15-Day Microsoft Fabric for Data Engineering Master Guide

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For 4–10 Years Experienced Data Engineers

Objective

This guide is designed to:

  • Build strong Microsoft Fabric fundamentals

  • Understand unified analytics architecture

  • Learn OneLake and Lakehouse concepts

  • Build enterprise-level data engineering understanding

  • Prepare for modern cloud analytics interviews

  • Understand Fabric ecosystem integration

Target Audience:

  • Data Engineers

  • Azure Data Engineers

  • Fabric Engineers

  • BI Engineers

  • Analytics Engineers

  • Cloud Data Platform Engineers

Daily Time Commitment:

  • 3 Hours Per Day

  • 15 Days Total

Learning Strategy:

  • 20% Theory

  • 80% Hands-On Practice

Goal:

  • Understand Microsoft Fabric architecture deeply

  • Build Lakehouse-based pipelines

  • Integrate engineering + analytics workflows

  • Handle enterprise data platforms

  • Build scalable modern analytics solutions

Daily Learning Structure

Hour 1 – Learn Concepts

Focus on:

  • Understanding WHY Fabric exists

  • Unified analytics understanding

  • Enterprise architecture patterns

  • Lakehouse implementation concepts

Avoid:

  • Memorizing UI clicks blindly

  • Watching endless tutorials

Hour 2 – Hands-On Development

Focus on:

  • Creating workspaces

  • Building pipelines

  • Creating Lakehouses

  • Writing notebooks

  • Building reports

Hour 3 – Real-Time Scenarios

Focus on:

  • Enterprise architecture

  • Optimization

  • Incremental pipelines

  • Security

  • Monitoring

  • End-to-end analytics flow

SECTION 1 – MICROSOFT FABRIC BASICS

Topics:

  • What is Microsoft Fabric

  • Why Fabric

  • Unified Analytics Platform

  • SaaS-based analytics

  • Fabric Components

WHAT IS MICROSOFT FABRIC

Microsoft Fabric is a unified cloud analytics platform integrating:

  • Data Engineering

  • Data Science

  • Data Warehousing

  • Real-Time Analytics

  • Power BI

  • Data Integration

All within one ecosystem.

WHY FABRIC IS USED

Problems Fabric Solves:

  • Multiple disconnected tools

  • Complex integrations

  • Separate storage systems

  • Fragmented analytics platforms

  • Data duplication

UNIFIED ANALYTICS PLATFORM

Critical Interview Topic.

Fabric combines:

  • ADF-like pipelines

  • Databricks-like notebooks

  • Power BI integration

  • Lakehouse architecture

  • Real-time analytics

SECTION 2 – FABRIC ARCHITECTURE

Topics:

  • OneLake

  • Lakehouse

  • Warehouse

  • Data Factory

  • Power BI

  • Real-Time Analytics

ONELAKE

Most Important Fabric Topic.

Purpose:
Unified storage layer.

Benefits:

  • Single source of truth

  • No data duplication

  • Shared storage across services

LAKEHOUSE

Critical Topic.

Purpose:
Combine:

  • Data lake

  • Data warehouse

Supports:

  • Structured data

  • Unstructured data

  • Spark processing

  • SQL analytics

WAREHOUSE

Purpose:
SQL analytics engine.

Use Cases:

  • BI reporting

  • Enterprise SQL workloads

  • Analytics dashboards

SECTION 3 – FABRIC WORKSPACES

Topics:

  • Workspaces

  • Capacity

  • Items

  • Collaboration

WORKSPACES

Purpose:
Organize Fabric resources.

Contains:

  • Pipelines

  • Notebooks

  • Reports

  • Lakehouses

  • Warehouses

CAPACITY

Purpose:
Compute allocation.

Understand:

  • F SKUs

  • Performance scaling

  • Capacity management

SECTION 4 – DATA ENGINEERING IN FABRIC

Topics:

  • Notebooks

  • Spark jobs

  • DataFrames

  • ETL pipelines

NOTEBOOKS

Supports:

  • PySpark

  • SQL

  • Python

Purpose:
Data transformations.

SPARK IN FABRIC

Understand:

  • Distributed processing

  • Spark runtime

  • Notebook execution

DATAFRAMES

Practice:

  • Read CSV

  • Read JSON

  • Read parquet

  • Transform data

  • Write delta tables

SECTION 5 – DATA FACTORY IN FABRIC

Topics:

  • Pipelines

  • Copy activity

  • Dataflows

  • Orchestration

  • Scheduling

FABRIC DATA FACTORY

ADF-like orchestration inside Fabric.

