Module 1 – Microsoft Fabric Fundamentals
14 Lessons
Microsoft Fabric is Microsoft's end-to-end, cloud-based data and analytics platform. It brings data integration, data engineering, data warehousing, data science, real-time intelligence, and business intelligence into one unified SaaS platform.
Module Learning Path
Microsoft Fabric │ ▼ Why Fabric? │ ▼ Fabric Architecture │ ▼ OneLake │ ▼ SaaS vs PaaS │ ▼ Capacity & Licensing │ ▼ Workspaces │ ▼ Roles & Permissions │ ▼ End-to-End Analytics │ ▼ Create Fabric Trial │ ▼ Create Workspace │ ▼ Explore Fabric UI │ ▼ Fabric Data Items │ ▼ Fabric Administration │ ▼ Module Review
Lesson 1.1 – Introduction to Microsoft Fabric
What is Microsoft Fabric?
Microsoft Fabric is Microsoft's end-to-end, cloud-based data and analytics platform.
It brings together:
- Data integration
- Data engineering
- Data warehousing
- Data science
- Real-time intelligence
- Business intelligence
- Power BI
into one unified Software-as-a-Service (SaaS) platform.
In simple terms
Microsoft Fabric = One platform where you can collect data → store data → transform data → analyze data → build reports and insights.
Why Was Microsoft Fabric Introduced?
Traditionally, organizations often use multiple Azure services for analytics.
Azure Data Factory │ ▼ Data Movement │ ▼ Azure Data Lake Storage │ ▼ Azure Synapse / Databricks │ ▼ SQL / Data Warehouse │ ▼ Power BI
Although these services work together, teams still need to manage:
- Separate resources
- Separate configurations
- Security
- Connections
- Monitoring
- Multiple storage locations
- Data movement
- Potentially multiple copies of data
Microsoft Fabric provides a more integrated experience by bringing these analytics capabilities together around a common storage foundation called OneLake.
Core Idea of Microsoft Fabric
MICROSOFT FABRIC
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
Data Integration Data Engineering Data Warehouse
│ │ │
└──────────────────┼──────────────────┘
│
▼
OneLake
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
Data Science Real-Time Intelligence Power BI
Major Microsoft Fabric Workloads
| Workload | Purpose |
|---|---|
| Data Factory | Data ingestion, integration, transformation, and orchestration |
| Data Engineering | Spark-based big-data processing, notebooks, and Lakehouses |
| Data Warehouse | SQL-based enterprise data warehousing |
| Data Science | Machine learning, experimentation, and predictive analytics |
| Real-Time Intelligence | Streaming and event-driven analytics |
| Power BI | Data modeling, visualization, dashboards, and reporting |
| OneLake | Unified storage foundation for Fabric |
What is OneLake?
OneLake is one of the most important concepts in Microsoft Fabric.
You can think of it as:
"OneDrive for organizational data."
Instead of every analytics workload maintaining completely separate storage, Fabric provides OneLake as the common data lake for the organization.
Fabric also supports OneLake Shortcuts, which allow Fabric to reference data in supported external locations without necessarily copying all of that data into another location.
Simple Real-World Example
SQL Server Salesforce Oracle Excel Files APIs │ ▼ Microsoft Fabric Data Factory │ ▼ OneLake / Lakehouse │ ▼ Data Engineering Clean + Transform Data │ ▼ Fabric Data Warehouse │ ▼ Power BI │ ▼ Sales Dashboard
A business user can finally see information such as:
- Total Sales
- Sales by Region
- Top Products
- Customer Trends
- Monthly Revenue
Fabric Terminology to Remember
Microsoft Fabric │ ├── Capacity │ ├── Workspace │ │ │ ├── Lakehouse │ ├── Warehouse │ ├── Pipeline │ ├── Notebook │ ├── Semantic Model │ └── Power BI Report │ └── OneLake
Azure Synapse / ADF Perspective
| Traditional Azure Environment | Microsoft Fabric |
|---|---|
| Azure Data Factory pipelines | Fabric Data Factory |
| ADLS Gen2 | OneLake |
| Synapse Spark | Fabric Data Engineering |
| Synapse SQL concepts | Fabric Warehouse / SQL analytics |
| Power BI | Integrated directly into Fabric |
| Separate Azure resources | More unified SaaS experience |
The mapping isn't always 1:1, but it is a useful starting point when learning Fabric.
