Microsoft Fabric – Complete Learning Roadmap (Senior Data Engineer)

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Microsoft Fabric – Complete Learning Roadmap (Senior Data Engineer)

Goal

Master Microsoft Fabric from an enterprise Data Engineer perspective by understanding not only how to use each component, but also why it exists, where it fits in a real-world architecture, how to implement it, how to troubleshoot it, and how to explain it confidently in interviews.

Microsoft Fabric Complete Learning Roadmap

Microsoft Fabric

│
├── Module 1  : Fundamentals
├── Module 2  : Architecture
├── Module 3  : Workspaces
├── Module 4  : OneLake
├── Module 5  : Lakehouse
├── Module 6  : Warehouse
├── Module 7  : SQL Endpoint
├── Module 8  : Data Factory
├── Module 9  : Dataflow Gen2
├── Module 10 : Notebooks
├── Module 11 : Delta Lake
├── Module 12 : Real-Time Intelligence
├── Module 13 : Semantic Models
├── Module 14 : Administration
├── Module 15 : Security & Governance
├── Module 16 : CI/CD & Deployment

This roadmap contains approximately 120–150 learning topics and is intended for an 8+ years Senior Data Engineer preparing for Microsoft Fabric-focused roles.

Module 1 – Microsoft Fabric Fundamentals

Learn

  • What is Microsoft Fabric?

  • Why Microsoft created Fabric

  • Problems with traditional Azure architecture

  • Fabric vision and objectives

  • SaaS architecture

  • End-to-end analytics platform

  • Differences between Fabric and Azure Synapse

  • Differences between Fabric and Azure Databricks

  • Differences between Fabric and Snowflake

  • Fabric Experiences

  • Unified Storage

  • Unified Security

  • Unified Governance

  • Unified Licensing

  • Capacity Units (CU)

  • F SKUs vs P SKUs

  • Tenant

  • Domain

  • Workspace

  • Items

  • Artifacts

  • Fabric Capacity

  • Trial Capacity

  • Reserved Capacity

  • Pay-As-You-Go

  • Regional Availability

  • Licensing Models

  • Workspace Licensing

  • Capacity Assignment

  • Copilot Integration

  • Microsoft 365 Integration

Interview Preparation

Be able to answer:

  • Why Microsoft Fabric?

  • Why not Azure Data Factory + Synapse?

  • What business problems does Fabric solve?

  • Why is OneLake important?

  • Advantages and limitations of Fabric

Module 2 – Microsoft Fabric Architecture

Learn

  • End-to-end Fabric Architecture

  • Control Plane

  • Data Plane

  • Storage Layer

  • Compute Layer

  • Metadata Layer

  • Workspace Architecture

  • Item Relationships

  • Compute Engines

  • Spark Engine

  • SQL Engine

  • Power BI Engine

  • Event Processing Engine

  • Data Movement

  • Metadata Flow

  • Lineage

  • Dependency Tracking

Understand the architecture from Source Systems to Reports.

Module 3 – Workspaces

Learn

  • Workspace Types

  • Workspace Roles

  • Admin

  • Member

  • Contributor

  • Viewer

  • Workspace Settings

  • Capacity Assignment

  • Workspace Domains

  • Workspace Git Integration

  • Deployment Pipelines

  • Workspace Monitoring

  • Workspace Security

  • Workspace APIs

  • Backup & Recovery

  • Naming Standards

  • Best Practices

  • Cross Workspace Sharing

Hands-on

  • Create multiple workspaces

  • Assign capacities

  • Configure permissions

  • Explore workspace settings

Module 4 – OneLake

Learn

  • What is OneLake?

  • OneLake Architecture

  • Physical vs Logical Storage

  • Files

  • Tables

  • Folder Structure

  • Delta Tables

  • Parquet

  • CSV

  • JSON

  • OneLake Explorer

  • OneLake APIs

  • Shortcuts

  • Mirroring

  • Data Sharing

  • Cross Workspace Access

  • External ADLS Integration

  • External S3 Integration

  • OneCopy

  • Data Virtualization

  • Data Duplication

  • Access Control

  • Security

  • Performance

  • Metadata

  • Folder Structure Best Practices

  • Disaster Recovery

  • Cost Optimization

Hands-on

  • Upload CSV files

  • Create Delta Tables

  • Configure OneLake Shortcuts

  • Connect external ADLS

  • Explore folder structure

Interview Preparation

  • ADLS vs OneLake

  • OneLake vs Data Lake

  • OneCopy concept

  • Shortcuts vs Copying Data

Module 5 – Lakehouse

Learn

  • Lakehouse Architecture

  • Medallion Architecture

  • Bronze Layer

  • Silver Layer

  • Gold Layer

  • Managed Tables

  • External Tables

  • Views

  • Spark SQL

  • Delta Tables

  • SQL Endpoint

  • Notebook Integration

  • Partitioning

  • OPTIMIZE

  • VACUUM

  • Time Travel

  • MERGE

  • UPDATE

  • DELETE

  • INSERT OVERWRITE

  • Schema Evolution

  • Schema Enforcement

  • Constraints

  • Identity Columns

  • Primary Keys

  • Foreign Keys (Logical)

  • Data Skipping

  • Statistics

  • Small File Problem

  • Compaction

  • Z-Ordering (where supported)

  • Checkpoints

  • Transaction Logs

  • Streaming Tables

  • Maintenance

  • Performance Optimization

Hands-on

Build a complete Bronze → Silver → Gold architecture.

