Lakehouse (Data Lake & Enterprise Data Warehouse) – Microsoft Fabric, Data Factory, DAX & Power BI

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Course Description

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

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Inventory controllers and stock control personnel.
Logistics and distribution supervisors.
Internal auditors and operational compliance personnel.
Supply chain and operations professionals.
Personnel responsible for reviewing warehouse controls and performance.

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Basic familiarity with warehouse, logistics, or inventory activities.
General understanding of business processes and operational documentation.
Basic numerical skills, including percentages and simple calculations.
Basic spreadsheet skills are helpful.
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Course Outline

Day 1: Warehouse Processes, Layout, and Storage Controls Module 1: Warehouse Operations and Process Controls

Warehouse roles within the supply chain.
End-to-end flow: receiving, put-away, storage, replenishment, picking, packing, and dispatch.
Physical stock movement and corresponding system transactions.
Responsibilities, authorization, and segregation of duties.
Common operational weaknesses and audit evidence.

Module 2: Warehouse Layout and Storage Systems

Functional zones, movement paths, and space utilization.
Receiving, storage, picking, staging, and dispatch areas.
Storage options: floor stacking, shelving, pallet racking, and bin storage.
Fixed and dynamic location systems.
Product slotting based on movement, size, handling needs, and compatibility.
Layout bottlenecks and their effects on productivity and inventory control.

Module 3: Receiving and Put-Away

Purchase orders, delivery documents, and expected receipts.
Quantity, condition, and item identification checks.
Recording shortages, overages, damage, and discrepancies.
Controls for acceptance, quarantine, and release.
Put-away rules and location confirmation.
Traceability through labels, barcodes, lot numbers, and serial numbers.
Timely recording of receipts and stock movements.

Module 4: Storage, Stock Rotation, and Replenishment

Storage discipline and location integrity.
FIFO and FEFO principles, applications, and limitations.
Batch, lot, and expiry-date management.
Controls for slow-moving, obsolete, damaged, and expired stock.
Replenishment of picking locations.
Supervision and review of stock rotation compliance.

Day 2: Order Fulfillment and Inventory Accuracy Module 5: Picking Strategies and Order Fulfillment

Discrete, batch, zone, and wave picking.
Selecting picking strategies for different order profiles.
Pick paths, location sequencing, and travel reduction.
Replenishment coordination and stock availability.
Picking verification and error prevention.
Packing, staging, and dispatch confirmation.
Handling short picks, substitutions, cancellations, and returns.

Module 6: Inventory Records and Accuracy Controls

Item master data, units of measure, and location records.
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Transaction controls for receipts, transfers, issues, returns, and adjustments.
Common causes of physical and system stock mismatches.
Barcode scanning and WMS control points.
Inventory reconciliation and supporting documentation.
Measuring inventory accuracy and interpreting discrepancies.

Module 7: Cycle Counting and Physical Inventory

Cycle counting versus full physical inventory.
ABC classification and risk-based count priorities.
Count frequency based on value, movement, and discrepancy history.
Count preparation, blind counting, and movement controls.
Recounts, variance investigation, and adjustment approval.
Root cause analysis and corrective action follow-up.
Reviewing the effectiveness of the counting program.

Module 8: Shrinkage Reduction and Inventory Protection

Sources of shrinkage: loss, damage, theft, misplacement, and recording errors.
Access controls and accountability for stock movements.
Controls for high-value and sensitive inventory.
Returns, write-offs, scrap, and disposal controls.
Exception reporting and unusual transaction patterns.
Investigating recurring losses and unexplained adjustments.
Preventive and detective controls for shrinkage reduction.

Day 3: Performance Management, Process Review, and Improvement Module 9: Warehouse Productivity and Inventory KPIs

Receiving productivity and dock-to-stock time.
Put-away productivity and replenishment performance.
Picking productivity and order accuracy.
Inventory accuracy and cycle count variance.
Space utilization and storage capacity.
Shrinkage, damage, and expiry indicators.
Defining KPI formulas, data sources, and reporting boundaries.
Interpreting trends while balancing speed, accuracy, and service.

Module 10: Supervising and Auditing Warehouse Processes

Process mapping and identification of critical control points.
Reviewing procedures against actual warehouse practices.
Tracing transactions from documents to systems and physical stock.
Reviewing approvals, access rights, and segregation of duties.
Identifying control gaps and recurring process exceptions.
Documenting findings with clear supporting evidence.
Prioritizing corrective actions and tracking closure.

Module 11: Warehouse Improvement Planning

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Module 12: Capstone — Warehouse Operations and Inventory Improvement Plan

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