Interview Definition
Microsoft Fabric is Microsoft's unified SaaS analytics platform that integrates data integration, engineering, data warehousing, data science, real-time analytics, and business intelligence on top of OneLake.
Quick Notes
Microsoft Fabric
- ✓ End-to-end analytics platform
- ✓ SaaS-based
- ✓ Unified analytics experience
- ✓ OneLake provides common storage
- ✓ Supports Lakehouse architecture
- ✓ Data Factory for integration
- ✓ Spark for data engineering
- ✓ SQL for data warehousing
- ✓ Data Science and AI capabilities
- ✓ Real-Time Intelligence
- ✓ Power BI for visualization
- ✓ Centralized governance and security capabilities
Key Takeaway
Data Sources │ ▼ Ingestion │ ▼ Storage │ ▼ Transformation │ ▼ Analytics │ ▼ Semantic Model │ ▼ Power BI │ ▼ Business Insights
Lesson 1.2 – Why Microsoft Fabric?
Why Fabric?
Microsoft Fabric was introduced to address a common problem in modern data platforms:
Organizations often use many separate tools for ingestion, storage, engineering, warehousing, data science, real-time analytics, and reporting.
Fabric brings these capabilities together into a unified SaaS analytics platform.
The main reason for Microsoft Fabric is:
Simplification: one platform, one common data lake, integrated analytics workloads, centralized governance, and a consistent user experience.
1. The Problem Before Fabric
Data Sources │ ▼ Azure Data Factory │ ▼ ADLS Gen2 │ ▼ Azure Databricks / Synapse Spark │ ▼ Synapse SQL / SQL Database │ ▼ Power BI │ ▼ Business Users
This architecture is powerful, but organizations may need to manage many separate resources.
ADF + Storage Account + Synapse + Spark + SQL + Power BI + Security + Monitoring + Connections
This can increase:
- Administration
- Integration effort
- Governance complexity
- Data movement
- Operational overhead
2. Fabric Provides a Unified Analytics Platform
MICROSOFT FABRIC
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
Data Factory Data Engineering Data Warehouse
│ │ │
├──────────────────┼──────────────────┤
│ │ │
▼ ▼ ▼
Data Science Real-Time Intel. Power BI
│
▼
OneLake
3. OneLake – One Data Lake for the Organization
One of the biggest reasons to use Fabric is OneLake.
OneDrive for organizational data.
Traditional Approach Azure Data Factory │ ▼ Storage / ADLS │ ▼ Azure Synapse │ ▼ Synapse Storage │ ▼ Data Science │ ▼ ML Data Copies │ ▼ Power BI │ ▼ Imported BI Data
Result:
- ❌ Multiple copies
- ❌ Multiple storage locations
- ❌ More data movement
- ❌ More management
- ❌ Potential data inconsistency
OneLake
│
Sales Data
│
┌──────────────┼──────────────┐
│ │ │
▼ ▼ ▼
Engineering Warehouse Power BI
4. Reduce Data Duplication
Consider a company with 1 TB of sales data.
Original Data → 1 TB Data Engineering → 1 TB Data Warehouse → 1 TB Analytics → 1 TB Power BI → Additional cached/imported data
Multiple copies can increase:
- Storage requirements
- Processing requirements
- Governance complexity
- Synchronization requirements
5. Easier Data Integration
SQL Server ─────┐
Oracle ─────────┤
Salesforce ─────┤
SAP ────────────┼──► Fabric Data Factory
Files ──────────┤ │
APIs ───────────┘ ▼
OneLake
Data engineers can create:
- Pipelines
- Connections
- Dataflows
- Transformations
6. Lakehouse + Warehouse
Data Engineers │ ▼ Spark / Python │ ▼ Lakehouse │ ▼ Business Data │ ▼ Warehouse │ ▼ SQL / BI
| Lakehouse | Warehouse |
|---|---|
| Good for large-scale data | Good for structured relational data |
| Structured, semi-structured, unstructured data | Enterprise reporting and SQL analytics |
| Data engineering | BI workloads |
7. Better Collaboration Between Teams
Fabric Workspace
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
Data Engineer SQL Developer BI Developer
│ │ │
Lakehouse Warehouse Power BI
8. SaaS Experience
Microsoft Fabric is delivered primarily as Software as a Service (SaaS).
