#Fabric Data Engineer

Fabric Data Engineer Data Engineer is the latest trending job role that deals with End to End Data Warehouse design (DWH) using ETL (Extract, Transform, Load) techniques. This prominent job role also involves Big Data Analytics and Business Intelligence implementation using Spark, PySpark, Cloud Computing, TSQL and more.

✅ Cloud ETL, DWH with Big Data Analytics
✅ OneLake & Lakehouse for Unified Storage
✅ Fabric Data Factory for ETL
✅ Dataflows Gen2, Self-Service Data Prep
✅ Delta Lake, Delta Tables with Big Data
✅ ETL Notebooks with PySpark, TSQL
✅ Realtme IoT with Eventstreams
✅ CI/CD with Fabric Git Integrations
✅ End-to-End Capstone Project on Banking
✅ 1:1 Mentorship, Interview Guidance

Module 1: SQL Concepts & Queries

Ch 1: Data Engineer Job Roles

Introduction to Data
Data Engineer Job Roles
Data Engineer Challenges
Data and Databases Intro

Ch 2: Database Intro & Installations

Database Types (OLTP, DWH, ..)
DBMS: Basics
SQL Server 2025 Installations
SSMS Tool Installation
Server Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

Creating Databases (GUI)
Creating Tables, Columns (GUI)
SQL Basics (DDL, DML, etc..)
Creating Databases, Tables
Data Inserts (GUI, SQL)
Basic SELECT Queries

Ch 4: SQL Basics V2 (Commands, Operators)

DDL: Create, Alter, Drop, Add, modify, etc..
DML: Insert, Update, Delete, select into, etc..
DQL: Fetch, Insert… Select, etc..
SQL Operations: LIKE, BETWEEN, IN, etc..
Special Operators

Ch 5: Data Types

Integer Data Types
Character, MAX Data Types
Decimal & Money Data Types
Boolean & Binary Data Types
Date and Time Data Types
SQL_Variant Type, Variables

Ch 6: Excel Data Imports

Data Imports with Excel
SQL Native Client
Order By: Asc, Desc
Order By with WHERE
TOP & OFFSET
UNION, UNION ALL

Ch 7: Schemas & Batches

Schemas: Creation, Usage
Schemas & Table Grouping
Real-world Banking Database
2 Part, 3 Part & 4 Part Naming
Batch Concept & “Go” Command

Ch 8: Constraints, Keys & RDBMS – Level 1

Null, Not Null Constraints
Unique Key Constraint
Primary Key Constraint
Foreign Key & References
Default Constraint & Usage
DB Diagrams & ER Models

Ch 9: Normal Forms & RDBMS – Level 2

Normal Forms: 1 NF, 2 NF
3 NF, BCNF and 4 NF
Adding Keys to Tables
Cascading Keys
Self Referencing Keys
Database Diagrams

Ch 10: Joins & Queries

Joins: Table Comparisons
Inner Joins & Matching Data
Outer Joins: LEFT, RIGHT
Full Outer Joins & Aliases
Cross Join & Table Combination
Joining more than 2 tables

Ch 11: Views & RLS

Views: Realtime Usage
Storing SELECT in Views
DML, SELECT with Views
RLS: Row Level Security
WITH CHECK OPTION
Important System Views

Ch 12: Stored Procedures

Stored Procedures: Realtime Use
Parameters Concept with SPs
Procedures with SELECT
System Stored Procedures
Metadata Access with SPs
SP Recompilations

Ch 13: User Defined Functions

Using Functions in MSSQL
Scalar Functions in Real-world
Inline & Multiline Functions
Parameterized Queries
Date & Time Functions
String Functions & Queries
Aggregated Functions & Usage

Ch 14: Triggers & Automations

Need for Triggers in Real-world
DDL & DML Triggers
For / After Triggers
Instead Of Triggers
Memory Tables with Triggers
Disabling DMLs & Triggers

Ch 15: Transactions & ACID

Transaction Concepts in OLTP
Auto Commit Transaction
Explicit Transactions
COMMIT, ROLLBACK
Checkpoint & Logging
Lock Hints & Query Blocking
READPAST, LOCKHINT

Ch 16: CTEs & Tuning

Common Table Expression
Creating and Using CTEs
CTEs, In-Memory Processing
Using CTEs for DML Operations
Using CTEs for Tuning
CTEs: Duplicate Row Deletion

Ch 17: Indexes Basics, Tuning

Indexes & Tuning
Clustered Index, Primary Key
Non Clustered Index & Unique
Creating Indexes Manually
Composite Keys, Query Optimizer
Composite Indexes & Usage

Ch 18: Group By Queries

Group By, Distinct Keywords
GROUP BY, HAVING
Cube( ) and Rollup( )
Sub Totals & Grand Totals
Grouping( ) & Usage
Group By with UNION
Group By with UNION ALL

Ch 19: Joins with Group By

Joins with Group By
3 Table, 4 Table Joins
Join Queries with Aliases
Join Queries & WHERE, Group By
Joins with Sub Queries
Query Execution Order