Module 6 – Warehouse

Learn

  • Warehouse Architecture

  • SQL Endpoint

  • Storage

  • Compute

  • Distribution

  • Statistics

  • Views

  • Materialized Views

  • Stored Procedures

  • Functions

  • Transactions

  • Query Optimization

  • Execution Plans

  • Monitoring

  • Security

  • Role Management

  • Permissions

  • Best Practices

Module 7 – SQL Endpoint

Learn

  • What is SQL Endpoint?

  • Lakehouse SQL Endpoint

  • Warehouse SQL Endpoint

  • Metadata

  • Read-only behavior

  • Performance

  • Security

  • Connectivity

  • Power BI Integration

  • External Connections

  • Limitations

  • Best Practices

Module 8 – Fabric Data Factory

Learn

  • Pipelines

  • Activities

  • Copy Activity

  • Lookup

  • ForEach

  • Until

  • If

  • Switch

  • Wait

  • Stored Procedure Activity

  • Notebook Activity

  • Dataflow Activity

  • Execute Pipeline

  • Variables

  • Parameters

  • Expressions

  • Dynamic Content

  • Triggers

  • Schedule Trigger

  • Event Trigger

  • Pipeline Runs

  • Retry

  • Timeout

  • Concurrency

  • Logging

  • Monitoring

  • Alerts

  • Metadata-Driven Pipelines

  • Reusable Pipelines

  • Incremental Loading

  • CDC

  • Watermark Pattern

  • Error Handling

  • Pipeline Recovery

  • Performance Optimization

  • Cost Optimization

Hands-on

Develop a reusable metadata-driven ETL framework.

Module 9 – Dataflow Gen2

Learn

  • Power Query

  • M Language

  • Merge

  • Append

  • Join

  • Split

  • Conditional Columns

  • Functions

  • Parameters

  • Custom Functions

  • Data Profiling

  • Data Cleansing

  • Destinations

  • Scheduling

  • Monitoring

  • Incremental Refresh

  • Data Quality

  • Best Practices

Module 10 – Notebooks

Learn

  • Spark Architecture

  • Sessions

  • Clusters

  • Python

  • PySpark

  • Spark SQL

  • Scala (Overview)

  • Markdown

  • Magic Commands

  • Widgets

  • Parameters

  • Secrets

  • Libraries

  • Packages

  • Caching

  • Broadcast Joins

  • Adaptive Query Execution

  • Notebook Scheduling

  • Notebook Chaining

  • Utilities

  • Visualization

  • Debugging

  • Performance Optimization

Module 11 – Delta Lake

Learn

  • Delta Format

  • Transaction Log

  • Commit

  • Checkpoint

  • Time Travel

  • MERGE

  • UPDATE

  • DELETE

  • Schema Evolution

  • Schema Enforcement

  • OPTIMIZE

  • VACUUM

  • Compaction

  • Partitioning

  • Statistics

  • Concurrency

  • ACID Transactions

  • Snapshot Isolation

  • Streaming

  • Change Data Feed (CDF)

  • Performance Tuning

  • Troubleshooting

Module 12 – Real-Time Intelligence

Learn

  • Event Streams

  • Eventhouse

  • KQL Database

  • Kusto Query Language (KQL)

  • Streaming

  • Real-Time Dashboards

  • Event Processing

  • Windowing

  • Aggregations

  • Alerts

  • Data Activator

  • IoT Integration

  • Azure Event Hub Integration

  • Real-Time Monitoring

Module 13 – Semantic Models

Learn

  • Semantic Models

  • Relationships

  • Measures

  • Hierarchies

  • Perspectives

  • Aggregations

  • Composite Models

  • Incremental Refresh

  • Row-Level Security (RLS)

  • Object-Level Security (OLS)

  • Calculation Groups

  • Performance Optimization

Module 14 – Administration

Learn

  • Tenant Settings

  • Capacity Management

  • Monitoring Hub

  • Usage Metrics

  • Audit Logs

  • Deployment Pipelines

  • Git Integration

  • Workspace Management

  • Backup

  • Recovery

  • Cost Optimization

  • Resource Monitoring

Module 15 – Security & Governance

Learn

  • Microsoft Entra ID Authentication

  • RBAC

  • Workspace Roles

  • Row-Level Security

  • Object-Level Security

  • Sensitivity Labels

  • Microsoft Purview

  • Data Lineage

  • Data Classification

  • Encryption

  • Private Link

  • Managed Identity

  • Service Principals

  • Secrets Management

  • Compliance

  • Governance Policies

Module 16 – CI/CD & Deployment

Learn

  • Git Integration

  • Azure DevOps

  • GitHub

  • Deployment Pipelines

  • Environment Variables

  • Parameters

  • Version Control

  • Branch Strategy

  • Merge Strategy

  • Dev → Test → UAT → Production Promotion

  • Rollback Strategy

  • Release Validation

  • Deployment Automation

Recommended Learning Approach

For each module, cover the following:

  1. Theory and Concepts

  2. Internal Architecture

  3. Hands-on Implementation

  4. Enterprise Use Cases

  5. Real-Time Project Scenarios

  6. Best Practices

  7. Performance Optimization

  8. Security Considerations

  9. Monitoring and Troubleshooting

  10. Interview Questions and Answers

  11. Common Production Issues

  12. Comparisons with Azure, Databricks, Synapse, and Snowflake

  13. End-to-End Project Implementation

By completing every module in this roadmap, you will gain a comprehensive understanding of Microsoft Fabric suitable for Senior Data Engineer interviews and enterprise implementations.

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