Ingest Data │ ▼ Transform Data │ ▼ Model Data │ ▼ Analyze Data │ ▼ Create Reports
9. Integrated Power BI
OneLake │ ▼ Lakehouse / Warehouse │ ▼ Semantic Model │ ▼ Power BI │ ▼ Reports & Dashboards
One important technology here is Direct Lake.
10. Centralized Security and Governance
Users │ ├── Workspaces ├── Roles ├── Permissions ├── Data Access └── Governance
11. Shared Capacity
MICROSOFT FABRIC
│
▼
CAPACITY
│
┌───────────────┼───────────────┐
│ │ │
▼ ▼ ▼
Data Factory Data Engineering Warehouse
│ │ │
└───────────────┼───────────────┘
│
▼
Power BI / Other
12. End-to-End Analytics in One Platform
DATA SOURCES SQL Server Oracle Salesforce Files │ ▼ DATA FACTORY │ ▼ ONELAKE │ ▼ LAKEHOUSE │ ▼ DATA ENGINEERING │ ▼ WAREHOUSE │ ▼ SEMANTIC MODEL │ ▼ POWER BI │ ▼ BUSINESS DECISIONS
Major Benefits of Fabric
WHY MICROSOFT FABRIC ==================== ✓ Unified analytics platform ✓ OneLake – common organizational data lake ✓ Reduced unnecessary data movement ✓ SaaS-based architecture ✓ Integrated Data Factory ✓ Lakehouse + Warehouse ✓ Spark + SQL support ✓ Integrated Data Science ✓ Real-Time Intelligence ✓ Native Power BI integration ✓ Direct Lake capabilities ✓ Centralized security and governance ✓ Shared capacity model ✓ Better collaboration ✓ End-to-end analytics experience
Easy Interview Answer
Microsoft Fabric simplifies modern data analytics by bringing data integration, engineering, data warehousing, data science, real-time intelligence, and Power BI into one SaaS platform. It uses OneLake as a unified data foundation, which can reduce data movement and duplication while improving collaboration, governance, and end-to-end analytics.
Remember This
Traditional Approach │ ▼ Many Services │ ▼ Many Connections │ ▼ Multiple Storage Locations │ ▼ More Integration │ ▼ More Administration
Microsoft Fabric │ ▼ One Platform │ ▼ OneLake │ ▼ Integrated Workloads │ ▼ Shared Governance │ ▼ Analytics & Power BI
Lesson 1.3 – Fabric Architecture
Microsoft Fabric follows a unified SaaS analytics architecture in which multiple analytics workloads operate over a common data foundation called OneLake.
A simple way to remember Fabric architecture is:
Data Sources │ ▼ Fabric Workloads │ ▼ OneLake │ ▼ Analytics / AI │ ▼ Power BI │ ▼ Business Users
1. High-Level Fabric Architecture
The important point is that Fabric isn't simply a collection of independent tools.
Its workloads are designed to work together through:
- Shared storage
- Security
- Governance
- Workspaces
- Capacity
2. Major Layers of Fabric Architecture
1. Data Source Layer │ ▼ 2. Data Integration Layer │ ▼ 3. OneLake Storage Layer │ ▼ 4. Processing & Analytics Layer │ ▼ 5. Semantic / BI Layer │ ▼ 6. Security & Governance
Layer 1 – Data Sources
Everything starts with data.
