changing dimensions overview
Module 12: Microsoft Fabric Warehouse
- Introduction to Fabric Warehouse
- Creating a Warehouse
- Tables and schemas
- Loading warehouse data
- SQL querying
- Joins and aggregations
- Views
- Data transformation using SQL
- Building dimensional structures
- Integrating Lakehouse and Warehouse data
- Preparing warehouse data for BI consumption
Day 4 – Semantic Modeling, DAX and Power BI
Module 13: Semantic Models in Microsoft Fabric and Power BI
- Semantic model concepts
- Business-friendly analytical models
- Tables and relationships
- Cardinality
- Filter direction
- Star schema optimization
- Date dimensions
- Hierarchies
- Measures and calculated columns
- Model organization
- Designing reusable enterprise semantic models
Module 14: Introduction to DAX
- DAX overview
- DAX syntax and data types
- Calculated columns vs. measures
- Creating basic measures
- SUM
- COUNT and COUNTROWS
- DISTINCTCOUNT
- AVERAGE
- MIN and MAX
- DIVIDE
- Working with variables
- Business KPI calculations
Module 15: DAX Context and Analytical Calculations
- Row context
- Filter context
- Context transition
- CALCULATE
- FILTER
- ALL
- VALUES
- RELATED
- Conditional expressions
- Date and time intelligence concepts
- Year-to-date calculations
- Month-to-date calculations
- Prior-period comparisons
- Growth calculations
- Percentage and ratio measures
Module 16: Power BI Reporting with Microsoft Fabric
- Power BI integration with Fabric
- Connecting to Lakehouse data
- Connecting to Warehouse data
- Import mode overview
- DirectQuery overview
- Direct Lake concepts
- Selecting appropriate connectivity modes
- Creating Power BI reports
- Tables and matrices
- Charts and visualizations
- Slicers and filters
- Drill-down and drill-through
- Conditional formatting
- KPI visualization
- Dashboard and report design principles
Day 5 – End-to-End Lakehouse Analytics, Governance and Optimization
Module 17: Building an End-to-End Microsoft Fabric Analytics Solution
- Identifying source systems
- Designing ingestion architecture
- Loading source data using Data Factory
- Implementing Bronze, Silver, and Gold layers
- Transforming and validating data
- Building curated Lakehouse tables
- Loading Enterprise Data Warehouse structures
- Building fact and dimension tables
- Creating a semantic model
- Developing DAX measures
- Creating Power BI reports
- Understanding the complete data-to-insight lifecycle
Module 18: Security and Data Governance Fundamentals
- Enterprise data security considerations
- Workspace access concepts
- Roles and permissions
- Data access control
- Power BI security concepts
- Row-Level Security fundamentals
- Protecting sensitive analytical data
- Data governance concepts
- Data lineage
- Data discovery
- Governance considerations across Fabric
- Managing trusted enterprise data assets
Module 19: Performance and Optimization
- Lakehouse performance considerations
- Data organization considerations
- Efficient pipeline design
- Optimizing transformation processes
- Warehouse query performance considerations
- Semantic model optimization
- DAX performance fundamentals
- Power BI report performance
- Direct Lake performance considerations
- Monitoring Fabric workloads
- Capacity and resource considerations
Module 20: Enterprise Lakehouse Solution Design
- Translating business requirements into data architecture
- Source-to-target architecture
- Selecting Lakehouse vs. Warehouse workloads
- Designing scalable ingestion pipelines
- Designing the Medallion data architecture
- Creating an enterprise dimensional model
- Developing analytical KPIs
- Building management dashboards
- Integrating Fabric, Data Factory, DAX, and Power BI
- Production deployment considerations
- Recommended practices for maintainable Microsoft Fabric solutions