Use Cases:

  • ETL pipelines

  • Incremental processing

  • Workflow orchestration

PIPELINES

Practice:

  • Copy data

  • Trigger notebooks

  • Schedule workflows

DATAFLOWS

Purpose:
Low-code transformations.

SECTION 6 – DELTA LAKE IN FABRIC

Topics:

  • Delta tables

  • ACID transactions

  • Merge

  • Time travel

  • Optimization

DELTA TABLES

Critical Topic.

Benefits:

  • Reliable storage

  • Incremental processing

  • Historical tracking

MERGE OPERATIONS

Use Cases:

  • CDC

  • Upserts

  • SCD Type 2

TIME TRAVEL

Purpose:
Historical data access.

SECTION 7 – DATA WAREHOUSE IN FABRIC

Topics:

  • SQL warehouse

  • Warehousing concepts

  • T-SQL support

  • Analytics

FABRIC WAREHOUSE

Purpose:
Enterprise analytics.

Use Cases:

  • BI reporting

  • Aggregations

  • SQL analytics

T-SQL SUPPORT

Practice:

  • Window functions

  • Aggregations

  • CTEs

  • Joins

SECTION 8 – REAL-TIME ANALYTICS

Topics:

  • Event streams

  • Streaming analytics

  • Real-time dashboards

EVENT STREAMS

Purpose:
Real-time ingestion.

Use Cases:

  • IoT data

  • Logs

  • Streaming analytics

REAL-TIME DASHBOARDS

Purpose:
Streaming visualization.

SECTION 9 – POWER BI IN FABRIC

Topics:

  • Reports

  • Dashboards

  • Semantic models

  • DAX

  • DirectLake

DIRECTLAKE MODE

Critical Fabric Topic.

Purpose:
Direct reporting on OneLake.

Benefits:

  • Faster analytics

  • Reduced duplication

  • Better performance

SEMANTIC MODELS

Purpose:
Business reporting layer.

SECTION 10 – SECURITY AND GOVERNANCE

Topics:

  • RBAC

  • Workspaces

  • Row-level security

  • Sensitivity labels

  • Governance

GOVERNANCE

Critical Enterprise Topic.

Topics:

  • Data lineage

  • Access management

  • Compliance

SECTION 11 – PERFORMANCE OPTIMIZATION

Topics:

  • Partitioning

  • Caching

  • Delta optimization

  • Query optimization

  • Capacity management

DELTA OPTIMIZATION

Practice:

  • Optimize

  • Vacuum

  • Z-ordering

CAPACITY OPTIMIZATION

Understand:

  • Resource utilization

  • Scaling

  • Cost optimization

SECTION 12 – INCREMENTAL PROCESSING

Topics:

  • CDC

  • Watermarking

  • Incremental refresh

  • Merge logic

CDC PIPELINES

Purpose:
Process changed data only.

WATERMARKING

Purpose:
Track incremental loads.

SECTION 13 – REAL-TIME ENTERPRISE ARCHITECTURE

Typical Flow:

Source Systems
↓
Fabric Pipelines
↓
OneLake Bronze
↓
Lakehouse Processing
↓
Silver Layer
↓
Gold Layer
↓
Warehouse
↓
Power BI Dashboards

MEDALLION ARCHITECTURE

Layers:

  • Bronze

  • Silver

  • Gold

BRONZE LAYER

Purpose:
Raw ingestion.

SILVER LAYER

Purpose:
Validated and transformed data.

GOLD LAYER

Purpose:
Business-ready analytics.

SECTION 14 – REAL-TIME PROJECT STRUCTURE

Typical Fabric Project Structure:

project/
│
├── pipelines/
│ └── ingestion_pipeline
│
├── notebooks/
│ ├── bronze_processing
│ ├── silver_processing
│ └── gold_aggregation
│
├── lakehouse/
│ ├── bronze_tables
│ ├── silver_tables
│ └── gold_tables
│
├── warehouse/
│ └── reporting_tables
│
├── reports/
│ └── executive_dashboard.pbix
│
├── config/
│ └── config.json
│
└── documentation/
└── architecture.docx

SECTION 15 – MID-LEVEL PROJECTS

PROJECT 1 – SALES LAKEHOUSE PIPELINE

Requirements:

  • Ingest CSV sales data

  • Process bronze/silver/gold layers

  • Generate Power BI reports

Concepts Used:

  • Pipelines

  • Lakehouse

  • Delta tables

PROJECT 2 – CUSTOMER ANALYTICS PLATFORM

Requirements:

  • Process customer transactions

  • Generate KPIs

  • Build dashboards

Concepts Used:

  • Warehouse

  • Semantic models

  • DAX

PROJECT 3 – CDC INCREMENTAL PIPELINE

Requirements:

  • Process incremental changes

  • Maintain history

  • Implement merge logic

Concepts Used:

  • Delta merge

  • Watermarking

  • Incremental pipelines

PROJECT 4 – REAL-TIME EVENT STREAMING

Requirements:

  • Stream events

  • Process real-time analytics

  • Generate dashboards

Concepts Used:

  • Event streams

  • Real-time analytics

  • Power BI

PROJECT 5 – ENTERPRISE REPORTING PLATFORM

Requirements:

  • Build warehouse

  • Create dashboards

  • Implement security

  • Optimize performance

Concepts Used:

  • Warehouse

  • Power BI

  • Governance

SECTION 16 – MICROSOFT FABRIC INTERVIEW QUESTIONS

BASIC QUESTIONS

  1. What is Microsoft Fabric?

  2. What is OneLake?

  3. What is Lakehouse architecture?

  4. Difference between Lakehouse and Warehouse.

  5. What is DirectLake?

  6. What are Fabric workspaces?

  7. What is Fabric Data Factory?

  8. What are notebooks in Fabric?

  9. What are Delta tables?

  10. What is medallion architecture?

INTERMEDIATE QUESTIONS

  1. Explain Fabric architecture.

  2. Explain OneLake benefits.

  3. Explain DirectLake mode.

  4. Explain Delta merge.

  5. Explain CDC pipelines.

  6. Explain Fabric pipelines.

  7. Explain Lakehouse implementation.

  8. Explain Fabric security.

  9. Explain incremental processing.

  10. Explain real-time analytics.

ADVANCED QUESTIONS

  1. Design enterprise Fabric architecture.

  2. Build scalable analytics platform.

  3. Optimize Fabric workloads.

  4. Implement CDC frameworks.

  5. Design medallion architecture.

  6. Explain governance implementation.

  7. Build real-time reporting architecture.

  8. Handle billions of records.

  9. Optimize Power BI with DirectLake.

  10. Explain enterprise troubleshooting strategy.

SECTION 17 – 15-DAY EXECUTION PLAN

WEEK 1 – FOUNDATION

Day 1

  • Fabric basics

  • Unified analytics

  • Architecture overview

Day 2

  • OneLake

  • Lakehouse

  • Warehouse

Day 3

  • Workspaces

  • Capacity

  • Collaboration

Day 4

  • Notebooks

  • Spark processing

  • DataFrames

Day 5

  • Fabric pipelines

  • Data Factory

  • Scheduling

Day 6

  • Delta tables

  • Merge

  • Optimization

Day 7

  • Mini Lakehouse project

WEEK 2 – ADVANCED FABRIC

Day 8

  • Warehouse

  • SQL analytics

  • Reporting

Day 9

  • Power BI integration

  • DirectLake

  • Semantic models

Day 10

  • Streaming analytics

  • Event streams

Day 11

  • Security

  • Governance

  • RLS

Day 12

  • Incremental processing

  • CDC

  • Watermarking

Day 13

  • Performance optimization

  • Capacity tuning

Day 14

  • Mid-level projects

Day 15
FINAL MOCK INTERVIEW + REVISION

REAL-TIME BEST PRACTICES

Always Follow:

  • Use medallion architecture

  • Use Delta tables

  • Implement incremental pipelines

  • Optimize storage layouts

  • Use proper partitioning

  • Monitor capacities

  • Implement governance

  • Use DirectLake where possible

  • Avoid unnecessary duplication

  • Use modular pipelines

MOST IMPORTANT SKILLS FOR SENIOR ENGINEERS

You must become strong in:

  • Lakehouse architecture

  • OneLake understanding

  • Delta Lake optimization

  • Enterprise analytics architecture

  • Incremental processing

  • Real-time reporting

  • Governance and security

  • Unified analytics design

  • Scalability thinking

  • Performance optimization

FINAL INTERVIEW EXPECTATIONS

At 4–10 years experience, interviewers expect:

  • Strong Fabric architecture understanding

  • Lakehouse implementation knowledge

  • OneLake understanding

  • Real-time analytics capability

  • Delta optimization knowledge

  • Power BI integration understanding

  • Enterprise architecture mindset

  • Governance implementation understanding

  • End-to-end analytics platform knowledge

They do NOT expect only UI knowledge.

They expect:

  • Enterprise engineering mindset

  • Unified analytics understanding

  • Scalability thinking

  • Performance optimization mindset

  • Modern cloud analytics architecture capability

END OF DOCUMENT

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