Fabric can work with data coming from:
- On-premises systems
- Azure services
- Other clouds
- SaaS applications
- Files
- APIs
- Streaming/event sources
Layer 2 – Data Integration – Data Factory
The next layer is responsible for bringing and transforming data.
Fabric provides Data Factory for data integration and orchestration.
Pipelines
Used to orchestrate data movement and processing.
Copy Activities
Used to copy data between supported systems.
Dataflow Gen2
Used for low-code data transformation using Power Query-based experiences.
Layer 3 – Storage – OneLake
At the heart of Fabric architecture is OneLake.
OneDrive for data.
It provides a unified logical data lake for the organization.
OneLake │ ▼ Workspace A │ ├── Lakehouse └── Other Fabric Items │ Workspace B │ ├── Warehouse └── Other Fabric Items │ Workspace C │ └── Lakehouse
4. Open Data Format – Delta + Parquet
OneLake
│
▼
Lakehouse
│
├── Files
│ ├── CSV
│ ├── JSON
│ └── Parquet
│
└── Tables
│
▼
Delta Lake
│
▼
Parquet
Fabric's Lakehouse architecture relies heavily on open data formats, particularly Delta Lake tables stored using Parquet files.
5. OneLake Shortcuts
External Data │ ├── ADLS Gen2 ├── Amazon S3 └── Other Supported Locations │ ▼ OneLake Shortcut │ ▼ Lakehouse
6. Processing Layer – Data Engineering
Raw Data │ ▼ Lakehouse │ ▼ Spark │ ├── Notebook ├── PySpark ├── Spark SQL └── Data Transformation │ ▼ Clean Data
7. Medallion Architecture
ONELAKE
│
▼
┌─────────────┐
│ BRONZE │
│ Raw Data │
└──────┬──────┘
│
▼
┌─────────────┐
│ SILVER │
│ Clean Data │
└──────┬──────┘
│
▼
┌─────────────┐
│ GOLD │
│ Business │
│ Ready Data │
└──────┬──────┘
│
▼
Power BI
8. Data Warehouse Layer
Fabric also provides a Data Warehouse workload.
This is particularly useful for teams that prefer SQL and relational warehouse concepts.
- Tables
- Views
- T-SQL
- Stored procedures where supported
- Star schemas
- Fact tables
- Dimension tables
9. Lakehouse vs Warehouse
| Lakehouse | Warehouse |
|---|---|
| Optimized for engineering + analytics | Optimized for SQL analytics |
| Spark-friendly | T-SQL-friendly |
| Handles files and tables | Primarily relational tables |
| Common for data engineering | Common for BI/data warehousing |
| Delta-based tables | SQL warehouse experience |
| Data engineers | SQL developers / analysts |
10. Data Science Layer
OneLake │ ▼ Lakehouse │ ▼ Notebook │ ▼ Data Exploration │ ▼ Machine Learning │ ▼ Model │ ▼ Predictions
11. Real-Time Intelligence
Events / Streams │ ▼ Eventstream │ ▼ Eventhouse │ ▼ Real-Time Analytics │ ▼ Dashboards / Alerts / Power BI
12. Semantic Layer
Lakehouse / Warehouse │ ▼ Semantic Model │ ├── Measures └── Relationships │ ▼ Power BI
13. Direct Lake
OneLake │ ▼ Delta Tables │ ▼ Direct Lake │ ▼ Semantic Model │ ▼ Power BI
14. Power BI Layer
Prepared Data │ ▼ Semantic Model │ ▼ Power BI │ ▼ Reports │ ▼ Business Decisions
15. Workspace Layer