Ch 20: Sub Queries

Sub Queries Concept
Sub Queries & Aggregations
Joins with Sub Queries
Sub Queries with Aliases
Sub Queries, Joins, Where
Correlated Queries

Ch 21: Cursors & Fetch

Cursors: Realtime Usage
Local & Global Cursors
Scroll & Forward Only Cursors
Static & Dynamic Cursors
Fetch, Absolute Cursors

Ch 22: Window Functions, CASE

IIF Function and Usage
CASE Statement Usage
Window Functions (Rank)
Row_Number( )
Rank( ), DenseRank( )
Partition By & Order By

Ch 23: Merge(Upsert) & CASE, IIF

Merge Statement
Upsert Operations with Merge
Matched and Not Matched
IIF & CASE Statements
Merge Statement inside SPs
Merge with OLTP & DWH

Ch 24: Key Take-Aways from Module 1

Case Study 1: Medicare Scenario
Case Study 2: Ecommerce Scenario

Module 2: Fabric Data Engineering: Warehouse

Ch 1: Fabric Introduction

Need for Fabric, Big Data
Fabric Data Engineering Model
Fabric Components (Items)
Microsoft Fabric: Advantages
Cloud Warehouse Uses
Benefits of Fabric Over Azure
Azure Versus Fabric DWH

Ch 2: Fabric Account, Workspace

Need for Fabric Workspace
Workspace Creation Process
Pins and New Items
Item Categorization
ETL, Storage, Analytical
Streaming, Monitoring
Compute & Separation

Ch 3: Fabric Architecture

Intelligent Data Foundation
Polaris Distributed Engine
Stateless & Stateful
Cache, Metadata, Xact & Data
Fabric Tasks, Inputs & DAG
State Machine & Statistics
Hot Spot Recovery

Ch 4: Fabric Warehouse

Fabric Warehouse Creation
Fabric Warehouse Features
Fabric Warehouse Properties
Fabric Warehouse Limitations
DWH Internal Operations
Default Schemas & Objects

Ch 5: Fabric Data Types

Realtime use of Fabric Houses
Exact, Approximate Numbers
Date and Time Data Types
Fixed & Variable Length
Binary & String Data Types
Fabric Type Limitations

Ch 6: SSMS Connections

Warehouse SQL Connection
Database Engine Server
Multi Factor Authentication
Warehouse Artifacts
Executing .SQL Scripts
Testing Fabric Artifacts

Ch 7: Fabric Caching

Fabric Caching Process
In-memory Cache, Disk Cache
Cache Types: LRU /MRU
Cold Cache / Cold Run
Realtime use of Caching
Performance Advantages
Warehouse Optimizations

Ch 8: Fabric Statistics

Query Engine Options
Statistics Types
Leverage Statistics
Auto, Manual Statistics
Update Statistics
Statistics Consistency
Statistics Lists & Reports

Ch 9: Time Travel

Continuous Data Protection
Data Storage, Retention
FOR TIMESTAMP AS OF
Time Travel Scenarios
Time Travel Implementation
Time Travel on Queries
Time Travel Limitations

Ch 10: Aggregated Data Store

Options for Data Aggregations
Save As table, Save As View
Single Table Aggregations
Multi Table Aggregations
Dynamic Conditions
Parameterized Aggregations

Ch 11: Zero Copy Cloning

User Layer, Storage Layer
Cloning & Parquet Files
Synapse Data Warehouse
Data History Retention
Point In Time , Schema Level
Zero Copy Cloning Limitations

Ch 12: Fabric Security

Workspace Security
Warehouse Security
Item Security & Roles
Adding AD Users
Item Security Limitations
MFA & Client Security

Module 3: Fabric Data Engineering: Data Factory Pipelines

Ch 13: Fabric Data Factory

ETL Implementation Options
Need for Fabric Data Factory
ETL Operations in FDF
Data Sources, Transformations
Data Destinations (Sinks)
Creating Pipelines

Ch 14: Fabric Pipelines

Activities and Connections
Gateways & OnPrem Access
Data Sets & Activity Sets
Data Activator & Alerts
Run ID & Monitoring
Pipeline Creation, Verification

Ch 15: Fabric Pipelines Design

Creation Options for Pipelines
Azure SQL DB Data Loads
Creating Data Sets
RRR Transformations
Copy Command Usage
Internal Staging (Workspace)

Ch 16: Fabric Aggr Data Loads

Aggregation Scenarios
Creating Views in TSQL
Using Views in FDF Pipelines
Using Pipeline Editor
Data Loads to Warehouse
Pipeline Verifications

Ch 17: ETL Staging

Staging : Advantages
Caching & Storing Concept
Staging Types in Fabric
Workspace & External
External Stages in Pipelines
Compressions & Advantages
Pipeline Trigger, Monitor

Ch 18: OnPrem Gateways

Need for On_Premi Gateway
Installing & Configuring
Authentication, Usage
OnPremises Connections
Pipelines for Data Loads
Warehouse Data Storage
Data Refresh with Gateways