Microsoft Fabric │ ▼ Workspace │ ├── Lakehouse ├── Warehouse ├── Pipeline ├── Notebook ├── Semantic Model └── Power BI Report
16. Capacity Layer
Fabric Capacity
│
├── Workspace A
│ └── Lakehouse
│
├── Workspace B
│ └── Warehouse
│
└── Workspace C
└── Power BI
17. Security and Governance Layer
SECURITY & GOVERNANCE │ ├── Microsoft Entra ID ├── Workspace Permissions ├── Data Access Controls └── Governance │ ▼ Microsoft Fabric │ ▼ OneLake
18. Complete Fabric Architecture
DATA SOURCES
│
▼
FABRIC DATA FACTORY
│
▼
ONELAKE
│
┌───────────┼───────────┐
│ │ │
▼ ▼ ▼
Lakehouse Warehouse Real-Time
│ │ Intelligence
│ │
└─────┬─────┘
│
▼
Engineering / SQL / Data Science
│
▼
Semantic Model
│
▼
Power BI
│
▼
Business Insights
19. Real-World Example
SOURCE SYSTEMS SQL Server Oracle Salesforce CSV │ ▼ FABRIC DATA FACTORY │ ▼ BRONZE Raw Data │ ▼ Spark / Notebook │ ▼ SILVER Cleaned Data │ ▼ Transform / Model │ ▼ GOLD Business Ready Data │ ├── Lakehouse └── Warehouse │ ▼ Semantic Model │ ▼ Direct Lake │ ▼ Power BI │ ▼ Sales / Customer / Finance Dashboards
Fabric Architecture in One Line
Sources │ ▼ Data Factory │ ▼ OneLake │ ▼ Lakehouse / Warehouse │ ▼ Engineering / SQL / Data Science │ ▼ Semantic Model │ ▼ Power BI │ ▼ Business Insights
Interview Answer
Microsoft Fabric is a SaaS-based unified analytics architecture built around OneLake. Data can be ingested using Fabric Data Factory and processed using Data Engineering, Data Science, Data Warehouse, and Real-Time Intelligence workloads. Lakehouse and Warehouse items provide analytical data structures over the OneLake foundation, while semantic models and Power BI provide the business intelligence layer. Workspaces, Fabric capacity, security, and governance span the platform, creating an integrated end-to-end analytics environment.
Key Takeaway
OneLake is the data foundation, Fabric workloads are the processing and analytics engines, and Power BI is a major business-consumption layer.
Lesson 1.4 – OneLake
What is OneLake?
OneLake is the unified data lake built into Microsoft Fabric.
It provides a single logical data lake for an entire organization and acts as the common storage foundation for Fabric workloads.
OneDrive for data.
1. Why Do We Need OneLake?
Organization │ ▼ OneLake │ ▼ Multiple Workspaces │ ▼ Multiple Fabric Items │ ▼ Multiple Analytics Workloads
OneLake provides Fabric with a common storage foundation.
OneLake
│
┌───────────────────┼───────────────────┐
│ │ │
▼ ▼ ▼
Data Engineering Warehouse Power BI
│ │ │
└───────────────────┼───────────────────┘
│
Shared Data
2. OneLake Architecture
┌─────────────────────────────────────────────┐ │ ORGANIZATION │ │ │ │ OneLake │ │ │ │ ┌──────────────┬──────────────┬────────┐ │ │ │ Workspace A │ Workspace B │ C │ │ │ │ │ │ │ │ │ │ Lakehouse │ Warehouse │Lakehouse│ │ │ │ │ │ │ │ │ │ Tables/Files │ Tables │Tables/ │ │ │ │ │ │Files │ │ │ └──────────────┴──────────────┴────────┘ │ └─────────────────────────────────────────────┘
3. OneLake Hierarchy
OneLake
│
├── Workspace A
│ │
│ ├── Lakehouse
│ └── Other Items
│
├── Workspace B
│ │
│ ├── Warehouse
│ └── Other Items
│
└── Workspace C
│
└── Lakehouse
4. What Can Be Stored in OneLake?
Sales_Lakehouse
│
├── Tables
│ ├── Customers
│ ├── Products
│ ├── Orders
│ └── Sales
│
└── Files
├── CSV
├── JSON
├── Parquet
└── Other Files
5. OneLake and Delta Parquet
Delta Lake Table │ ├── Parquet Data ├── Transaction Log └── Table Metadata
Delta Lake Table = Parquet + Transaction Log + Table Metadata
6. One Copy, Multiple Analytics Engines
OneLake │ ▼ Sales Table │ ├── Spark │ ├── SQL │ └── Power BI
7. OneLake Shortcuts
External Storage │ ▼ Shortcut │ ▼ OneLake
8. Why Are Shortcuts Important?
Original Data │ ▼ Shortcut │ ▼ Fabric
9. OneLake + Lakehouse
OneLake
│
▼
Sales Lakehouse
│
├── Files
│ └── Raw Data
│
└── Tables
└── Delta Tables
10. Lakehouse Tables vs Files
| Files | Tables |
|---|---|
| Raw or semi-structured files | Managed analytical tables |
| CSV, JSON, Parquet | Customers, Products, Orders, Sales |
11. OneLake and Medallion Architecture
OneLake
│
▼
Sales Domain
│
├── BRONZE
│ └── Raw Data
│
├── SILVER
│ └── Clean Data
│
└── GOLD
└── Business Data
│
▼
Power BI
12. OneLake and Direct Lake
OneLake │ ▼ Delta Tables │ ▼ Direct Lake │ ▼ Semantic Model │ ▼ Power BI
13. OneLake vs ADLS Gen2
| Feature | ADLS Gen2 | OneLake |
|---|---|---|
| Purpose | General Azure data lake storage | Unified storage foundation for Fabric |
| Management | Azure resource | Fabric SaaS-managed experience |
| Storage Accounts | User creates/manages them | OneLake is provided through Fabric |
| Organization | Containers/directories | Fabric workspaces/items |
| Power BI Integration | Requires architecture/configuration | Deep Fabric integration |
| Fabric Workloads | External storage option | Native Fabric storage foundation |
14. OneLake vs Lakehouse
OneLake ≠ Lakehouse
Rather:
A Lakehouse is one of the Fabric data items that uses OneLake.
OneLake
│
├── Workspace: Sales
│ └── Sales Lakehouse
│
├── Workspace: Finance
│ └── Finance Lakehouse
│
└── Workspace: HR
└── HR Lakehouse
15. OneLake vs Warehouse
OneLake
│
├── Lakehouse
│ └── Spark + Delta Tables
│
└── Warehouse
└── SQL / Relational Analytics
16. OneLake Security
Microsoft Entra ID │ ▼ Fabric │ ▼ Workspace Access │ ▼ Fabric Item Access │ ▼ Data Access
17. Real-World Example
SOURCE SYSTEMS
SQL Server
Oracle
Salesforce
CSV
│
▼
Fabric Data Factory
│
▼
OneLake
│
▼
Retail Lakehouse
│
├── Files
│ └── Raw Data
│
└── Tables
├── Customer
├── Product
└── Sales
│
▼
Spark Notebooks
│
▼
Clean / Transform Data
│
▼
Business Tables
│
▼
Semantic Model
│
▼
Power BI
18. Advantages of OneLake
ONELAKE BENEFITS ================ ✓ One unified logical data lake ✓ Built directly into Microsoft Fabric ✓ Common data foundation across Fabric workloads ✓ Reduces unnecessary data duplication ✓ Supports open data formats ✓ Delta Lake / Parquet architecture ✓ Supports OneLake Shortcuts ✓ Works with Lakehouses ✓ Supports Warehouse scenarios ✓ Spark and SQL analytics ✓ Deep Power BI integration ✓ Supports Direct Lake ✓ Simplifies data sharing ✓ Improves collaboration ✓ Centralized governance capabilities
19. Important OneLake Terms
| Term | Meaning |
|---|---|
| OneLake | Unified logical data lake for Fabric |
| Lakehouse | Fabric item combining lake and warehouse-style analytics |
| Delta Lake | Table format/transaction layer commonly used for Lakehouse tables |
| Parquet | Columnar file format |
| Shortcut | Reference to data without traditional copying |
| Workspace | Collaboration/container boundary for Fabric items |
| Direct Lake | Power BI storage mode designed for Fabric/OneLake data |
| Tables | Structured analytical tables |
| Files | File-based data area in a Lakehouse |
Interview Question – What is OneLake?