Module 4: Fabric Data Engineering: LakeHouse

Ch 13: Fabric Data Factory

ETL Implementation Options
Need for Fabric Data Factory
ETL Operations in FDF
Data Sources, Transformations
Data Destinations (Sinks)
Creating Pipelines

Ch 14: Fabric Pipelines

Activities and Connections
Gateways & OnPrem Access
Data Sets & Activity Sets
Data Activator & Alerts
Run ID & Monitoring
Pipeline Creation, Verification

Ch 15: Fabric Pipelines Design

Creation Options for Pipelines
Azure SQL DB Data Loads
Creating Data Sets
RRR Transformations
Copy Command Usage
Internal Staging (Workspace)

Ch 16: Fabric Aggr Data Loads

Aggregation Scenarios
Creating Views in TSQL
Using Views in FDF Pipelines
Using Pipeline Editor
Data Loads to Warehouse
Pipeline Verifications

Ch 17: ETL Staging

Staging : Advantages
Caching & Storing Concept
Staging Types in Fabric
Workspace & External
External Stages in Pipelines
Compressions & Advantages
Pipeline Trigger, Monitor

Ch 18: OnPrem Gateways

Need for On_Premi Gateway
Installing & Configuring
Authentication, Usage
OnPremises Connections
Pipelines for Data Loads
Warehouse Data Storage
Data Refresh with Gateways

Module 5: Fabric Data Engineering: Data Flow

Ch 25: Power Query Level 1

Power Query Concept
Need for Power Query
Data Flow Gen 1
Data Flow Gen 2
Power Query Items
Differences with Copy Activity
ETL, ELT Process

Ch 26: Power Query Level 2

Data Flow Gen2 Operations
PQ Online Editor
Working with Binary Content
Detailed Data Options
Data Cleansing Options
Step Names, Aggregations
Warehouse Data Loads

Ch 27: Power Query Level 3

Binding Power Query Steps
Edit / Delete Steps
Optimizing Power Query
ETL & ELT with Power Query
Advanced Editor
M Language Expressions
Duplicate / Reference Queries

Module 6: Fabric Data Engineering: PySpark & KQL

Ch 28: Fabric Notebooks

Need for Notebooks
Fabric Notebook Types
Get / Prep / Analyze
Sessions, Markdown Folding
Standard, High Concurrency
Magic Command
Freeze Cells

Ch 29: Spark SQL Notebooks

Creating Environment
Creating Spark Clusters
Spark Cluster Compute
SQL Analytics in Notebooks
Visual Query Vs SQL
Cell Execution Options
Magic Command Usage

Ch30: PySpark Notebooks

Creating / Using Environment
PySpark Notebook Sessions
Reading Source Data
Data Prep & Aggregations
Data Loads, Analytics
Cell Execution Options
Markdown Cells

Ch 31: StreamHouse, KQL

Need for Stream House
Auto creation of KQL
Manual KQL Databases
Verification & Usage
Differences with Warehouse
Differences with Lakehouse

Ch 32: KQL Query Sets

KQL Database Extraction
File Imports – on Premises
Metadata Edit Options
Query Analytics
Exports, Visualizations
Query Sets Versus Notebooks

Ch 33: Fabric Data Activator

Need for Alerts, Notifications
Fabric Data Activator Options
Alert Conditions, Thresholds
Email Notifications
Events & Notifications
Edit / Enable / Disable

Ch 34: Model Layouts

Need for Layouts
Creating Model Layouts
Adding Refences, Keys
Power BI Semantic Models
Creating Report Items
Using Power BI Desktop

Module 7: Fabric Data Engineering: Additional Take-aways

👉🏻 Azure Synapse Migrations

👉🏻 DP 700 Exam Guidance

👉🏻 End to End Realtime Project: Ecommerce / Insurance Domain

Brochure Download Request a Callback

What is the Fabric Data Engineer course and who can join?

This course is designed for Data Engineers, BI Developers, Cloud Engineers, SQL Developers, and professionals who want to work with Microsoft Fabric, Lakehouse, Warehouses, Data Engineering Pipelines, and AI-powered analytics.

What are the prerequisites for learning Fabric Data Engineering?

Basic SQL knowledge is helpful, but not mandatory. The program includes MSSQL + TSQL fundamentals before moving into Fabric components.

What modules are included in the Fabric Data Engineer course?

Module 1: MSSQL & TSQL (3 Weeks)
Module 2: Fabric Data Engineering (6 Weeks)
Module 3: Power BI with AI (6 Weeks)
DP-700 Exam Guidance is also included

Does this course include real-time projects?

Yes. The program includes a mini SQL project, one Fabric real-time project, and one Power BI project, including end-to-end pipeline implementation in an E-commerce domain.

What is Microsoft Fabric and why is it important?

Microsoft Fabric is an end-to-end analytics platform combining Data Engineering, Data Factory, Data Science, Power BI, Real-time Analytics, and Storage into a single unified service. It provides better performance, cost optimization, and simpler data architecture.