OneLake is Microsoft Fabric's unified, organization-wide logical data lake. It provides a common storage foundation for Fabric workloads and is automatically available with Fabric. It supports open data formats such as Delta Lake and Parquet and provides features such as OneLake Shortcuts that allow data to be referenced without unnecessary copying. OneLake enables workloads such as Data Engineering, Data Warehouse, Data Science, and Power BI to work efficiently with shared organizational data.
Easy Way to Remember
ONELAKE
"OneDrive for Data"
│
┌────────────┼────────────┐
│ │ │
▼ ▼ ▼
Lakehouse Warehouse Shortcuts
│ │ │
Spark SQL External Data
│ │ │
└────────────┼────────────┘
│
▼
Direct Lake
│
▼
Power BI
Key Takeaway
OneLake is the storage foundation of Microsoft Fabric.
Lessons 1.5–1.12 – Remaining Module Content
The same formatting style is used throughout the remaining lessons: clear headings, tables, bullet points, interview answers, and compact architecture diagrams.
Lesson 1.5 – SaaS vs PaaS
SaaS = Use software over the internet.
PaaS = Build and deploy applications without managing infrastructure.
| Feature | SaaS | PaaS |
|---|---|---|
| Full Form | Software as a Service | Platform as a Service |
| Target Users | End users | Application developers |
| Main Purpose | Use software | Build and deploy software |
| Infrastructure Management | Provider | Provider |
| Application Management | Provider | Customer |
| Coding Required | No | Yes |
| Examples | Microsoft 365, Salesforce | Azure App Service, Heroku |
Easy Memory
SaaS │ ▼ Use software over the internet
PaaS │ ▼ Build and deploy applications without managing infrastructure
Lesson 1.6 – Capacity and Licensing
Capacity = Compute Power
License = User Rights
| Fabric SKU | Capacity Units |
|---|---|
| F2 | 2 CU |
| F4 | 4 CU |
| F8 | 8 CU |
| F16 | 16 CU |
| F32 | 32 CU |
| F64 | 64 CU |
| F128 | 128 CU |
| F256 | 256 CU |
| F512 | 512 CU |
Fabric Capacity │ ▼ F64 │ ▼ 64 CU │ ├── Workspace A ├── Workspace B └── Workspace C
Capacity vs License
| Capacity | License |
|---|---|
| Organization level | User level |
| Provides compute | Provides user rights |
| F2, F4, F8, F64, etc. | Free, Pro, PPU |
| Shared resource | Assigned to individual users |
Lesson 1.7 – Workspaces
Workspace = A shared project area where a team builds and manages Fabric analytics solutions.
Microsoft Fabric │ ▼ Fabric Capacity │ ▼ Workspace │ ├── Lakehouse ├── Warehouse ├── Pipeline ├── Notebook ├── Dataflow ├── Semantic Model └── Power BI Report
| Role | General Purpose |
|---|---|
| Admin | Workspace administration and access management |
| Member | Broad collaboration and content management |
| Contributor | Content development |
| Viewer | Content consumption |
Admin │ └── Manages Member │ └── Collaborates Contributor │ └── Develops Viewer │ └── Consumes
Lesson 1.8 – Roles and Permissions
User │ ▼ Microsoft Entra ID │ ▼ Workspace Role │ ▼ Permissions │ ├── View ├── Create ├── Edit ├── Delete ├── Share └── Manage
Security Flow
User │ ▼ Microsoft Entra ID │ Authentication │ ▼ Microsoft Fabric │ ▼ Workspace Role │ │ Authorization ▼ Fabric Item Permission │ ▼ Data Security │ ├── RLS ├── OLS └── Other Workload-Specific Controls │ ▼ Data / Report
| Security | Controls |
|---|---|
| RLS | Rows |
| OLS | Tables / Columns / Model Objects |
Least Privilege – give users only the access they actually require.
Lesson 1.9 – End-to-End Analytics
Data Sources │ ▼ Ingestion │ ▼ Storage │ ▼ Transformation │ ▼ Data Modeling │ ▼ Analytics │ ▼ Visualization │ ▼ Business Decisions
SOURCE SYSTEMS │ ▼ FABRIC DATA FACTORY │ ▼ ONELAKE │ ▼ LAKEHOUSE │ ▼ DATA ENGINEERING │ ▼ WAREHOUSE │ ▼ SEMANTIC MODEL │ ▼ POWER BI │ ▼ BUSINESS INSIGHTS
End-to-end analytics is the complete process of collecting data from source systems, ingesting and storing it, transforming and modeling it, performing analytics, and delivering business insights through reports and dashboards.
Lesson 1.10 – Create Microsoft Fabric Trial
To start learning Fabric, sign in to the Microsoft Fabric portal with an eligible Microsoft account and start the Fabric trial if the option is available.
Open Browser │ ▼ Open Microsoft Fabric │ ▼ Sign In │ ▼ Start Fabric Trial │ ▼ Create Workspace │ ▼ Create Lakehouse │ ▼ Explore Fabric
Trial availability, duration, and capabilities can change over time and may be controlled by organizational policies.
Lesson 1.11 – Create a Workspace
Open Microsoft Fabric │ ▼ Workspaces │ ▼ + New Workspace │ ▼ Enter Workspace Name │ ▼ Configure Settings │ ▼ Create
Example:
Workspace Name: FabricDemo Description: Workspace for learning Microsoft Fabric
After creation, the workspace can contain Lakehouses, Warehouses, Pipelines, Notebooks, Dataflows, Semantic Models, Reports, and other supported Fabric items.
Lesson 1.12 – Explore Microsoft Fabric UI
The Microsoft Fabric UI is the web-based portal used to create, manage, monitor, and analyze data across Fabric workloads.
Fabric Home │ ├── Home ├── Create ├── Workspaces ├── OneLake ├── Data Hub ├── Monitor ├── Power BI └── Settings
Monitor
Monitor │ ├── Pipelines ├── Notebooks ├── Refreshes ├── Jobs └── Capacity
Lakehouse
Lakehouse │ ├── Tables ├── Files ├── Shortcuts └── SQL Analytics
Data Pipeline
Source │ ▼ Copy Activity │ ▼ Transformation │ ▼ Destination
Module 1 – Quick Revision
Microsoft Fabric
Microsoft Fabric │ ▼ Unified SaaS Analytics Platform
OneLake
OneLake │ ▼ Unified Data Foundation
Capacity
Capacity │ ▼ Compute Power
Workspace
Workspace │ ▼ Organization + Collaboration
Roles
Admin │ ▼ Member │ ▼ Contributor │ ▼ Viewer
Easy memory:
Admin → Manage Member → Collaborate Contributor → Develop Viewer → Consume
Security
Microsoft Entra ID │ ▼ Authentication │ ▼ Workspace Roles │ ▼ Item Permissions │ ▼ Data Security │ ├── RLS → Rows └── OLS → Objects
Fabric End-to-End Flow
Data Sources │ ▼ Data Factory │ ▼ OneLake │ ▼ Lakehouse / Warehouse │ ▼ Data Engineering / SQL / Data Science │ ▼ Semantic Model │ ▼ Power BI │ ▼ Business Insights
Module 1 – Final Interview Definition
Microsoft Fabric is Microsoft's unified SaaS analytics platform that brings together data integration, data engineering, data warehousing, data science, real-time intelligence, and Power BI. OneLake provides the common data foundation, while workspaces organize Fabric items, capacities provide compute resources, and security controls access to the platform and data.
Module 1 – Key Takeaway
MICROSOFT FABRIC
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
DATA FACTORY DATA ENGINEERING DATA WAREHOUSE
│ │ │
└──────────────────┼──────────────────┘
│
▼
ONELAKE
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
DATA SCIENCE REAL-TIME INTEL. POWER BI
│ │ │
└──────────────────┼──────────────────┘
│
▼
BUSINESS INSIGHTS
Remember: OneLake is the data foundation, Capacity provides compute, Workspaces organize the solution, Roles control access, Fabric workloads process the data, Semantic Models provide business meaning, and Power BI delivers insights to users.